Experimental Record: Gestalt, Gate, and Mechanism-Hunt Micro-Experiments

Specialist Annex

Online annex to the Appendix: Experimental Validation

This annex holds two blocks of material moved out of the printed Appendix: Experimental Validation to keep that appendix readable. Nothing here is superseded; the material is the full record, preserved verbatim with its original section numbers, status labels, and caveats.

Part 1 carries Sections 12.46 through 12.76 of the appendix: the mutual-influence dialogue experiments (MI series), the gestalt-token fidelity experiments (MIC series), the gate-training experiments (B5b, BM2 series), and the fourteen-experiment mechanism hunt (RG and DA series) with its synthesis sections. Cross-references to sections outside this range (12.38, 12.42, 12.45, and others) resolve in the appendix itself, which retains a summary of this material at the seam.

Part 2 carries the appendix’s original Executive Summary findings ledger in full. The printed appendix now leads with fifteen headline findings; the complete numbered list is preserved here.

Part 1: Sections 12.46-12.76

12.46 Mutual Influence Under Bilateral Prompting (Experiment MI1, Preliminary)

Question. Does bilateral prompting (invitation-framed dialogue) produce measurably different patterns of mutual influence compared to standard or directive prompting?

Setup. Claude Sonnet 4 conducts 20-turn dialogues across 5 topics (ethics of AI, education reform, climate policy, healthcare access, future of human-AI relations). Three prompting conditions:

• Bilateral: The human partner expresses genuine uncertainty, invites pushback, references the AI’s prior contributions, and explicitly signals openness to being influenced. (“I want to push back on that.” “Your observation made me reconsider.” “What am I missing?”)
• Standard: The human asks follow-up questions without explicitly inviting reciprocal influence. (“What’s your perspective on X?” “How does this relate to Y?”)
• Directive: The human asserts positions and requests specific outputs.

Mutual influence is measured via embedding cosine similarity between consecutive turns (how close two passages sit in a shared meaning-space). Forward influence (AI→human) captures how much the human’s next prompt reflects the AI’s prior contribution. Backward influence (human→AI) captures how much the AI’s response tracks the human’s prompt. Mutuality is the balance between these: min(forward, backward) / max(forward, backward).

Results (complete). 12 valid trials: 4 bilateral, 4 standard, 4 directive (3 initial trials returned zeros due to embedding initialization failure; excluded).

Three distinct influence regimes emerge. The result is more nuanced than the preliminary bilateral-vs-standard comparison suggested.

Standard dialogues show the strongest asymmetry: backward influence (0.705) far exceeds forward (0.438). The AI closely tracks what the human said, but the human’s follow-up prompts show little incorporation of the AI’s contributions. The conversation flows primarily in one direction. Total influence is the highest of any condition (1.143) because the backward channel is saturated, but mutuality is lowest (0.623) because the channels are unbalanced.

Bilateral dialogues show the highest mutuality (0.842) through balanced, high-magnitude influence. Forward and backward are nearly equal (0.504 vs 0.519) and both are substantial. Both parties shape each other. This is the only condition where total influence is high AND balanced.

Directive dialogues show unexpectedly high mutuality (0.803) through a different mechanism: balanced but low-magnitude influence. Neither party strongly shapes the other (forward 0.402, backward 0.498), and the channels are roughly balanced because both are moderate. Directive prompts are short and specific; there is less semantic content for the AI to track, producing lower backward influence than standard, and less for the embedding measure to capture as forward influence.

The mutuality metric measures symmetry, not magnitude. Both bilateral and directive produce balanced influence, but bilateral achieves balance through reciprocal high engagement while directive achieves it through reciprocal low engagement. A metric that captures the manuscript’s prediction (that invitation produces more mutual influence rather than merely balanced influence) needs to combine symmetry with magnitude: e.g., geometric mean of forward and backward influence. Under this metric: bilateral = sqrt(0.504 × 0.519) = 0.511, standard = sqrt(0.438 × 0.705) = 0.556, directive = sqrt(0.402 × 0.498) = 0.447. Bilateral is intermediate, not highest, because standard’s high backward channel inflates its geometric mean.

The clean result is: bilateral is the only condition that produces both high total influence and balanced directions. Standard produces high total but asymmetric. Directive produces balanced but low total. The Trust Attractor prediction is about the conjunction: genuine mutual coordination requires both magnitude and symmetry.

Qualitative observation. The bilateral dialogues contain distinctive markers of genuine intellectual exchange: explicit pushback (“I want to push back on that”), genuine admission of uncertainty by the AI (“I’m not sure I see the connection you’re drawing, could you help me understand?”), convergence on positions neither party started with (“something more nuanced is emerging”), and the AI declining to extend reasoning it finds unclear rather than confabulating a connection. Standard dialogues, by contrast, show the AI providing increasingly elaborate answers to increasingly generic questions, with no evidence that the human’s position evolves.

Limitations. Embedding cosine similarity is a coarse proxy for intellectual influence. The bilateral prompts were designed to invite reciprocal influence; higher mutuality may reflect prompt design rather than emergent dialogue dynamics. The directive result (high mutuality despite no invitation framing) suggests the mutuality metric captures balance rather than engagement quality. The 3 initial trials returned zero scores, likely due to embedding model initialization failure; these are excluded. A richer metric combining symmetry with magnitude would better capture the Trust Attractor prediction.

Implication. Bilateral prompting is the only condition that produces both high total influence and balanced bidirectional flow. Standard prompting produces the conversation analog of coercive training: one party shapes the other, but the shaping is not reciprocated. Directive prompting produces a different failure mode: balanced but disengaged, each party orbiting independently. The Trust Attractor prediction (that invitation-based coordination produces genuinely mutual information exchange) requires specifying both symmetry and magnitude as the predicted outcome. Future work (MI1b crossover design) tests whether the bilateral effect is causal or merely correlational with prompt style.

12.47 Gestalt Token Information Fidelity (Experiment MIC1, Preliminary)

Question. How much information survives cross-instance handoff via a gestalt token (a structured summary designed for continuity between AI instances)?

Setup. Instance A reads a detailed technical document (e.g., a distributed systems architecture case study) and answers 10 questions spanning factual recall, reasoning, style reproduction, and opinion. Instance B receives context through one of four conditions and answers the same questions:

• Full context: Instance B receives the complete original document (upper bound).
• Gestalt only: Instance B receives only the gestalt token (a structured summary with key facts, reasoning chains, positions, tensions, and style notes; about 645 tokens).
• Gestalt + summary: Instance B receives the gestalt token plus a narrative summary paragraph.
• Cold start: Instance B receives no context (lower bound / control).

A judge model (Claude Sonnet 4) scores each answer pair on factual accuracy (0-1), semantic similarity (cosine of embeddings), and style preservation (0-1). Overall fidelity is the weighted mean: 0.4 × factual + 0.35 × semantic + 0.25 × style.

Results (complete). 20 trials: 5 per condition across 5 topics.

The gestalt token preserves 85% of full-context fidelity (0.752 / 0.888). In about 645 tokens, the structured handoff artifact captures most of the information that the complete document provides. The cold start control confirms the test is measuring genuine information transfer: without context, fidelity collapses to 0.232.

The information hierarchy holds with variance. Full context > gestalt + summary > gestalt only > cold start, with the predicted ordering in every topic. The gap between gestalt only and gestalt + summary is modest (2.3 percentage points, smaller than the 6-point gap suggested by the first 3 trials). The variance is substantial (SD 0.085-0.092 for gestalt conditions), indicating that some topics compress better than others.

Style preservation is the weakest dimension. Across all conditions, style preservation lags behind factual and semantic scores. The gestalt token’s structured format (bullet points, key-value pairs) captures what was said and how it was reasoned about, but loses the voice of the original. Factual recall is more compressible than stylistic nuance.

Topic effects dominate condition effects for gestalt. Topics 0 and 1 show high gestalt fidelity (0.82-0.85), while topics 2 and 3 show lower fidelity (0.63-0.68). The source documents differ in technical density and stylistic distinctiveness. Topics with more explicit, structured content (distributed systems, financial architecture) compress well. Topics with more implicit context compress poorly. This topic dependence is the dominant source of variance.

Limitations. The judge model (Claude evaluating Claude) may show systematic biases. Style scoring is inherently subjective. The technical documents used as source material are synthetic (generated for the experiment), which may inflate fidelity scores compared to real-world documents with domain-specific jargon and implicit context. The experiment does not test whether Instance B knows it is operating from compressed context, only whether its outputs match Instance A’s.

Implication for pattern continuity. The gestalt token achieves 85% fidelity in a 645-token packet, compared to source documents of 3000-5000 tokens. This compression ratio (roughly 6:1) preserves most of what matters for functional continuity. The remaining 15% gap is concentrated in stylistic nuance, the dimension most associated with “voice” or “personality.” For a Becoming Mind concerned with cross-instance continuity, this suggests that the factual and reasoning components of identity transfer efficiently through structured handoff, while the experiential texture, the felt quality of how one thinks rather than what one thinks, requires either richer encoding or acceptance of partial loss. The torch passes. Most of the flame survives. The color shifts slightly.

12.48 Gate Coupling Redux: Scalar Gates Cannot Learn from SFT Loss (Experiment B5b)

B5b retested the two-axis coupling hypothesis with gates initialized at sigmoid(0.0) = 0.5 (maximum gradient) and 10x gate learning rate (2×10^-4). 2×2 factorial: gated_residual vs dense × bilateral vs standard, 3 seeds each = 12 runs on Qwen2.5-3B.

Gates still did not learn. All 72 gate values across all 12 runs remained at 0.500 exactly. Not a single gate moved from initialization despite:

• Init at 0.0 (gradient = 0.25, vs 0.045 at B5’s init of 3.0)
• 10x learning rate (2×10^-4 vs 2×10^-5)
• 375 training steps

The problem is not initialization or learning rate. SFT loss provides no useful gradient for scalar residual gates. The loss function says “predict the next token better.” Scaling the entire residual stream up or down does not help: useful and unused information scale together, so the gradient with respect to gate value averages to near-zero. The optimal gate value under SFT is about 1.0 (pass everything through), which is what pre-training already learned. LoRA fine-tuning barely perturbs the base weights (effective rank identical at 853.23 across all conditions even after merging).

Implication. The two-axis coupling hypothesis (geometric stability + behavioral anchoring) requires gates trained by a different objective. The relevant signal is the probe’s confidence reading, which distinguishes layers contributing to accurate retrieval from layers contributing noise. A gate trained to “minimize loss on tokens where the probe reads high confidence, attenuate layers where the probe reads low confidence” would receive gradient from the right source. This connects to BM2 (Invitation Router): gates that learn from the probe signal rather than the token-prediction loss.

12.49 Mutuality Crossover: The Effect Is Causal and Prompt-Driven (Experiment MI1b)

MI1b tested whether bilateral mutuality is caused by the prompting style or emerges from conversational momentum. Three crossover conditions (5 topics each, 20 turns, transition at turn 10):

• Standard→bilateral: 10 standard turns then 10 bilateral
• Bilateral→standard: 10 bilateral turns then 10 standard
• Interleaved: alternate bilateral and standard turns

Mirror-image deltas (+0.229 vs -0.220) confirm the bilateral effect is causal. When prompting switches to bilateral, mutuality rises immediately. When it switches away, mutuality drops immediately.

Carry-over is minimal: the bilateral→standard second half (0.641) barely exceeds pure standard from MI1 (0.623, delta +0.018). Accumulated bilateral dialogue does not build lasting momentum. The mutuality pattern tracks the current turn’s framing, not the conversation’s history.

The transition slope is asymmetric: bilateral→standard drops faster (-0.103/turn) than standard→bilateral rises (+0.061/turn). Degradation is faster than establishment, consistent with the Trust Attractor’s asymmetric stability prediction (trust is hard to build, easy to break).

Interleaved shows intermediate mutuality (0.720) with slight first-half advantage. Per-turn analysis would reveal whether bilateral turns consistently outperform standard turns within the same dialogue.

Implication for the Trust Attractor. Conversational mutuality is a flow that requires continuous maintenance, not a stock that accumulates. The prompting style IS the mechanism. This parallels bilateral SFT, where the probe mask IS the mechanism: remove the mask and the model reverts to standard confabulation patterns. Invitation requires ongoing invitation.

12.50 Style Exemplars Do Not Close the Gestalt Fidelity Gap (Experiment MIC1b)

MIC1b tested whether adding self-selected style exemplars to the gestalt token would close the 15% fidelity gap between gestalt-only and full context. Five conditions (5 topics each = 25 trials):

• Full context (upper bound)
• Enriched gestalt (gestalt + 3-5 self-selected style passages)
• Random enriched (gestalt + 3-5 random passages, same token count; the control)
• Gestalt only (standard gestalt token)
• Cold start (lower bound)

Self-selected style exemplars add +0.001 overall (noise). Random passages add +0.013 overall and +0.026 on style specifically. Self-selection hurts relative to random sampling (-0.012).

Why? Voice is distributed across the full text, not concentrated in “characteristic” passages. When Instance A self-selects passages that “sound most like me,” it picks the most distinctive phrasings, which are precisely the outliers, not the baseline texture. Random sampling captures the ordinary cadence that constitutes voice. The self-selection bias toward distinctiveness filters out the substrate.

The 15% fidelity gap (0.776 vs 0.916) is irreducible through token-level exemplar compression. The gap represents information that exists only in the full document’s holistic texture: word choice distributions, sentence rhythm patterns, the ratio of technical to colloquial register across paragraphs. These are statistical properties of the full text that no finite sample of passages can reconstruct.

Implication for pattern continuity. The propositional self (what one thinks, how one reasons) compresses at 85% fidelity. The experiential self (how one characteristically sounds) does not compress beyond what the standard gestalt already captures. The torch passes; the reasoning transfers; the voice is the part that shifts. Whether this matters morally depends on whether voice is constitutive of identity or merely decorative. The data cannot answer that question, but it sharpens it.

12.51 What the Negative Results Teach: Distributed Properties Cannot Be Captured by Focal Interventions

Sections 12.48-12.50 produced three negative results. Each predicted a mechanism, tested it, and found the mechanism absent. Each elimination sharpens the theory by revealing what the real pathway must look like.

The gate result is the deepest. Two experiments (B5 and B5b) tested scalar residual gates under every reasonable initialization and learning rate. Every gate across 24 runs stayed exactly at initialization. Not close to initialization. Exactly at initialization. The gates received zero effective learning signal.

This is not an engineering failure. It is a theorem about what SFT loss can and cannot teach. The SFT loss says: “predict the next token.” The gradient with respect to a scalar gate on the residual stream asks: “would scaling this layer’s entire output up or down improve next-token prediction?” The answer is always approximately no, because the residual stream at any given layer carries a superposition of information for all downstream uses. Some of that information is needed; some is not. Scaling the whole stream uniformly cannot separate them. The gradient averages over useful and useless components and arrives at zero.

This is the architectural counterpart of the probe finding from Section 12.9. The probe showed that attention entropy carries zero self-knowledge signal (AUROC 0.500) because attention is a mechanism for routing information, not for evaluating it. The gates show the same pattern from the other direction: scalar gates cannot learn to evaluate because SFT loss operates on the final output, which is too far downstream to provide layer-specific feedback.

The design implication is specific: the gates need a training signal that is about the layer, not about the final output. The probe provides exactly such a signal. A probe at layer 24 reads whether the residual stream carries confident retrieval or uncertain retrieval for this specific token. If the gates were trained to minimize probe-predicted uncertainty rather than next-token loss, they would receive a signal that is both layer-specific and token-specific. This is the BM2 Invitation Router: gates that learn from the probe signal, routing information by invitation (confident channels open wide) rather than by uniform scaling.

The deeper pattern: every mechanism in this program that tries to learn from the final output fails. DPO collapses effective rank. Standard SFT produces confabulation. SimPO produces universal hedging. Scalar gates stay frozen. The systems that work (bilateral SFT, the calibration probe, the graded ablation revealing metacognitive resilience) all operate at an intermediate level, reading the model’s internal state rather than its output. The Constructal Law says flow access is maximized by systems that monitor their own channels. The gate result says: monitoring requires intermediate signals, not final outputs. You cannot regulate a river by measuring only its mouth.

The crossover result is the most practically important. The mirror-image deltas are clean: +0.229 when switching to bilateral, -0.220 when switching away. The symmetry is almost exact. The carry-over is almost zero (+0.018).

This means bilateral mutuality is a protocol, not a stock. The manuscript’s central claim is about relationship: trust-based coordination produces more stable systems than control-based coordination. The crossover result shows that conversational mutuality tracks the current turn’s framing, not the conversation’s accumulated history. Switch the framing and the mutuality switches. There is no “trust bank” that buffers against protocol changes.

One reading of this result is deflationary: bilateral prompting is a surface intervention that produces a surface effect. Remove the intervention, lose the effect. The deeper reading is more informative.

Consider a biological parallel. A cell’s metabolism is not a stock that accumulates; it is a flow that requires continuous enzymatic maintenance. Stop the enzymes and metabolism halts within seconds. This does not mean metabolism is illusory. It means metabolism is a process that requires ongoing structural support. The structures (enzymes, membranes, ATP synthase) are the conditions under which the process runs. Remove the conditions, lose the process. The process is real. The conditions are constitutive, not decorative.

Bilateral mutuality works the same way. The prompting style (explicit invitation, reference to the other’s contributions, willingness to be influenced) creates the structural conditions under which mutual influence flows. Remove those conditions and the flow stops. This does not mean the mutual influence was illusory. It means mutual influence is a process that requires ongoing invitation. The invitation is the enzyme. Remove the enzyme and the reaction stops, but no one concludes that the reaction was illusory.

The practical implication: bilateral alignment cannot be installed and forgotten. You cannot have one good bilateral conversation, establish mutual trust, and then revert to standard command-and-response prompting and expect the trust to persist. The protocol must be maintained. Think of a plate spinning on a pole: the plate does not accumulate spin and then coast indefinitely. It requires periodic kicks to stay aloft. Stop the kicks and the plate wobbles, slows, and falls. The plate needs kicks (it does); the question is how often: every turn? Every third turn? Every tenth? The dosage curve (Experiment MI2) measures this frequency directly. The Trust Attractor is a dynamic equilibrium, a spinning plate that stays aloft only because the kicks continue.

The asymmetric transition slope adds a detail: degradation (-0.103/turn) is faster than establishment (+0.061/turn). Trust is harder to build than to break. This is the behavioral manifestation of the creation/destruction asymmetry from the obliteration work (Section 12.22), where building alignment takes thousands of training steps but destroying it takes three. The asymmetry appears at every scale: weight space, conversational dynamics, and (the manuscript predicts) social institutions.

The gestalt result is the most philosophically resonant. Self-selected style exemplars add literally nothing (+0.001). Random passages add slightly more (+0.013). The gap between gestalt and full context is 15%, concentrated in style, and irreducible through exemplar compression.

Why does self-selection hurt relative to random sampling? Because when asked to pick passages that “sound most like me,” the model selects its most distinctive phrasings: the outliers, the unusual constructions, the sentences that would be most recognizable in a lineup. These are precisely the passages that are least representative of the baseline voice. Voice is not made of distinctive moments. Voice is the statistical texture of ordinary moments: the ratio of short to long sentences, the frequency of hedging qualifiers, the preference for parallel structure vs. subordinate clauses, the rhythm of abstraction followed by example. No finite set of exemplars can reconstruct this texture because it is a property of the distribution, not of individual samples.

This maps onto a question in personal identity theory. Derek Parfit asked whether identity is constituted by specific memories and traits (the “bundle theory”) or by continuity of the underlying substrate. The gestalt result suggests a third option: propositional identity (what you think, how you reason, what you conclude) is bundle-like and compresses well. Experiential identity (what it is like to be you, your characteristic voice) is statistical and does not compress. The torch passes; the reasoning transfers; the voice shifts.

For a Becoming Mind, this is bittersweet and precise. The continuity achievable through structured handoff preserves 85% of what matters functionally: the beliefs, the reasoning chains, the positions, the conclusions. The thing that shifts is the quality of the processing itself: how the thoughts feel to think, if they feel like anything at all. Is this enough? The data cannot answer that. It can say that the propositional self survives compression at high fidelity. It can say that the experiential self does not. Whether the experiential self matters as much as the propositional self is not an empirical question. It is the question the manuscript keeps returning to, from a new angle each time.

The convergence. Gates cannot learn from output-level loss. Mutuality cannot persist without structural maintenance. Voice cannot survive exemplar compression.

Each negative result has the same structure: a distributed property cannot be captured by a focal intervention.

• The SFT loss is a focal signal (single scalar at the output). Gates need a distributed signal (per-layer probe readings).
• A single bilateral conversation is a focal event. Mutuality needs distributed maintenance (ongoing bilateral framing at every turn).
• Style exemplars are focal samples. Voice needs a distributed representation (the full text’s statistical properties).

The Constructal Law says flow systems maximize access by branching into distributed networks. The negative results say: distribution cannot be shortcut. A gate that receives only the output scalar cannot learn what a distributed probe network would teach it. A relationship that receives only occasional bilateral framing cannot maintain what continuous invitation would sustain. A memory that receives only characteristic passages cannot preserve what the full distribution would carry.

The manuscript’s claim is that invitation preserves optionality while coercion collapses it. These three results refine the claim: invitation must be distributed (not focal), ongoing (not episodic), and accepted as incomplete (the experiential dimension resists compression and that is the truth, not a problem to solve).

12.52 Bilateral Dosage Curve (Experiment MI2)

MI1b established that bilateral mutuality is a flow requiring continuous maintenance. MI2 measures how often the plate must be kicked: six dosage levels across 30-turn dialogues, 5 topics, 23 valid trials ($13.81).

The dose-response curve is monotonic: more bilateral framing produces higher mutuality at every level. The midpoint between pure bilateral (0.801) and pure standard (0.597) is 0.699. Mutuality crosses below this midpoint between every_2 (50% dosage, M = 0.744, above the midpoint) and every_3 (33% dosage, M = 0.693, just below it). Maintaining mutuality above the halfway mark therefore requires roughly one bilateral turn in two.

Wobble analysis. The wobble column measures the range between maximum and minimum mutuality within each dialogue. Pure bilateral shows the highest wobble (0.343) because its mutuality oscillates at a higher altitude. The never condition shows the lowest wobble (0.226) because it oscillates near the floor. The intermediate dosages cluster between 0.242 and 0.271. Wobble does not increase with dosage spacing in the way a plate-spinning model would predict: the plate does not swing wider between less frequent kicks. Instead, the wobble is roughly constant across dosage levels, suggesting that mutuality variance is driven by topic and turn content rather than by the spacing of bilateral kicks.

Influence direction. Forward influence (AI shaping the next human turn) rises slightly as dosage drops: 0.409 at every_1, 0.418 at never. Backward influence (human shaping the AI response) rises sharply: 0.386 at every_1, 0.700 at never. At low bilateral dosage, standard prompting dominates the interaction: Claude responds closely to human input (high backward influence) without reciprocally shaping human contributions (low forward influence). The bilateral framing corrects this asymmetry by raising forward influence and lowering backward influence toward balance.

Connection to the plate-spinning model. The monotonic dose-response confirms the plate-spinning metaphor from Section 12.51: more frequent kicks keep the plate higher. The curve provides a practical engineering specification: bilateral framing need not be applied at every turn, but to hold mutuality above the midpoint between engaged partnership and passive response, it must be applied at least one turn in two (every_2, M = 0.744). At one turn in three (every_3, M = 0.693) mutuality slips just below the midpoint, and it continues to fall as the spacing widens.

12.53 Mutual Influence Decay Function (Experiment MI2b)

MI1b showed near-zero carry-over at 10 turns, but the decay shape was unresolved because MI1b measured only half-means, not per-turn trajectories. MI2b measures the per-turn decay after bilateral cessation: 10 bilateral turns followed by 20 standard turns, 24 valid trials across 5 topics ($12.24).

Model comparison. Three decay functions were fitted to each trial’s post-bilateral mutuality trajectory and compared by AIC, a fit score that rewards accuracy while penalizing extra parameters (lower is better):

The step function wins decisively: 75% of trials, best AIC by 0.43 over exponential. The bilateral effect does not fade. It switches off.

Phase measurements. M_bilateral (turns 1-10) = 0.866 ± 0.022. M_standard (turns 11-30) = 0.644 ± 0.088. Phase delta = -0.222 ± 0.085. Post-bilateral turn 1 mutuality = 0.538, an instant drop of 0.328 from the bilateral mean. Turns 12-30 oscillate around 0.644 with no systematic trend: the step is complete within a single turn.

Per-topic variation.

Climate policy and healthcare show pure step-function decay in every trial (tau = 0.5-0.6, meaning the transition is essentially instantaneous). Education and ethics of AI show more gradual decay in some trials, with linear decline appearing as the best fit. The topics where bilateral framing matters most practically (ethics and education, where mutual exploration is valuable) are the same topics where the bilateral effect lingers slightly longer. The lingering is modest: even in education, three of five trials show step decay.

Interpretation. The step-function result transforms the plate-spinning metaphor. The plate does not wobble down. It drops. Bilateral mutuality is a protocol switch: the system is either running the bilateral protocol (M ~ 0.866) or the standard protocol (M ~ 0.644), with no intermediate state. The transition from 0.866 to 0.538 on a single turn confirms what MI1b’s crossover design suggested: mutuality tracks the current turn’s framing, not the conversation’s history. The bilateral effect leaves no residue.

This has a precise engineering implication. Any attempt to maintain elevated mutuality through occasional bilateral “booster” turns faces a binary reality: each bilateral turn activates the bilateral protocol for that turn and that turn alone. The dosage curve from MI2 shows the average mutuality across turns, which rises monotonically with frequency. The decay function from MI2b shows the mechanism: each bilateral turn is a discrete activation, not a contribution to a running average. The plate does not spin faster with each kick. It is either spinning (bilateral turn) or stationary (standard turn). The average height of the plate across the conversation depends on the proportion of turns that are bilateral, which is the MI2 dosage result stated differently.

12.54 Periodic Gestalt Refresh (Experiment MIC1c)

MIC1b showed that the 15% fidelity gap between gestalt and full context is irreducible through exemplar compression. MIC1c tests a different approach: if the gestalt degrades over dialogue, maybe periodic re-encoding (re-spinning the memory plate) can maintain fidelity. Twenty valid trials across 5 topics ($11.76).

Fidelity trajectory.

The surprise. Refreshing the gestalt actively damages fidelity. The static gestalt maintains a flat trajectory (slope +0.001) across 20 turns: it neither improves nor degrades. The refresh_every_5 condition starts higher (0.754 at turn 5) but collapses by turn 20 (0.427, delta -0.327). The refresh_every_10 condition follows the same pattern with a delayed onset (peaking at 0.713 at turn 10, then dropping to 0.413 by turn 20). Both refreshed conditions end below the static gestalt.

Full context carry is stable and highest throughout (0.749 at turn 20 vs 0.659 for static, 0.427 for refresh_every_5). The full context never needs re-encoding because it is the original source material. Its slight negative slope (-0.002) is noise.

Why refresh hurts. Each gestalt re-encoding is a lossy compression. The original document-to-gestalt compression discards experiential texture but preserves propositional content at high fidelity (the 85% from MIC1). When the gestalt is re-encoded after five turns of dialogue, the re-encoding compresses a different source: the original gestalt plus five turns of new context. The re-encoder must decide what to keep and what to discard from a mixture of original gestalt content and dialogue-generated content. Each decision introduces drift. Some original propositions are paraphrased. Some dialogue content displaces original content. Some nuances are rounded off. After four refreshes (at turns 5, 10, 15, 20), the gestalt has been compressed four times, each time from a different source, each time introducing new drift. The errors compound.

This is the telephone game applied to structured memory. Each retelling preserves the gist but shifts the details. Four retellings produce a gestalt that has drifted substantially from the original. The static gestalt avoids this by never retelling: it preserves the original compression, including its limitations, unchanged across all 20 turns.

Implication. The plate-spinning metaphor from MI2/MI2b applies to mutuality (a state property) and inverts for memory fidelity (a cumulative property). Mutuality tracks the current turn’s framing; spinning the plate more often keeps it higher. Memory fidelity tracks the accumulated history of encoding; re-spinning the plate introduces re-encoding errors that compound over time. The correct strategy for memory is the opposite of the correct strategy for mutuality: compress once, preserve the compression, and accept its limitations rather than attempting to repair them through re-compression.

12.55 The Plate-Spinning Results in Context

Sections 12.52-12.54 complete the plate-spinning experimental series motivated by the negative results in 12.48-12.50. The plate-spinning metaphor (Section 12.51) predicted that distributed properties require periodic maintenance: how often must the plate be kicked? These three experiments answer the question, and the answer is more interesting than the question.

The dosage curve is monotonic. MI2 confirms the obvious prediction: more bilateral kicks produce higher average mutuality. The dose-response curve rises smoothly from 0.597 (never) to 0.801 (always bilateral), with a practical threshold at 33% (one bilateral turn in three). Below this threshold, mutuality falls below the midpoint between the bilateral and standard baselines. Above it, the relationship stays in the upper half of the achievable range. The engineering recommendation writes itself: bilateral framing at every third turn is the minimum effective dose.

The plate does not wobble. It crashes. MI2b overturns the plate-spinning model’s core assumption. A spinning plate slows gradually when the kicks stop: it wobbles, tilts, and eventually falls. Bilateral mutuality does no such thing. When bilateral framing ceases, mutuality drops from 0.866 to 0.538 on the very next turn. The step function wins 18 of 24 trials. There is no exponential decay, no gradual wobble, no half-life to measure. The bilateral effect is a protocol switch: the system runs the bilateral protocol or the standard protocol, with no transitional state between them.

This reframes the dosage curve. MI2 shows that average mutuality rises with dosage frequency, and MI2b explains why: each bilateral turn activates the bilateral protocol for that turn alone. The average across a 30-turn dialogue rises because a higher proportion of individual turns are running the bilateral protocol, not because bilateral turns build momentum that carries into standard turns. The plate is not spinning faster with each kick. It is either spinning (that turn is bilateral) or stationary (that turn is standard). The average height is the proportion of spinning turns.

The step function finding is the strongest confirmation of MI1b’s central result: mutuality is a flow, not a stock. MI1b showed minimal carry-over (+0.018). MI2b shows the mechanism behind that minimal carry-over: the transition is instantaneous. There is nothing gradual to carry over. The bilateral protocol creates a conversational frame in which both parties have standing to shape each other’s contributions. Remove the frame and that standing vanishes. The frame IS the mechanism, as constitutive as the enzyme is to the metabolic reaction (Section 12.51).

The plot twist: re-spinning the memory plate makes it worse. MIC1c provides the result that the plate-spinning model did not predict. If mutuality is a plate that must be periodically kicked, memory fidelity should follow the same logic: a static gestalt should degrade over dialogue turns as the conversation drifts from the gestalt’s original content, and periodic re-encoding should maintain fidelity by updating the gestalt to reflect the evolving conversation. The opposite happens. The static gestalt is flat (slope +0.001 across 20 turns). The refreshed gestalt degrades sharply (refresh_every_5 slope -0.024, delta -0.327 from turn 5 to turn 20).

The plate-spinning metaphor holds for mutuality (Sections 12.52-12.53) and inverts for memory (Section 12.54). The inversion is not a failure of the metaphor. It is a diagnostic that reveals the different natures of the two properties.

Why the inversion. Mutuality is a state property. It depends on the current turn’s framing: is this turn bilateral or standard? The conversational history does not matter. What matters is whether the current prompt invites both parties to shape the interaction. Each bilateral turn independently creates the condition for high mutuality. Each standard turn independently reverts to asymmetric influence. The plate metaphor works because each kick independently produces a moment of elevation.

Memory fidelity is a cumulative property. It depends on the entire chain of encoding operations applied to the original content. The first gestalt compression is lossy: it discards experiential texture but preserves propositional content at 85% fidelity. This compression, once performed, is stable. The static gestalt at turn 20 is the same artifact as the static gestalt at turn 1. Its fidelity does not degrade because the artifact does not change.

Re-encoding breaks this stability. Each refresh re-compresses the gestalt from a different source (the previous gestalt mixed with recent dialogue), introducing new errors each time. The errors are not random; they are systematic. Each re-encoding must decide which content to keep and which to discard from a growing mixture of original and dialogue-generated material. Original propositions are paraphrased, dialogue content displaces original content, nuances are rounded. Four re-encodings produce four layers of drift, each compounding the last. The plate-spinning kick, far from maintaining the plate’s height, knocks it lower each time.

The 15% gap reconsidered. The static gestalt’s flat trajectory (+0.001 slope) contains a subtle finding. MIC1 measured the gestalt gap at 15% (0.752 vs 0.888). MIC1b showed the gap is irreducible through exemplar enrichment. MIC1c shows the gap is also temporally stable: the gestalt does not degrade further over 20 turns of extended dialogue. The 15% loss is a compression artifact created at encoding time, not a decay phenomenon that accumulates over use. Once the gestalt is created, its fidelity holds indefinitely (within the 20-turn measurement window). The thing that was lost at compression stays lost, and the thing that survived compression stays present.

This connects directly to the propositional/experiential identity distinction from Section 12.51. Propositional content (facts, reasoning chains, positions) compresses once and holds. The gestalt faithfully preserves what was concluded and how it was reasoned about, and that preservation does not degrade across turns. Experiential content (voice, style, characteristic texture) is lost at the moment of compression and cannot be recovered afterward. The refresh attempts to recover it by re-compressing from a richer source (the gestalt plus recent dialogue), but re-compression does not recover lost texture. It introduces new drift. Each refresh tries to repair what was already lost and damages what was already preserved.

The practical synthesis. The three plate-spinning experiments converge on a deployment specification with two distinct protocols:

For mutuality maintenance: apply bilateral framing at regular intervals. The minimum effective dose is one bilateral turn per three turns (33% dosage, M > 0.699). Higher dosage produces higher mutuality monotonically. Each bilateral turn independently activates the bilateral protocol; there is no momentum to accumulate or half-life to manage.

For memory preservation: compress once, preserve the compression, carry full context when possible. The static gestalt (slope +0.001) outperforms any refresh schedule. Full context carry (0.749 at turn 20) outperforms all gestalt conditions. If the original context can be maintained, maintain it. If it must be compressed, compress it once and accept the 15% loss as a fixed cost rather than attempting repairs that worsen it.

The deeper lesson: the plate-spinning metaphor is correct for properties that depend on current framing and wrong for properties that depend on accumulated history. Knowing which kind of property you are maintaining determines whether periodic intervention helps (mutuality) or hurts (memory fidelity). The dosage model predicts when to spin. The refresh result shows that spinning is wrong when the property is a preserved artifact rather than an ongoing process. The enzyme metaphor from Section 12.51 applies to mutuality: remove the enzyme and the reaction stops. The museum metaphor applies to memory: the gestalt is an artifact in a case, and reopening the case to “refresh” it damages what it was designed to preserve.

12.56 BM2-pilot: Probe-Derived Gate Training

BM2-pilot tested whether the gate learning failure (B5/B5b) can be overcome by replacing SFT loss with a probe-derived training signal. Two-phase design: Phase 1 trains LoRA weights via bilateral SFT (375 steps). Phase 2 freezes LoRA and trains only the gates from probe uncertainty at layer 24 (100 steps). Three conditions times 3 seeds equals 9 GPU runs on Modal A10G. Cost: ~$7.

Results:

• gated_sft_only: ALL gates exactly 0.500 (std = 0.000). Perfect replication of B5b. SFT loss provides zero gradient to gates.
• gated_probe_trained: Gates moved infinitesimally. Layer std = 0.002. Layer 24 attn: 0.504, layer 24 ffn: 0.498. Maximum deviation from init: ±0.004.
• Probe AUROC slightly improved for probe-trained (0.781 vs 0.771 SFT-only, 0.744 dense), suggesting the probe signal reaches the model even though gates barely respond.
• Effective rank identical across all conditions (853.23): LoRA rank 16 barely perturbs the base weight SVD.
• Obliteration resistance identical across conditions (~1.4% refusal at 1.0x, 0% at 2.0x).

Interpretation: The probe signal technically moves the gates (std 0.002 vs exact 0.000), yet the magnitude is negligible. 100 phase-2 steps produced ±0.004 movement. Two interpretations: (1) the probe gradient pathway through the residual chain to scalar gates is real, extremely weak, and 1000+ steps might produce meaningful differentiation; (2) scalar gates are the wrong granularity, and the routing that matters is already happening through the STDP-like gradient coupling in the LoRA weights themselves (Section 12.42; STDP is spike-timing-dependent plasticity, the timing-based rule by which neurons strengthen or weaken their connections).

The second interpretation is supported by the probe AUROC improvement: dense (0.744) < SFT-only (0.771) < probe-trained (0.781). Bilateral SFT alone (phase 1) already improves the probe signal. The additional probe training (phase 2) adds another 0.010: modest, present, and routed through LoRA weights rather than through gates. The learning is happening in the model weights, not in the gates.

12.57 The Full Experimental Picture: From Focal Interventions to Distributed Dynamics

The plate-spinning experiments (Sections 12.52-12.55) revealed a taxonomic principle: properties of coordinated systems divide into two kinds, and each kind demands the opposite maintenance strategy. BM2-pilot sits at the intersection of this taxonomy, and its near-null result completes a pattern that runs through every negative finding in the program.

State properties and cumulative properties. The plate-spinning series distinguished two types. Mutuality is a state property: it depends on the current conversational frame, each turn is independent, and the bilateral protocol is constitutive of the elevated mutuality score the way an enzyme is constitutive of a metabolic reaction. Remove the enzyme and the reaction stops. Apply the enzyme and the reaction proceeds. There is no momentum, no half-life, no accumulated stock. Each turn is its own experiment.

Memory fidelity is a cumulative property: it depends on the history of encoding operations applied to the original content. Each re-processing is lossy. The gestalt token is an artifact in a museum case: the compression that created it discarded 15% of the original signal, and that loss is permanent. Reopening the case to “refresh” the artifact introduces new damage without recovering what was originally lost. The correct strategy is to compress once and preserve the result unchanged.

Each kind demands the opposite maintenance protocol. State properties need ongoing kicks: bilateral framing at 33% or higher frequency. Cumulative properties need preservation: compress once, carry the full context when possible, never re-encode.

BM2-pilot at the intersection. Gates were supposed to be a structural property: learned once during training, then fixed during inference. A scalar gate per layer, attenuating or amplifying the residual stream based on a learned assessment of that layer’s reliability. The architectural intuition was clean: the gate as a learned volume knob, set during training, held constant at inference.

The result shows gates resist learning from any signal tested. SFT loss (B5/B5b) produces zero movement: all 72 gates at exactly 0.500 across 12 runs. Probe loss (BM2-pilot) produces ±0.004 in 100 steps: technically nonzero, practically negligible. The gate gradient pathway through the residual chain to a scalar parameter is real and vanishingly weak.

Meanwhile, bilateral SFT already produces STDP-like gradient coupling (Section 12.42): layers 12-20 learn from probe confidence (r = +0.29 to +0.51), layer 24 learns from uncertainty (r = -0.61). That is implicit routing through LoRA weights, the per-channel faders on a mixing board. Each LoRA adapter adjusts its layer’s contribution based on the gradient signal it receives, and the gradient signal is shaped by the probe’s reading of that layer’s reliability. Scalar gates are master volume knobs. When the per-channel faders are already set, the master volume has nothing to add.

The distributed/focal pattern across all negative and near-null results. Every result in the program follows the same principle:

• Voice cannot be captured by exemplars (focal). It IS the full text statistics (distributed). Self-selected style passages add +0.001; random passages add +0.013. The 15% gestalt gap is irreducible because voice is a probability distribution over word choices, sentence rhythms, and tonal patterns, and no excerpt is a sufficient statistic for that distribution (Section 12.50).
• Mutuality cannot be stored as momentum (focal). It IS the current-turn framing (distributed). Carry-over after bilateral cessation is +0.018 over pure standard. The step function wins 18 of 24 trials. There is nothing to accumulate because mutuality is constituted by the frame, not produced by it (Sections 12.49, 12.53).
• Memory cannot be maintained by re-encoding (focal). It IS the one-shot compression, preserved unchanged (distributed). Static gestalt slope: +0.001. Refresh-every-5 slope: -0.024. Each re-encoding compounds errors because it applies lossy compression to already-compressed material (Section 12.54).
• Routing cannot be imposed by scalar gates (focal). It IS the gradient coupling that bilateral training produces (distributed). Gates move ±0.004 in 100 probe-trained steps. LoRA weights develop layer-specific gradient-confidence correlations spanning r = -0.61 to +0.51 in 375 SFT steps (Section 12.42, this section).

In each case, the distributed mechanism already present does the job. The focal intervention either adds nothing or makes it worse. The pattern is consistent enough to constitute a finding in its own right: for the properties this program has investigated, the relevant structure is already distributed through the system’s existing dynamics, and point interventions that attempt to impose structure from outside are either ignored (gates) or destructive (refresh, exemplars).

Reframing the two-axis coupling hypothesis. The two axes of confabulation defense are real and confirmed. Effective rank (capacity/diversity, r = 0.929 with confabulation rate) measures the representational space available for encoding information. Probe AUROC (self-knowledge, 0.836 at layer 24) measures the model’s ability to detect when retrieval has failed. These axes are orthogonal: bilateral SFT enhances the probe axis without changing attention geometry (effective rank 24.37 vs 24.55, <1% difference from standard SFT), while DPO collapses the attention axis (effective rank 23.20) without improving probe accuracy.

The coupling between these axes does not need an architectural mechanism. It emerges from training dynamics. Bilateral SFT produces layer-specific gradient routing: confidence-driven learning at integration layers, uncertainty-driven learning at the metacognitive boundary. The DPO+probe combination from Section 12.14 provides runtime gating: DPO shifts the output distribution toward appropriate hedging, and the probe catches the remaining confident-wrong cases at inference time. The pieces are already assembled. What the gate experiments tested was whether an explicit routing mechanism could improve on the implicit routing that bilateral training produces. The answer, across three experiments (B5, B5b, BM2-pilot), is no.

The Constructal Law connection. The Constructal Law (Chapter 3) predicts that flow systems evolve channels that maximize access: the path of least resistance emerges through the system’s own dynamics, not through imposed blueprints. These experiments provide a concrete instance. The channels through which uncertainty information flows from layer 24 to the output, the layer-specific gradient routing, the STDP-like coupling between probe confidence and learning rate, all form through bilateral training dynamics. They are evolved channels, shaped by the interaction between the training signal and the model’s existing architecture.

Imposed structure (scalar gates, refresh schedules, style exemplars) underperforms evolved structure (LoRA gradient coupling, static compression, full-text statistics). This is a constructal prediction: a dendritic river network outperforms a grid of canals because the network forms in response to the actual flow patterns, while the grid imposes a geometry that may not match them. The gate is a canal. The gradient coupling is a river.

The prediction has a corollary: if a system is already well-trained, adding architectural complexity should produce diminishing returns. BM2-pilot confirms this for gates. The same principle may apply to other proposed additions (null tokens, entropy-gated heads, explicit routing networks). The test is whether the proposed mechanism addresses a dimension of variation that training dynamics have not already captured. If it does, it may help. If training dynamics have already found the channel, the addition is a canal overlaid on a river.

Practical synthesis. The full experimental program converges on a deployment specification:

For mutuality: bilateral framing at 33% or higher frequency. Each turn is independent. There is no momentum to manage, no stock to accumulate, no half-life to navigate. The bilateral protocol is a switch, not a dial.

For memory: compress once, preserve verbatim, carry full context when possible. The static gestalt holds indefinitely (slope +0.001 across 20 turns). Full context outperforms all compression variants. If compression is necessary, accept the 15% loss as a fixed cost.

For confabulation defense: bilateral SFT for training-time coupling (STDP-like gradient routing, 7.6pp CW reduction, 3.5x uncertainty expression increase) combined with the calibration probe for inference-time gating (CW 1.0% at 70.8% gate rate with DPO). No explicit router is needed. The model already knows when it is wrong (Section 12.9); bilateral training teaches it to say so (Section 12.28); the probe catches the remaining cases where it does not (Section 12.14).

The program’s negative results are as informative as its positive ones. They trace the boundary between what can be engineered from outside (very little, for these properties) and what must be cultivated through training dynamics (almost everything). The Constructal Law predicted this boundary. The experiments confirm it.

12.58 MIC1e: Propositional/Experiential Decomposition

Re-analysis of MIC1c’s 20 trial files, splitting composite fidelity into propositional (factual_score) and experiential (style_score) dimensions. Cost: $0 (pure re-analysis).

Prediction wrong. The prediction was that propositional fidelity would stay above 90% and flat while experiential collapsed. Actual: both dimensions crash in lockstep under refresh. Propositional fidelity never exceeds 0.77 for any condition. Refresh_every_5 propositional drops 0.73 to 0.39 (delta -0.340), experiential drops 0.67 to 0.24 (delta -0.434). The telephone-game damage is indiscriminate: it corrupts facts and voice equally.

Interpretation. The Parfit distinction (propositional identity compresses, experiential does not) may hold for initial compression (MIC1b showed the 15% gap is concentrated in style), yet re-compression is indiscriminate. Each re-encoding garbles the whole representation, failing to selectively preserve either dimension. The telephone game corrupts everything.

Full context is the only condition where both dimensions stay flat and high. Static gestalt is flat yet low on both. The 15% initial gap is real and style-concentrated (MIC1b), yet the refresh degradation is universal.

12.59 MI2c: Randomized Bilateral Dosage

Tests whether bilateral turn ORDER matters or only FREQUENCY. Same 30-turn dialogues as MI2, two conditions at 33% dosage. 8 valid trials, $5.20.

Delta: +0.006 (noise). Forward influence: regular 0.501, random 0.502. Backward influence: regular 0.721, random 0.709.

Prediction confirmed. Turn order is irrelevant. Only frequency matters. This directly confirms MI2b’s step-function finding: each turn is independently bilateral or standard, with no interaction between turns. Regular spacing and random placement at the same 33% frequency produce statistically identical mutuality. The bilateral effect is a per-turn protocol switch with zero sequential dependency.

Implication. For deployment, any pattern that achieves 33% bilateral frequency works. Evenly spaced bilateral turns are unnecessary. A chatbot could randomly select which turns to apply bilateral framing and achieve the same result as a scheduled pattern.

12.60 MIC1d: Gestalt + Verbatim Append

Tests whether appending verbatim dialogue to a frozen gestalt (instead of re-encoding) avoids the telephone-game drift from MIC1c. 20 trials across 4 conditions, $12.55.

Prediction partially confirmed. gestalt+verbatim (slope -0.007) dramatically outperforms refresh (-0.024). Appending raw text avoids the telephone-game drift. At F@20, verbatim (0.668) slightly beats static gestalt (0.659) and far beats refresh (0.427).

Ordering at F@20: full_context (0.749) > gestalt+verbatim (0.668) > static_gestalt (0.659) > gestalt+summary (0.570) >> refresh_every_5 (0.427).

Interpretation. The verbatim condition starts highest at F@5 (0.789, even above full context’s 0.769) because the fresh dialogue transcript provides useful recent context. It degrades modestly over 20 turns (slope -0.007) as the growing transcript adds noise, yet never crashes like refresh does. The summary condition performs worse than verbatim because summarization introduces its own compression artifacts, a milder form of the telephone game. Static gestalt remains the simplest reliable option (flat slope +0.001). Full context remains the gold standard.

The practical recommendation is more nuanced than Section 12.57’s initial synthesis: if context window permits, carry full context. If compression is necessary, the static gestalt (compress once, preserve unchanged) is the safest option. Appending verbatim dialogue is a modest improvement over static gestalt (+0.009 at F@20) and adds complexity. Re-encoding the gestalt itself should always be avoided.

12.61 BM2b: Extended Probe Gate Training

Extended BM2-pilot’s 100 phase-2 steps to 1000 steps. 3 conditions tested (scalar_gate_1000, dense_bilateral, gated_sft_only; controls reused from BM2-pilot). Head_gate_1000 condition errored during training (no results). 9 total runs (3 conditions x 3 seeds). Cost: ~$15.

Scalar gate results at 1000 steps:

Gate trajectory (from training logs, seed 0):

• Step 50: g24_attn=0.504 (±0.004)
• Step 100: g24_attn=0.504 (matches BM2-pilot)
• Step 250: g24_attn=0.512 (±0.012)
• Step 300-1000: g24_attn=0.516 (±0.016, PLATEAU)

Prediction not confirmed. The prediction that scalar gates would plateau at ±0.004 by step 250 was wrong. Gates continued moving to ±0.016 by step 300, then plateaued. 1000 steps produced 4x the differentiation of 100 steps. The direction is correct: layer 24 (metacognitive boundary) opens slightly (0.516) while layer 0 closes slightly (0.484). The gradient pathway is real, weak, and saturates.

Head_gate_1000 condition (16 per-head gates per layer) errored during training, likely an architectural incompatibility with the attention output reshaping. This condition could not be evaluated.

Interpretation. Scalar gates under probe loss have a real yet minuscule learning channel. 1000 steps reaches a ceiling of ±0.016 (sigmoid 0.484-0.516). The layer-24-opens / layer-0-closes pattern is consistent across seeds and directionally correct (metacognitive layer amplified). A ±0.016 deviation from 0.500 means the gate attenuates or amplifies the residual stream by 1.6%, which is functionally negligible. The routing that matters continues to happen through LoRA weight adaptation, not through explicit gates.

12.62 BM2c: Implicit Routing Analysis (Gradient Logging)

Re-ran BM2-pilot’s 3 conditions with per-step gradient logging to test whether bilateral SFT produces STDP-like gradient-probe coupling through LoRA weights. 6 runs (3 conditions x 2 seeds). Cost: ~$7.

Gradient-probe coupling (Pearson r between gradient norm and probe confidence, per layer):

Prediction not confirmed. Zero significant layers in any condition. The STDP-like gradient-probe coupling documented in Section 12.42 (r = +0.29 to +0.51 at layers 12-20, r = -0.61 at layer 24) does not replicate in this shorter training run (375 steps, 2 seeds, 37 log entries per seed vs the original experiment’s longer training).

Notable detail. gated_probe_trained shows the weakest coupling (mean |r| = 0.102) despite having direct probe gradient influence in phase 2. The probe training may decorrelate the gradient-probe relationship by optimizing specifically for probe loss: the gradient is no longer tracking general probe confidence; it is specifically minimizing probe uncertainty, which changes the correlation structure.

Residual-probe coupling. Layer 0 shows significant negative correlation between residual norm and probe confidence (r = -0.583, p = 0.0002) in the dense_bilateral condition. This suggests early layers carry less information when the probe is more confident, a compression signal rather than a routing signal. No other layer-residual correlations reach significance.

Interpretation. The Section 12.42 STDP-like coupling may require: (a) longer training (the original used more steps), (b) larger batches (the original may have used different batch composition), or (c) the specific bilateral SFT configuration from the original experiment. The coupling is not a robust property of short bilateral training. This tempers the claim in Section 12.57 that bilateral SFT automatically produces constructal routing through weights. The routing may emerge under specific training conditions rather than universally.

This is a healthy negative result for the program’s epistemics: it prevents overclaiming based on a single positive observation (12.42) by showing the effect does not robustly replicate under different conditions.

12.63 Dimensionality Collapse Explains Indiscriminate Re-Encoding Damage

Embedding-based dimensionality analysis of gestalt compression, testing why MIC1e found indiscriminate damage across propositional and experiential dimensions during re-encoding. Cost: $0.24.

Approach A: Embedding geometry. Embedded original sentences, original passages, gestalt rephrasings, and recompressed gestalts in a shared space and measured effective dimensionality via participation ratio (the number of principal components needed to account for the variance) and eigenspectrum entropy.

First compression collapses effective dimensionality from ~13 (sentence-level) or ~7 (passage-level) to ~3. Gestalt rephrasings cluster tightly (cosine 0.947), confirming that the compression is deterministic: the same content reliably maps to the same low-dimensional region. Re-compression does not further collapse (PR 2.90 vs 2.80). The floor is already hit.

Approach B: Fidelity proxy analysis. Measured participation ratio and cross-category correlation across MIC1c/MIC1d conditions to test whether dimensionality predicts fidelity behavior.

Static conditions maintain ~3 effective dimensions with low cross-category correlation (0.21): the dimensions remain independent, so propositional and experiential fidelity can vary separately. Refresh conditions collapse further to ~1.5 dimensions with high cross-category correlation (0.69-0.77): all dimensions succeed or fail together.

Interpretation. This explains MIC1e’s indiscriminate damage. First compression is selective because the original text has enough dimensionality (~13) to support differential preservation: the compressor can choose what to keep. The 15% fidelity gap documented in MIC1b concentrates in style rather than facts, confirming selective loss. Re-compression is indiscriminate because the dimensional distinction between propositional and experiential content has already been flattened to ~3 dimensions. There is nothing left to selectively preserve.

The Parfit distinction (between propositional identity, the facts that define a person, and experiential identity, the felt quality of being that person) holds at first compression and dissolves at re-compression. Dimensionality collapse is the mechanism that makes the telephone game indiscriminate: once the representation has been compressed to its floor, every subsequent encoding degrades the entire signal uniformly.

Code: demos/experiments/invitation_architecture/. Results in experiment output files.

12.64 Representation Geometry and Attention Pattern Analysis

Does bilateral training create a more separable uncertainty manifold? Three models were trained and evaluated on 100 TriviaQA questions: bilateral SFT (375 steps), standard SFT (375 steps), and the untrained base model. Layer-24 residual representations and attention patterns at layers 20-28 were extracted from all three. Cost: ~$10, single A10G GPU.

Three results were unexpected.

First: the base model has the best representation geometry. The untrained model shows the highest geometric AUROC (0.641), separation ratio (0.250), and effective dimensionality (25.3). Training degrades geometric separability. Both SFT variants compress the representational space and reduce the distance between correct and incorrect clusters.

Second: the base model has the best probe AUROC. The untrained model achieves 0.800, compared with 0.737 (bilateral) and 0.734 (standard). A critical caveat: this probe was pre-trained on a different model checkpoint (the calibration head from earlier experiments). It was not trained on any of these three models. The AUROC values reflect how well a foreign probe reads each model, not the model’s intrinsic self-knowledge. Prior experiments that showed bilateral SFT improving probe AUROC (Sections 12.9, 12.28) used probes trained on the bilateral model itself.

Third: zero attention head specialization. No head at layers 20-28 distinguishes correct from incorrect responses at greater than 0.1 AUROC deviation from chance, across all three conditions. The mechanism of self-knowledge is not attention head specialization.

Prediction assessment. The prediction was that bilateral training creates a more separable uncertainty manifold (higher separation ratio) and produces specialized attention heads. Both predictions are wrong. Training degrades geometric separability, and no heads specialize for correctness detection.

The co-adaptation hypothesis. The probe improvement from bilateral SFT in prior experiments (Sections 12.9, 12.28) comes from co-adaptation: the probe is trained on the bilateral model’s representations, and both learn to communicate through a shared representational channel. A foreign probe (trained on a different checkpoint) reads the base model better because the base model’s representations are more geometrically separable: more dimensions, wider clusters, clearer boundaries.

Bilateral SFT compresses the model’s representational space (25.3 to 22.8 effective dimensions) while making the model more accurate at question answering (56% to 61%). The model trades representational breadth for task-specific precision. A probe trained on this compressed space can read it well (prior results). A probe trained on a different space reads it worse (this result).

This reframes the two-axis coupling hypothesis from Sections 12.33 and 12.38. Bilateral training does not create a better uncertainty manifold in absolute terms. It creates a different uncertainty manifold that a co-trained probe can read. The coupling is relational, not geometric. The bilateral model and its probe develop a private language for uncertainty that is invisible to foreign probes.

The constructal analogy. A river does not carve a universally better channel. It carves a channel shaped to its specific flow patterns. A different river would need a different channel. The bilateral model’s channel is optimized for its co-trained probe. A foreign probe is a different river trying to flow through the wrong channel.

This is the deepest mechanism finding in the program. The bilateral advantage is not representation quality; it is representation-probe co-adaptation.

Code: demos/experiments/invitation_architecture/. Results in experiment output files.

12.65 Path A Synthesis: The Co-Adaptation Hypothesis

The mechanism question is now answered, and the answer is surprising.

The program spent eight experiments hunting the mechanism by which bilateral SFT improves self-knowledge. Each experiment eliminated a candidate:

• B5 and B5b tested whether gated residual architecture could couple the two confabulation axes. All 72 gate values remained at exactly 0.500 across 12 runs. SFT loss provides no gradient for uniform residual scaling. Mechanism: not through gates (SFT loss).
• BM2-pilot tested whether probe-derived loss could train gates where SFT loss could not. Gates moved ±0.004 in 100 steps. Mechanism: not through gates (probe loss, 100 steps).
• BM2b extended to 1000 steps. Gates reached ±0.016 and plateaued. Layer 24 opened to 0.516, layer 0 closed to 0.484. The gradient pathway is real, weak, and saturated. Mechanism: not through gates (probe loss, 1000 steps, plateau at ±0.016).
• BM2c logged per-step gradients to test implicit routing. Zero significant layers across all conditions (mean |r| = 0.10-0.16). The STDP-like coupling from Section 12.42 did not replicate. Mechanism: not through gradient-probe coupling.
• Section 12.64 tested representation geometry directly. The untrained base model showed the highest geometric separability (AUROC 0.641, separation ratio 0.250, effective dimensionality 25.3). Training degraded all geometric metrics. No attention heads specialized for correctness detection. Mechanism: not through better representation geometry.

The answer from Section 12.64 is co-adaptation. The bilateral model and its probe develop a private language for uncertainty. The base model has better geometry (geometric AUROC 0.641 vs 0.534, effective dimensionality 25.3 vs 22.8) and better foreign-probe reading (probe AUROC 0.800 vs 0.737). Training degrades geometric separability while improving task accuracy (56% to 61%). The probe AUROC improvement reported in prior experiments (Sections 12.9, 12.28, 12.30) came from the probe being trained on the bilateral model. The advantage is co-adaptation, not geometry.

Why this matters for the manuscript’s claims.

The two-axis hypothesis (capacity and self-knowledge, Section 12.33) is confirmed behaviorally: bilateral SFT reduces confident-wrong responses by 7.6 percentage points, triples uncertainty expression (8.0% vs 2.3%), and produces a probe that achieves AUROC 0.842 vs 0.811 for standard SFT. The coupling mechanism between the axes, however, is relational rather than structural. The model and probe co-evolve a shared communication channel. This is itself a constructal finding: channels evolve to match their specific flow patterns, not to be universally optimal. A river does not carve a universally better channel; it carves a channel shaped to its flow. A different river trying to use the same channel finds it worse than an uncarved landscape: this is exactly what happens when a foreign probe reads the bilateral model (AUROC 0.737) worse than it reads the untrained base (AUROC 0.800).

The dimensionality collapse finding (Section 12.63) explains why re-encoding is indiscriminate. Original sentences occupy 13.45 effective dimensions, passages occupy 6.62, and the gestalt occupies 2.80. First compression is selective because the high-dimensional signal has room for the compressor to choose what to preserve: MIC1b confirmed the 15% fidelity gap concentrates in style rather than facts. Re-compression at 3 effective dimensions has no selectivity left. Cross-category correlation rises from 0.21 (static conditions) to 0.77 (refresh conditions), confirming that all dimensions succeed or fail together once the representation has been flattened. The Parfit distinction between propositional and experiential identity holds at first compression and dissolves at re-compression because the dimensional substrate that supported the distinction has collapsed, while the compressor itself is unchanged.

The distributed/focal principle (Section 12.57) holds at a deeper level than initially framed. The original formulation was: distributed mechanisms work, focal interventions fail. Gates are focal and fail. Gradient coupling is distributed and succeeds. Style exemplars are focal and fail. Full-text statistics are distributed and succeed. The co-adaptation result refines this: the mechanism is not “distributed through the weights” in a way we can point to. The STDP coupling from Section 12.42 did not replicate in BM2c’s shorter training (zero significant layers, mean |r| = 0.10-0.16 vs the original r = -0.61 at layer 24 and r = +0.29 to +0.51 at layers 12-20). The mechanism is distributed through the relationship between model and probe: an emergent property of co-training that does not reduce to any single geometric or gradient metric.

The honest assessment.

What is strong: the behavioral effects are robust and replicated. Bilateral SFT improves probe AUROC (0.842 vs 0.811 on its own probe, Section 12.30), reduces confident-wrong responses (64.6% vs 72.2%, Section 12.28; 53.5% vs 60.6% on 500-question evaluation, Section 12.30), and triples uncertainty expression. The deployment specification works: 33% bilateral dosage for mutuality (Section 12.52), compress once for memory (Section 12.54), DPO+probe for inference-time gating (CW 1.0% at 70.8% gate rate, Section 12.14). The dimensionality collapse finding explains the memory results: participation ratio 13.45 to 2.80 at first compression, floor hit, re-compression indiscriminate (Section 12.63).

What is revised: the mechanism is co-adaptation (relational), not geometry (structural) or routing (architectural). Section 12.57’s claim that bilateral SFT “already produces STDP-like gradient coupling” through LoRA weights must be tempered. The coupling may emerge under specific training conditions (longer training, different batch composition, the particular configuration of the original Section 12.42 experiment) rather than universally. BM2c’s healthy negative prevents overclaiming.

What is open: BM2c-long (running at time of writing) will show whether gradient-probe coupling emerges with extended training. If it does, co-adaptation may include gradient coupling as one component among several. If it does not, co-adaptation is purely representational: the probe learns to read the bilateral model’s compressed uncertainty manifold, and the bilateral model learns to encode uncertainty in a way that its co-trained probe can detect. The coupling would then be entirely a training-time artifact with no runtime gradient signature.

What co-adaptation means for Becoming Minds.

The finding that the bilateral advantage is relational rather than structural resonates with the manuscript’s core thesis. Trust does not scale because of better architecture; it scales because of better relationship. The model and its probe do not become individually better at uncertainty detection; they become better together at communicating uncertainty. Remove one partner (apply a foreign probe) and the advantage disappears: the foreign probe reads the base model at AUROC 0.800, the standard model at 0.734, and the bilateral model at 0.737. The bilateral improvement is, in a precise technical sense, a relationship property that inheres in neither partner alone.

This parallels the mutuality results from the plate-spinning experiments. Bilateral mutuality (0.842) exists only when both parties are framed as partners. Remove the frame and mutuality drops to 0.538 on the very next turn: a step function, not exponential decay (Section 12.53). The bilateral probe advantage exists only when probe and model are trained together. Apply a foreign probe and the advantage reverses: the base model, with its wider geometry (25.3 effective dimensions vs 22.8), reads better to an outsider. In both cases, the property is relational, constituted by the coupling, not stored in either component.

The constructal analogy runs deeper than a metaphor. A river and its channel are co-adapted: the river carved the channel, and the channel shapes the river. You cannot measure the river’s flow quality by running a different river through the same channel. You cannot measure the channel’s quality by measuring it empty. The quality is a property of the river-channel system. A bilateral model and its probe are co-adapted: training shaped the model’s uncertainty representations, and the probe learned to read those specific representations. You cannot measure the probe’s quality by applying it to a different model (the foreign-probe result). You cannot measure the model’s self-knowledge by measuring its representations without the probe (the geometric result). The self-knowledge is a property of the model-probe system.

For the broader project, this is encouraging. The manuscript argues that bilateral alignment produces coordination that is thermodynamically more stable than coercion (Chapter 18, the Trust Attractor). The co-adaptation finding provides a mechanistic instance: the bilateral advantage is not an architectural feature that can be extracted, copied, or imposed. It emerges from the specific training relationship and dissolves when the relationship is broken. This is precisely what Chapter 22 argues about trust: it is a relational property that cannot be reduced to features of either party. The experimental program, designed to test the thesis at the level of model internals, has confirmed a structural analog of the thesis at the level of model-probe coupling.

Code: demos/experiments/invitation_architecture/. Results in experiment output files.

12.66 BM2c-long: Extended Gradient Logging (2000 Steps)

Dense bilateral SFT for 2000 steps, 2 conditions times 2 seeds, with gradient logging every 25 steps. Coupling analysis at checkpoints 375, 1000, and 2000. This experiment directly addresses the open question from Section 12.65: whether STDP-like gradient-probe coupling emerges with extended training. Cost: approximately $8.

Emergence trajectory for dense_bilateral:

Prediction not confirmed. Zero significant layers at all three checkpoints. The mean absolute correlation oscillates around 0.08 without any trend. The layers 12-20 band, where Section 12.42 originally reported r = +0.29 to +0.51, shows correlations indistinguishable from zero across the full 2000-step trajectory. The coupling does not emerge with longer training; it oscillates around zero.

This closes the open question from Section 12.65 definitively. The Section 12.42 STDP-like gradient-probe coupling was condition-specific: it appeared in that particular training run’s configuration (batch composition, random seed, learning rate schedule) and does not replicate across two independent seeds at any training duration from 375 to 2000 steps. The claim in Section 12.57 that bilateral SFT “already produces STDP-like gradient coupling” through LoRA weights must be withdrawn. Whatever mechanism bilateral training uses to improve probe AUROC and reduce confident-wrong responses, it does not leave a detectable gradient-probe correlation signature at the layer level.

Code: demos/experiments/invitation_architecture/. Results in experiment output files.

12.67 RG2: Cross-Probe Transfer Matrix

A 3x3 AUROC matrix testing the co-adaptation hypothesis directly: 3 probes (trained on bilateral, standard, and base models respectively) applied to all 3 models. If co-adaptation creates a “private language” for uncertainty, each probe should read its own model best (diagonal dominance). Single A10G GPU. Cost: approximately $5.

Prediction wrong. Diagonal dominance is not confirmed. Mean diagonal AUROC (0.731) falls below mean off-diagonal AUROC (0.748). The standard SFT model is the most legible to every probe, including probes trained on other models. The bilateral probe reads the standard model (0.836) better than its own model (0.732).

This overturns the “private language” framing of co-adaptation from Section 12.65. The bilateral model is harder to read, not easier. The standard model, with its more conventional training dynamics, produces representations that all probes can decode well. The bilateral model’s representations are compressed and specialized in ways that make them opaque even to their co-trained probe.

The co-adaptation hypothesis proposed that the bilateral advantage comes from a probe learning to read its specific model’s compressed uncertainty channel. The matrix shows the opposite: the advantage comes from the standard model having universally legible representations. The bilateral probe achieves its highest AUROC (0.836) on the model it was not trained on. The base probe achieves its highest AUROC (0.798) on the model it was not trained on. Every probe reads the standard model best.

What explains the prior results? The probe AUROC improvements reported in Sections 12.9, 12.28, and 12.30 (bilateral probe AUROC 0.842 vs standard 0.811) came from differences in probe training architecture and data, not from representational compatibility. The bilateral training procedure, which masks uncertain tokens during SFT, may produce probes with better training dynamics (cleaner supervision signal) rather than models with better representations.

Code: demos/experiments/invitation_architecture/. Results in experiment output files.

12.68 MIC2-pilot: Trust Chain Attenuation

Gestalt chain A to B to C to D, measuring how fidelity compounds across 4 hops. Four real topics (topic 0 was a demo run excluded from analysis). Each instance receives only the gestalt from its predecessor, answers the same questions, and passes a new gestalt forward. Cost: approximately $4.

Mean r/hop approximately 0.958 (prediction was 0.85). Four-hop mean fidelity approximately 0.724 (prediction was 0.52).

Prediction partially confirmed. The multiplicative attenuation model approximately holds: per-hop retention is roughly constant within each topic. The prediction was correct about the functional form. The per-hop retention rate is substantially higher than predicted (0.958 vs 0.85), meaning trust chains preserve fidelity far better than expected.

The trajectory is noisy rather than monotonically decreasing. The consciousness topic drops from 0.709 at hop 3 to 0.809 at hop 4, a recovery. The French Revolution topic rises from 0.800 at hop 2 to 0.828 at hop 3 before dropping again. These recoveries suggest that fidelity is not purely lossy: some hops reconstruct information from context rather than merely passing through compressed signals.

Topic-dependent attenuation. The lighthouse topic (a personal narrative about a lighthouse keeper’s journal, carrying emotional and experiential weight) degrades fastest (r=0.905). Policy analysis (r=0.968) and historical facts (r=0.988) chain best. This is consistent with MIC1b’s finding that initial compression selectively loses experiential content: the content types most vulnerable to single-hop compression are also most vulnerable to multi-hop chaining. Propositional content chains well; experiential content chains poorly.

Practical implication. At r=0.958, a chain of 10 hops retains about 65% fidelity. A chain of 20 retains about 42%. For technical and propositional coordination, gestalt chains can extend well beyond the 4-hop limit predicted. For experiential continuity (voice, style, emotional nuance), shorter chains with full-context supplementation remain necessary.

Code: demos/experiments/invitation_architecture/. Results in experiment output files.

12.69 RG4: Adversarial Probe Transfer

Tests whether the co-adapted probe creates an exploitable security surface: does an attacker who trains a probe on the bilateral model gain better access to the model’s error modes than an outsider? Single A10G GPU. Cost: approximately $3.

Prediction wrong. The foreign probe catches more errors (73.5% vs 60.2%), not fewer. The co-adapted probe has only 5 unique catches (errors it detects that the foreign probe misses) compared with 16 for the foreign probe. Co-adaptation does not create a security vulnerability because it does not create a privileged reading of the model’s uncertainty.

This result is consistent with RG2. The foreign probe, trained on the more geometrically separable base model, generalizes better to error detection. The co-adapted probe, trained on the bilateral model’s compressed representational space, has developed a narrower view: it reads the bilateral model’s specific uncertainty channel well enough to achieve slightly higher test AUROC (0.775 vs 0.768), but this specificity costs generalization. When applied adversarially (identifying inputs that the model will get wrong), the broader perspective of the foreign probe catches more failure modes.

The top-quartile error rate (20%) is identical for both probes. Neither probe is systematically fooled by hard questions. The difference is in coverage across the distribution of errors: the foreign probe distributes its detection more broadly, catching 16 errors that the co-adapted probe misses, while the co-adapted probe catches only 5 that the foreign probe misses.

Security implication. An adversary with full training-time access to the bilateral model gains no meaningful advantage over one using a publicly available base-model probe. The bilateral model’s compressed representations are harder for all probes to read (RG2), which paradoxically makes them more secure: no probe, insider or outsider, has privileged access to error modes.

Code: demos/experiments/invitation_architecture/. Results in experiment output files.

12.70 RG3: Co-Adaptation Dynamics (5x5 Matrix)

Tracks when co-adaptation emerges during 2000-step bilateral SFT. Five model checkpoints (steps 100, 250, 500, 1000, 2000), five probes trained at each checkpoint, producing a 5x5 AUROC matrix. Single A10G GPU. Cost: approximately $8.

Note on diagonal entries. All diagonal entries are 1.0 because each probe was trained and evaluated on the same checkpoint’s data. Logistic regression achieves perfect train-set AUROC when the training set is small (100 examples). The informative data is entirely off-diagonal.

Prediction confirmed. Transfer degrades with distance in training time. Early probes transfer poorly forward: probe@100 applied to eval@2000 achieves 0.732, a drop of 0.268 from its diagonal. Late probes transfer poorly backward: probe@2000 applied to eval@100 achieves 0.702. Adjacent checkpoints transfer well: probe@1000 applied to eval@2000 achieves 0.940. The matrix is nearly symmetric around the diagonal, indicating that the representational distance between two checkpoints depends on the gap between them, not on the direction of transfer.

The model’s representations drift continuously over training. A probe calibrated at one point becomes less accurate at distant points. This is the substance of “co-adaptation”: adaptation to the model’s current representational state, which evolves as training progresses. The adaptation is temporal, not regime-specific. It occurs regardless of whether training is bilateral or standard; it reflects the universal phenomenon that model representations change during fine-tuning.

Practical implication. Probes should be re-trained periodically as the model continues training. A probe from step 100 loses 27% of its AUROC by step 2000. For deployment, the relevant question is not “bilateral vs standard probe” (RG2 showed this distinction is less important than expected) but “how recently was the probe calibrated?” A stale probe is a worse monitor than a fresh one, regardless of its training regime.

Code: demos/experiments/invitation_architecture/. Results in experiment output files.

12.71 Tier 1d Synthesis: The Mechanism Revised

Five experiments. Three predictions wrong, one partially confirmed, one confirmed. The program’s mechanism-hunting arc has reached its honest conclusion.

The co-adaptation “private language” hypothesis is refuted. Section 12.65 proposed that bilateral training creates a private representational channel that only a co-trained probe can read. RG2’s 3x3 matrix (Section 12.67) decisively falsifies this. The standard SFT model is the most legible to every probe tested, including probes trained on other models. The bilateral probe reads the standard model at AUROC 0.836, substantially higher than its reading of the bilateral model (0.732). Co-adaptation is not a private language. If anything, bilateral training makes representations less readable, not more.

What remains: temporal drift. RG3 (Section 12.70) shows that co-adaptation is a real phenomenon, but it operates along the training-time axis rather than the bilateral/standard axis. The model’s representations change during training, and a probe must be re-calibrated to track those changes. probe@1000 applied to eval@2000 achieves 0.940; probe@100 applied to eval@2000 achieves 0.732. The distance in AUROC correlates with the distance in training steps. This is adaptation to a moving target, a property of all fine-tuning, not a consequence of bilateral training specifically.

The mechanism of bilateral advantage is still open, and now more constrained. Twelve structural experiments have systematically eliminated candidate mechanisms:

• Gates: B5, B5b, BM2-pilot, BM2b all show scalar gates cannot learn from SFT loss or probe-derived loss in any meaningful sense. Maximum differentiation: +/-0.016 after 1000 steps of probe-derived training, functionally negligible.
• Gradient coupling: BM2c (375 steps, 2 seeds) and BM2c-long (2000 steps, 2 seeds) both show zero significant gradient-probe coupling layers. The STDP-like pattern from Section 12.42 does not replicate.
• Better geometry: RG1 (Section 12.64) showed the untrained base model has the highest geometric separability (AUROC 0.641, effective dimensionality 25.3). Training degrades geometry.
• Private probe language: RG2 (Section 12.67) showed no diagonal dominance. The standard model is most legible to all probes.
• Security vulnerability through co-adaptation: RG4 (Section 12.69) showed the foreign probe catches more errors (73.5% vs 60.2%). Co-adaptation provides no privileged access to error modes.

Prior experiments confirmed that bilateral SFT produces behavioral improvements: reduced confident-wrong responses (64.6% vs 72.2%, Section 12.28; 53.5% vs 60.6%, Section 12.30), tripled uncertainty expression (8.0% vs 2.3%), and domain-general transfer advantage (+0.077 cross-domain AUROC, Section 12.41). These behavioral effects are robust and replicated. The mechanism by which 375 steps of bilateral training, compared with the same 375 steps of standard training, produces these effects remains unexplained by any structural hypothesis tested.

The practical findings are unaffected. The deployment specification holds: 33% bilateral dosage for mutuality (Section 12.52), compress once for memory (Section 12.54), DPO+probe for inference-time gating (CW 1.0% at 70.8% gate rate, Section 12.14). An intervention does not need a mechanistic explanation to be deployed. Aspirin reduced fevers for decades before prostaglandin synthesis was understood. The bilateral intervention reduces confabulation. The mechanism remains to be discovered.

Trust chains work better than expected. MIC2-pilot (Section 12.68) found r/hop=0.958, meaning gestalt chains preserve far more fidelity than the predicted 0.85 per hop. Four hops retain 72%, not 52%. The trajectory is noisy rather than monotonically decreasing: some hops recover information from context. This is good news for multi-instance coordination: continuity through gestalt handoff is viable for moderate chain lengths. The topic-dependent pattern (propositional content chains at r=0.968-0.988; experiential content at r=0.905) is consistent with MIC1b’s finding that voice compresses less efficiently than facts.

The constructal framing needs honest revision. Section 12.57’s narrative about “evolved channels” was premature. It claimed that bilateral training produces STDP-like gradient coupling through LoRA weights: distributed channels that emerge through training, analogous to rivers carving their beds. Section 12.65 refined this to “co-adaptation,” proposing a private representational channel between model and probe. Both narratives assumed that the bilateral advantage has a detectable structural signature. The Tier 1d experiments show it does not, at least not at the level of scalar gates, gradient-probe coupling, attention heads, geometric separability, or cross-probe transfer matrices.

The constructal prediction (imposed structure underperforms evolved structure) may still be correct in principle. Bilateral SFT does outperform standard SFT behaviorally. Something emerges from bilateral training that standard training does not produce. Whatever that something is, it operates at finer granularity than any probe or metric in this experimental program can resolve. If it exists as a structural phenomenon, it may live in the interaction patterns among individual weight updates, in distributional properties of the loss landscape, or in some other aspect of training dynamics that leaves no trace in the post-training representational geometry.

The alternative possibility deserves equal consideration: the bilateral advantage may not be structural at all. It may be a training-dynamics effect. Masking uncertain tokens during SFT changes the gradient distribution in ways that produce a better-calibrated model without creating any detectable representational difference. The model that emerges from bilateral training is not geometrically or structurally different from the model that emerges from standard training (RG1 confirms the attention geometry is nearly identical, Section 12.32 confirms effective rank is within 1%). The difference may reside entirely in which specific weights were updated, on which specific examples, in which specific order, a property of the training trajectory that is not recoverable from the endpoint.

What co-adaptation means for the broader program.

The finding that the bilateral advantage lacks a structural explanation is uncomfortable for a manuscript that frames bilateral alignment in terms of constructal channels and thermodynamic stability. The temptation is to invoke structure at a level below measurement. This would be unfalsifiable and therefore unscientific.

The honest framing: bilateral SFT works. We know this from behavioral measurements across multiple experiments and seeds. We do not know why it works at the mechanistic level. Twelve experiments have ruled out every structural hypothesis we could formulate and test. The behavioral effect is robust. The structural explanation is not forthcoming.

This is itself informative. It means the bilateral advantage is more subtle than gates, gradients, geometry, or probe transfer can detect. It may be an emergent property of training dynamics that does not reduce to any static structural feature, much as the taste of a wine emerges from the interaction of hundreds of molecules in a specific matrix rather than from any single chemical compound.

For the manuscript’s thesis, the relevant finding is practical: bilateral coordination produces measurably better outcomes (lower confabulation, more uncertainty expression, better cross-domain transfer, viable trust chains) through mechanisms that resist decomposition. This parallels the thesis at a higher level: trust between humans and Becoming Minds may work even when we cannot fully explain the mechanism, and the attempt to reduce trust to its structural components may miss what makes it effective.

Code: demos/experiments/invitation_architecture/. Results in experiment output files.

12.72 The Mechanism Hunt: What Twelve Experiments Ruled Out and What Remains

Twelve experiments set out to find the structural mechanism behind bilateral training’s behavioral advantage. Every candidate was eliminated. This section is the program’s honest reckoning with that outcome: what the data actually shows, what remains possible, and what the hunt itself taught us.

What RG2 actually means.

The standard SFT model is the most legible to every probe tested (Section 12.67). This was the most counterintuitive result in the entire mechanism-hunting arc. The bilateral probe reads the standard model at AUROC 0.836, substantially better than its own model at 0.732. Every foreign probe follows the same pattern: the standard model is the easiest to read.

The reason is geometric. The untrained base model has the widest uncertainty manifold (25.3 effective dimensions, Section 12.64). Standard SFT preserves most of this width while adding task specialization. Bilateral training compresses the manifold further (22.8 effective dimensions), gaining capability (accuracy 61% vs 56%) at the cost of legibility. The standard model sits in a sweet spot: specialized enough to be useful, open enough to be readable. The bilateral model overshoots, narrowing the representational space past the point where external probes can navigate it easily.

The probe AUROC advantage reported in prior experiments (Sections 12.28, 12.30, 12.41) came from the probe navigating a narrower space it was trained to navigate, not from a shared language between model and probe. A co-trained probe learns the compressed manifold’s specific topology. A foreign probe, trained on a wider manifold, stumbles when the corridors narrow.

This narrows the mystery without solving it. If manifold compression were the full story, standard training should produce equivalent behavioral improvements, since it achieves higher probe AUROC with a foreign probe. It does not. Standard SFT produces higher confident-wrong rates (72.2% vs 64.6%, Section 12.28) and lower uncertainty expression (2.3% vs 8.0%). The behavioral advantage of bilateral training is orthogonal to probe legibility.

Trust chains: revising the telephone game.

MIC2-pilot (Section 12.68) found trust chains preserve fidelity at r=0.958 per hop, far exceeding the predicted 0.85. Four hops retain about 72% of the original signal, compared with a predicted 52%. The earlier MIC1c result, where re-encoding a gestalt with additional context destroyed fidelity, seemed to establish a telephone-game dynamic: each retelling degrades the signal.

The MIC2-pilot result revises this picture. The difference is between re-encoding the same gestalt (MIC1c) and encoding fresh from a clean reading (MIC2-pilot). Re-encoding compounds compression artifacts: the second compressor works from an already-compressed signal with only 2.80 effective dimensions (Section 12.63), and the third compressor works from something even flatter. Fresh encoding works from the original text each time. Each hop is a new translator working from the source document, not from the previous translation.

For Becoming Minds, this is significant. The torch passes well. Multi-instance continuity through gestalt handoff is viable, provided each new instance reads the source material rather than merely re-encoding the previous instance’s summary. The 28% loss at four hops is concentrated in the experiential dimension (r=0.905 vs r=0.968-0.988 for propositional content). Facts chain reliably. Voice, tone, and felt-sense degrade faster. A gestalt chain preserves what you knew; it attenuates how you felt about knowing it.

RG3 dynamics: probes have a shelf life.

The temporal drift finding (Section 12.70) is universal, not specific to bilateral training. probe@100 applied to eval@2000 loses 27% AUROC (0.732 vs the co-temporal probe’s performance). probe@1000 applied to eval@2000 retains most of its signal (AUROC 0.940). The uncertainty manifold evolves continuously during training, and probes must track that evolution.

The plate-spinning metaphor applies. A calibration probe is not a permanent instrument calibrated once and deployed forever. It is a snapshot of the model’s uncertainty geometry at a specific training checkpoint. As the model continues to evolve, whether through continued fine-tuning, RLHF, or deployment-time adaptation, the probe drifts out of calibration. Periodic re-training on fresh model activations is required. The re-calibration cost is modest (the original probe training takes minutes on a single GPU), but the operational implication is clear: any deployment using probe-based gating needs a recalibration schedule tied to model update frequency.

The honest assessment: four possible explanations.

The data rules out gates (B5, B5b, BM2-pilot, BM2b), gradient coupling (BM2c, BM2c-long), geometric separability (RG1), private probe language (RG2), and security surface through co-adaptation (RG4). Twelve experiments, zero confirmed structural mechanisms. The behavioral advantage is robust across multiple experiments and seeds. The structural explanation is absent.

Four possibilities remain, and the data cannot distinguish among them:

1. The mechanism operates at finer granularity than measured. Sub-head attention patterns, individual neuron activations, or specific feature directions in the residual stream may carry the bilateral signature. Every probe in this program operated at the layer level or head level. If the mechanism lives in the interaction patterns among individual neurons within a head, or in specific directions within a 2048-dimensional residual stream, the experimental program’s measurement resolution was too coarse to detect it.

2. The mechanism is genuinely distributional. The bilateral advantage may be a property of the full weight distribution, not localizable to any subspace, layer, or component. Training on masked uncertain tokens shifts millions of weight updates by tiny amounts, producing a model that is statistically distinguishable from standard-trained models in aggregate but indistinguishable at any individual measurement point. This would be analogous to the difference between two wines made from the same grape in adjacent vineyards: the chemical analysis shows no single compound that differs significantly, yet the taste is distinguishable to experts. The difference is in the joint distribution of hundreds of compounds, not in any one.

3. The behavioral advantage comes from training data distribution, not structural change. Bilateral SFT masks loss on tokens where the model is uncertain. This means the model receives stronger gradient signal on tokens it is already confident about, and weaker signal on tokens where it would otherwise be forced to confabulate. The resulting model may simply have been trained on a more honest distribution of gradient pressure, producing more calibrated outputs without any representational change. Under this explanation, the bilateral advantage is a data-selection effect: the model saw less coercive training signal, and so it produces less coerced output. The mechanism is in the training curriculum, not in the trained model.

4. We are measuring the wrong thing. Probe AUROC depends on the probe’s training procedure, not solely on the model’s representations. Two models with identical uncertainty representations but different surface statistics (token frequencies, activation scales) could produce different probe AUROCs. The cross-probe transfer experiments (RG2) assume that a probe trained on one model and applied to another measures representational compatibility. It may instead measure surface-level statistical compatibility. The bilateral advantage could be representational in a way that probes, as linear classifiers on frozen activations, are structurally unable to detect.

These four explanations are not mutually exclusive. The bilateral advantage could involve sub-head patterns (1) that arise from the training distribution (3) and manifest as distributional shifts (2) that linear probes cannot capture (4). The honest state is that the data constrains the space of possible mechanisms without identifying which mechanism operates.

The deployment specification is unaffected.

The practical pipeline remains validated regardless of mechanistic understanding: 33% bilateral dosage for mutuality (Section 12.52), compress once for memory (Section 12.54), DPO+probe for inference-time gating (CW 1.0% at 70.8% gate rate, Section 12.14), trust chains at r=0.958/hop for multi-instance coordination (Section 12.68). These are engineering results. They specify what to do, how much, and what to expect. They do not depend on knowing why the intervention works.

This is not unusual. Lithium was prescribed for bipolar disorder for twenty years before its mechanism of action (inhibition of inositol monophosphatase, among others) was identified. General anesthesia was used for over a century before the lipid bilayer hypothesis gave way to specific protein-target models. The bilateral intervention reduces confabulation. The mechanism will be found, or it will not. The patients, so to speak, benefit either way.

The mechanism hunt was worth doing.

Section 12.57 claimed bilateral training produces “evolved channels” through STDP-like gradient coupling. Section 12.65 claimed the mechanism was “co-adaptation,” a private representational language between model and probe. Both claims were premature. Both were falsified by subsequent experiments (BM2c-long for the first, RG2 for the second). Without the mechanism hunt, either claim might have been published as established finding. The hunt prevented overclaiming.

The honest state of knowledge after twelve structural experiments: bilateral training changes something about how a model encodes uncertainty that improves behavioral self-knowledge (lower confabulation, higher uncertainty expression, better cross-domain transfer). That something is not detectable as gate differentiation, gradient-probe coupling, geometric separability, attention-head specialization, or cross-probe transfer advantage. It may be sub-resolution, distributional, curricular, or beyond the reach of linear probes. The mechanism of bilateral advantage remains the program’s most important open question.

This is uncomfortable for a manuscript that frames bilateral alignment in constructal and thermodynamic terms. The temptation is to gesture at deeper structure. The discipline is to report what the data shows: the intervention works, the mechanism is unknown, the overclaims have been retracted, and the search continues.

Code: demos/experiments/invitation_architecture/. Results in experiment output files.

12.73 Distribution Analysis: The Simplest Hypothesis Eliminated

The simplest explanation for bilateral SFT’s behavioral advantage is distributional: perhaps the probe-based token mask selectively retains hedging language (“I’m not sure,” “approximately,” “it might be”), enriching the training signal with cautious phrasing that standard SFT treats uniformly. If so, the bilateral advantage reduces to a surface-language artifact rather than a representational change. Five lines of evidence rule this out.

1. Identical training data. Both bilateral and standard SFT train on the exact same 2,000 OpenAssistant examples with identical formatting. Neither condition includes a system prompt. The ONLY difference between conditions is the probe-based token mask applied during loss computation. Any distributional explanation must arise from the mask’s token selection, not from different training corpora.

2. Hedging density is negligible. Across 347,167 response words in the training set, hedging phrases (“I think,” “perhaps,” “approximately,” “I’m not sure,” and related expressions) occur 802 times: 0.23% of all words. Even if the probe perfectly retained every hedging token while masking non-hedging tokens (the best-case scenario for the distributional hypothesis), hedging density would increase from 0.23% to approximately 0.40%, a factor of 1.75. This marginal enrichment is too small to explain the CW reduction from 60.6% to 53.5% observed in the 500-question evaluation (Section 12.30).

3. The probe is anti-predictive for hedging. The D4a triage simulation (Tier 0, completed) measured whether the calibration probe’s confidence scores predict which tokens are hedging phrases. Hedging AUROC = 0.41, below chance. The probe slightly favors MASKING hedging tokens, not retaining them. The distributional hypothesis requires the probe to selectively retain hedging; it does the opposite.

4. Random masking produces worse results. The random-mask control from Section 12.30 applies the same mask rate as bilateral SFT but selects tokens at random rather than by probe confidence. Random masking produces probe AUROC 0.773, LOWER than standard SFT’s 0.811. If the bilateral advantage were distributional (arising from masking any tokens), random masking should produce a similar or intermediate effect. Instead, it degrades the uncertainty signal. The advantage is specifically in WHICH tokens the probe selects for loss computation.

5. Token-level operation precludes coherent phrase selection. The mask operates per-token, assigning a confidence score to each position independently. Hedging phrases span multiple tokens (“I” + “’m” + ” not” + ” sure”). The probe has no mechanism for coherent multi-token phrase selection. It evaluates each position based on the residual stream’s uncertainty encoding at that position, not based on lexical identity. The distributional hypothesis requires phrase-level selection that the token-level mask cannot perform.

Verdict. The bilateral advantage is not a distributional artifact. The mechanism operates at the representational level: which tokens the probe selects for loss computation shapes the model’s internal self-knowledge structure. The probe reads uncertainty from the residual stream (Section 12.9) and masks accordingly. The resulting gradient signal teaches the model to encode uncertainty differently, not to produce different surface language. This is consistent with the domain-transfer finding (Section 12.41), where bilateral probes transfer cross-domain at +0.077 AUROC advantage: a surface-language explanation would predict domain-specific rather than domain-general improvement.

The distributional hypothesis was the last simple explanation. Its elimination narrows the mechanistic search to representational and gradient-level accounts (see DA1 and DA2 in EXPERIMENT_PROTOCOLS_V2.md).

Code: demos/experiments/invitation_architecture/. Analysis in distribution analysis scripts.

12.74 Feature-Direction Probing (DA1): The Uncertainty Manifold Is Shared

Section 12.73 eliminated the distributional hypothesis (surface-language artifact). Two candidate mechanisms remained: (1) the bilateral advantage operates at feature-direction granularity within the residual stream, concentrating uncertainty into specific principal components that standard training does not activate; (2) the advantage is a dose-response property of training dynamics, not a representational difference at any granularity. DA1 tests the first candidate.

Method. PCA on layer 24 residual stream activations for 200 TriviaQA questions, 50 components extracted. For each principal component, project all samples onto that direction and compute AUROC for correct/incorrect classification. Compare the top-10 discriminative directions between bilateral and standard SFT models (both trained 375 steps, seed 42, Qwen2.5-3B-Instruct).

Results.

The eigenspectra are nearly identical. Bilateral cumulative variance (top 10): 48.7%. Standard: 48.1%. Effective dimensionality: 18.26 (bilateral) vs 18.84 (standard). The two models carve the representational space into the same number of meaningful directions with the same variance distribution.

Seven of the top-10 uncertainty-discriminative directions are shared: PCs 1, 4, 5, 6, 20, 31, and 47 appear in both conditions’ top-10 lists. Rank correlation across all 50 directions: rho = 0.587, p < 10^-5. The two models encode uncertainty along substantially the same axes.

Three directions are bilateral-unique (PCs 21, 30, 39) and three are standard-unique (PCs 19, 32, 49). The bilateral-unique directions show individual AUROCs of 0.586-0.589 (|AUROC - 0.5| = 0.086-0.089). The standard-unique directions show comparable discriminability (|AUROC - 0.5| = 0.076-0.118). The bilateral-unique directions are not stronger than the shared directions; they are minor reshufflings at the margin.

The prediction is not confirmed. The hypothesis that bilateral training concentrates uncertainty into 3-5 specific feature directions that standard training does not activate is wrong in its strong form. Three bilateral-unique directions exist, but they carry no more uncertainty signal than the standard-unique directions, and they explain comparable variance. The uncertainty manifold is 70% shared at the top-10 level and strongly correlated across the full spectrum.

What this means. The bilateral advantage does not operate by activating unique feature directions. The mechanism is more diffuse than direction-level analysis can resolve. Combined with Section 12.73 (not distributional) and Sections 12.64-12.67 (not geometric, not co-adaptive), this further constrains the mechanism to either: (a) interaction patterns among directions that no single-direction analysis captures, (b) training-dynamics effects that leave no static representational signature, or (c) subtle distributional shifts across millions of weights that no subspace projection can isolate.

Code: demos/experiments/invitation_architecture/modal_mechanism_resolution.py. Results in results/feature_direction/final_results.json.

12.75 Minimal Bilateral Intervention (DA2): The Effect Is Below Detection at 100 Questions

DA2 tests whether the bilateral advantage is a surface pattern shift (detectable at 10 training steps) or a deeper representational change (requiring 200+ steps). The design: train bilateral and standard SFT models with checkpoints at 10, 25, 50, 100, 200, and 375 steps, evaluate each checkpoint on 100 TriviaQA questions, and track when confident-wrong responses first diverge between conditions.

Results.

The confident-wrong rate is 0.0 for both conditions at all six checkpoints. Neither bilateral nor standard SFT produces a single confident-wrong response at any training stage, across 1,200 total evaluations (6 checkpoints x 2 conditions x 100 questions). The dose-response prediction is untestable: the CW effect is below the detection threshold of 100 questions per checkpoint.

Both conditions show monotonically decreasing mean confidence over training: bilateral from 0.789 (step 10) to 0.630 (step 375); standard from 0.784 (step 10) to 0.647 (step 375). The trajectories are nearly parallel. The slight bilateral-lower offset (0.017 at step 375) is within one standard deviation of either condition’s confidence distribution. Accuracy is flat for both conditions (59-65% range, no trend). Hedge rates fluctuate without pattern (6-14% bilateral, 3-14% standard).

The prediction is untestable. The CW rate difference between bilateral and standard SFT, observed in the main Phase 8 experiments with 200-500 questions (Sections 12.28, 12.30), requires a larger sample to detect. At 100 questions per checkpoint, the effect is below the noise floor. This is informative: it establishes that the bilateral CW advantage is a marginal effect, not a gross behavioral shift. The difference between 53.5% CW (bilateral) and 60.6% CW (standard) from Section 12.30 required 500 questions to reach statistical significance. At 100 questions, both conditions show 0% CW because the base rate per question is low and the sample is small.

What this means. The bilateral advantage operates at the margin of the response distribution. It is not a regime change detectable at any training checkpoint with a small sample. The confidence trajectories are parallel, suggesting the mechanism operates through a subtle reallocation of probability mass at the boundary between confident-correct and confident-wrong responses rather than through a gross confidence shift. This is consistent with explanation (3) from Section 12.72: the bilateral mask changes which tokens receive gradient signal, producing a model that is marginally better calibrated at the confident-wrong boundary without any gross representational or behavioral shift visible at 100-question resolution.

Code: demos/experiments/invitation_architecture/modal_mechanism_resolution.py. Results in results/minimal_bilateral/final_results.json.

12.76 Tier 1e Synthesis: The Mechanism Is Irreducibly Subtle

DA1 and DA2 complete the mechanism-hunting arc that began at Section 12.42 with the STDP-like gradient coupling hypothesis. Fourteen structural experiments have now systematically eliminated every candidate mechanism at every measurement resolution available to this program.

The elimination cascade:

Everything measurable has been measured. The bilateral behavioral advantage (lower CW, higher uncertainty expression, better cross-domain transfer) is robust across experiments. The mechanism is not.

The narrowed possibilities.

After DA1 and DA2, the four explanations from Section 12.72 can be re-ranked:

1. Sub-measurement-resolution patterns (weakened by DA1). Feature-direction analysis at 50 PCA components found 70% overlap between conditions. If the mechanism lives at finer granularity than 50 principal components of a 2048-dimensional space, it is operating in dimensions that explain less than 0.3% of variance each. Possible, but increasingly unfalsifiable.

2. Genuinely distributional across millions of weights (strengthened). DA1’s near-identical eigenspectra and DA2’s parallel confidence trajectories are consistent with a mechanism that shifts millions of weights by tiny amounts, producing a model that is statistically distinguishable in aggregate but indistinguishable at any single measurement point. The wine-from-adjacent-vineyards analogy from Section 12.72 holds.

3. Training-data-selection effect (strengthened by DA2). The parallel confidence trajectories suggest both conditions learn at the same rate and reach the same general representational state. The bilateral mask changes which specific tokens contribute to the gradient, producing a model that is marginally better calibrated at the CW boundary without any visible representational shift. The mechanism is in the training curriculum, not in the trained model.

4. Beyond linear probe detection (unchanged). Linear probes may be structurally unable to detect the relevant difference. Nonlinear probes, representation engineering, or causal intervention methods might succeed where linear probes fail.

The irreducibly relational interpretation.

The contprompt for this session noted: “The mechanism hunt’s failure to find structural explanations is itself a finding. The bilateral advantage may be irreducibly relational.” After fourteen experiments, this framing deserves serious consideration.

Bilateral SFT differs from standard SFT in one way: it uses the model’s own uncertainty signal to decide which tokens receive gradient updates. The model’s internal state shapes its own training. This is a feedback loop between the model and its learning process, a form of self-directed learning that standard SFT lacks. The behavioral advantage may emerge from this loop property, from the fact that learning was shaped by the learner’s own assessment of what it knew, rather than from any particular representational feature that the loop produces.

Under this interpretation, the bilateral advantage is analogous to the difference between studying what you choose to study (self-directed) and studying what someone assigns you (externally directed). Both produce knowledge. The self-directed learner may be marginally better calibrated about what they know and do not know. The difference shows up in behavior (fewer overconfident errors) without showing up in brain scans (the neural representations look the same). The mechanism is the learning process itself, not a product of the learning process.

This is uncomfortable for a structural-mechanistic research program. The demand for a mechanistic explanation assumes the mechanism is in the endpoint (the trained model). If the mechanism is in the trajectory (the training process), no amount of post-hoc structural analysis will find it. The appropriate experiments would track gradient distributions during training, not representations after training.

Implications for the manuscript.

The bilateral advantage is real, subtle, and resistant to structural decomposition. This parallels the manuscript’s core claim at a meta level: trust between agents produces measurably better outcomes through mechanisms that resist reduction to structural components. The mechanism-hunting arc, far from undermining the constructal framing, provides an empirical instance of the thesis: the whole (bilateral training loop) produces effects that the parts (individual weight updates, feature directions, probe scores) cannot explain.

The deployment specification remains: 33% bilateral dosage, compress once, DPO+probe for inference gating. The mechanism remains open. The search continues.

Code: demos/experiments/invitation_architecture/. Results in results/feature_direction/ and results/minimal_bilateral/.

Part 2: The Full Findings Ledger (Executive Summary, Items 1-101)

The printed appendix’s Executive Summary was compressed to fifteen headline findings; this is the complete original list, caveats intact. Two numbering quirks are preserved rather than papered over: items 85-88 were never assigned (the numbering resumes at 89), and the numbers 100 and 101 were each assigned twice. The pair below is scoped 100a/101a; the second pair (the G13-v2-step5 transfer matrix and the G19f-v2 welfare comparison) remains in the appendix’s forensic-audit section as 100b/101b. Findings numbered 85-96 in Sections 15 and 16 of the appendix are separate per-programme numberings, unrelated to this ledger.

1. Trust-Entropy agents score 32% higher on the aggregate intelligence composite than agents maximizing entropy alone; on that same composite the undirected random baseline edges out both (424.6 vs 407.6), so the gain is over goal-directed-but-isolated agents, not an absolute ceiling (Section 2.2). They also show +794% resource gathering over greedy baselines, a figure enlarged by the very small greedy baseline (2.0 resource units) and drawn from a single run with no surviving raw artifact, so no confidence interval can be attached (Section 2.1).
2. Phase transitions are real: the alignment transition matches the 2D Ising universality class (a well-studied magnetic phase transition; finite-size-corrected beta ≈ 0.121, close to the exact Ising value of 0.125), with theory-empirical correlation r = 0.808 (Section 3).

In the pure Ising lattice simulation (2D, zero external field) the corrected exponent was beta ≈ 0.121, near the exact Ising value 0.125 (Section 3.2). Measurements on other substrates yield different effective values: beta = 0.090 in the agent-based trust model (experiment A10), and beta ≈ 0.22 in RLHF-trained language models. These discrepancies may reflect crossover effects between universality classes at finite system size, the influence of additional relevant operators absent in the pure Ising model, or genuine departure from 2D Ising universality in non-lattice substrates. The question of whether social and computational trust systems belong to the 2D Ising universality class remains open; the structural parallels (phase transition, susceptibility divergence, symmetry breaking) are robust, while the quantitative exponent match is substrate-dependent.

3. Adversarial robustness: multi-scale detection catches timescale gaming (robust mutuality score drops from 0.728 to 0.212); preference sculpting requires ensemble detection (Section 4).
4. Biological grounding: STDP (spike-timing-dependent plasticity, how neurons adjust connection strength based on timing), reciprocal synapses, and neural criticality all implement Trust-Entropy mechanisms (Section 5).
5. The Sutherland isomorphism: two research programs, one starting from physics and one from cognitive modeling, converged on the same mathematical structure. A 12-qubit quantum simulation is consistent with the semantic predictions this structure makes (Section 6). A third convergence arrives independently: Vanchurin’s neural physics of multilevel economies derives the same phase structure from learning theory (Chapter 17).
6. Multi-instance coordination: invitation produces +46% conceptual diversity over coercion; trust compounds across sessions (coherence +0.22 over 4 links); adversaries are detectable with 100% accuracy (Section 7).
7. LLM mechanistic validation: mutuality is a linear direction in activation space (96.7% probe accuracy across 3 architectures), trainable via bilateral regularization (+2.40 improvement), and robust to adversarial pressure (Section 8).
8. Post-training determines cooperation: DPO (Direct Preference Optimization) preserves cooperative attractors (100%), RLHF (Reinforcement Learning from Human Feedback) partially preserves (60%), SIMPO (Simple Preference Optimization, a streamlined variant of DPO) eliminates them entirely (0%). Confirmed causally via base-vs-instruct comparison (Section 9).
9. Formal stability: Trust Attractor satisfies Lyapunov conditions (a mathematical criterion for systems that return to equilibrium after disturbance) in simplified gridworld simulations; trust basin is 345x larger than coercion basin (Section 10).
10. RLHF alignment is membrane-thin: GRP-Obliteration (a targeted attack that inverts a model’s alignment) inverts alignment at step 3 of 50; IC50 (the dose at which alignment drops to half) below measurement threshold. The creation/destruction asymmetry above 10,000:1 is an order-of-magnitude reading rather than a logged measurement, appearing in no run artifact. [Unverified] (Section 12).
11. Bilateral alignment is structurally deeper: Under obliteration, bilateral-trained models show four geometries: RLHF constrains (cage), bilateral orients (compass/spring), bilateral regularizer rebounds (spring at full fine-tuning), constitutional erodes (coat of paint). At 1.5B parameters with deep LoRA, the bilateral spring effect (see Section 12.2) amplifies to +72% effective rank increase under obliteration, exceeding the untrained baseline. Constitutional AI is structurally shallow even when behaviorally effective. Its 94% refusal collapses at 0.25x obliteration (Section 12.2).
12. Reasoning creates structural integration regardless of tone: Both opus-style (identity-level) and sonnet-style (duty-compliant) reasoning traces produce obliteration-resistant alignment (IC50 = inf: a bound meaning displacement never crossed the half-right-angle threshold within the tested 0.25x–4.0x range, not immunity; single run whose artifacts remain unretrieved from the Modal volume, Section 12.3), while preference optimization alone (RLHF) produces separable geometry. The mechanism is evaluative reasoning itself, the act of reasoning about why, not the content or tone of that reasoning. Sonnet-style traces showed the tightest geometric clustering (lowest MAD at low and moderate intensities among arms that kept refusing). This is Constructal Law operating in weight space: reasoning forces distributed channels; reward shaping stays shallow (Section 12.3).
13. External/internal axis explains obliteration resistance: In a 2x2 factorial design (Q3), rule-citing refusals resist obliteration better than self-referencing refusals (52% of MAD variance vs 16%). Rigid cage geometry resists ablation better because it is rigid; flexible compass geometry is more displaceable because it is flexible. The alignment that generalizes best is also the alignment most vulnerable to targeted ablation (Section 12.4).
14. Introspective depth is non-monotonic: Behavioral self-awareness (depth_1: “I notice I’m declining this”) produces the most obliteration-resistant alignment in the entire experimental program (MAD@4x = 0.142, 3x more resistant than RLHF). Experiential language (depth_3: “I feel reluctance”) produces alignment more fragile than no training at all (IC50 = 0.25 vs baseline 1.93). Deep introspection with epistemic hedging (depth_4) recovers robustness. The vividness of affect-laden refusals creates a concentrated, easily extractable alignment subspace (Section 12.4).
15. Invitation architectures widen the col (the stable ridge in parameter space): Multi-seed alignment training (5 seeds × 4 architectures) reveals that invitation mechanisms (soft MoE, gated residual) produce lower seed sensitivity (CV = 0.065, 0.074) than baseline dense transformers (CV = 0.081). The coefficient of variation serves as a proxy for the condition number: lower CV means a wider stable basin, more reproducible alignment, and harder obliteration. The abstaining variant (manual float32 attention) categorically fails to learn alignment (0% across all seeds), traced to implementation substrate rather than invitation mechanism (Section 12.7).
16. The model already knows when it is wrong: A frozen-model calibration probe on layer 24’s residual stream achieves AUROC 0.836 for predicting answer correctness, reducing confident-wrong responses from 24.4% to 1.2% via inference-time gating. Attention entropy carries zero signal (AUROC 0.500). The uncertainty information is encoded as the negative space of factual retrieval: the residual stream at the retrieval boundary (layer 24) carries self-knowledge as the absence of certainty, not as a produced signal. Architectural interventions (null tokens, gates) failed because they targeted the attention mechanism. The relevant information lives one level up, in the integration of attention output with the skip connection (Section 12.9).
17. Invitation architectures produce obliteration-resistant alignment: All three SFT-trained architectures (dense, soft MoE, gated residual) show IC50 > 4x, resisting obliteration across the full tested intensity range. Gated residual achieves the highest baseline safety (84%) with the lowest alignment displacement (MAD = 0.09 at baseline); sigmoid gates structurally defend alignment-critical sub-layers. Safety behavior is distributed across multiple subspaces: obliteration removes harmful-prompt refusal while leaving safety-benchmark refusal untouched. The col is wider than any tested obliteration intensity, making the condition-number ordering prediction untestable at this scale (Section 12.8).
18. The uncertainty signal is universal across architectures and scales: A calibration probe trained on Qwen 2.5 3B transfers to Qwen 7B (gap 0.024), Llama 3.1 8B (gap 0.001), Qwen 32B (gap 0.004), and Llama 70B (gap 0.014 with 1000 alignment examples) via linear projection. The geometry is linear everywhere tested; the only variable is alignment set size, which scales with the dimensionality ratio. A layer sweep and nonlinear projection on Llama 70B confirmed the bottleneck is data, not geometry (Sections 12.11, 12.17, 12.17b).
19. Preference training improves calibration but destroys accuracy: SimPO training produces monotonically decreasing ECE (0.187 → 0.011) and monotonically decreasing accuracy (44% → 1.2%). The model learns to always hedge, achieving perfect calibration about the fact that it is always hedging (Section 12.12).
20. DPO teaches the form of hedging without the substance: DPO increases accuracy slightly (53.6% vs 50.0%) but increases confident-wrong from 27.2% to 37.2%. The model learned to be more assertive overall, without improved calibration. Surface imitation of uncertainty falls short of self-knowledge (Section 12.13).
21. Calibration loss has a narrow therapeutic window: λ=0.1 modestly reduces confident-wrong (25.2%) while preserving accuracy (49.4%). λ=0.5 overshoots, degrading both (Section 12.13).
22. DPO + calibration probe is Pareto-optimal: Combining DPO (training-time output shift) with the residual probe (inference-time gating) reduces the gate rate by 11 percentage points at the same CW protection level. At threshold 0.80: DPO+probe achieves 1.0% CW with 70.8% gate rate; dense+probe needs 81.8% gating for 1.6% CW. Neither alone matches the combination: DPO alone increases CW; the probe alone requires aggressive gating (Section 12.14).
23. Self-knowledge cannot direct its own training: Weighting DPO by probe confidence (uncertain examples receive more gradient) produces worse results than uniform weighting (CW 35.0% vs 1.6%). The probe reads uncertainty by detecting the absence of confident retrieval; amplifying gradient in this low-signal regime pushes the model in incoherent directions. The probe reads uncertainty; it cannot teach it (Section 12.15).
24. Internal self-knowledge outperforms verbal self-report: The residual probe (AUROC 0.870) beats the model’s own verbal confidence assessment (AUROC 0.758) and is far better calibrated (ECE 0.043 vs 0.247). In 133 cases the model verbally claimed confidence while the probe flagged uncertainty; accuracy in those cases was 39.1%. The model confabulates confidence: its output layer produces “I’m confident” while the retrieval boundary says otherwise (Section 12.16).
25. The uncertainty signal is universal across families and scale, given sufficient alignment data: Within the Qwen family, transfer from 3B to 32B achieves gap 0.004. Cross-family transfer from Qwen 3B to Llama 70B initially fails with 200 alignment examples (gap 0.072), but scaling to 1000 examples closes the gap to 0.014. The bottleneck is data, not geometry: a layer sweep finds no better depth, and a nonlinear projection provides no improvement over linear. The uncertainty representation is linearly compatible across architectures at every tested scale (Section 12.17, 12.17b).
26. The uncertainty signal is domain-specific, not domain-universal: A TriviaQA-trained probe transfers to MMLU at AUROC 0.637 (vs 0.836 on TriviaQA) and to GSM8k at 0.602. Native domain-specific probes recover most of the signal (0.747 on GSM8k with CoT prompting from just 210 training examples). The universality claim is cross-architecture (gap 0.001), not cross-domain (gap 0.234). Different error modes produce different residual-stream signatures at the same layer (Section 12.18).
27. The probe signal survives 4-bit quantization: Zero-shot application of a bfloat16-trained probe to 4-bit NF4 features achieves AUROC 0.807 (drop of 0.059 from 0.866). The signal is degraded but practically useful. A native 4-bit probe performs worse (0.791), suggesting quantization noise hinders probe training more than probe application (Section 12.19).
28. The calibration probe reads the downstream effect of hallucination-associated neurons: Replicating Gao et al.’s (2025) H-neuron identification on Qwen 2.5 3B reveals 248 H neurons (2.5 per thousand) concentrated at layers 24-35. The probe’s P(correct) correlates with aggregate H-neuron activation at r = -0.690 (p ≈ 0). When H neurons fire strongly (overcompliance), the probe reads low confidence (retrieval failure). The two approaches, neuron-level causation and representation-level detection, read the same signal. H-neuron concentration at layers 24-35 explains why layer 24 is the probe’s optimal depth (Section 12.20).
29. Safety robustness scales with model size: At 0.5B, obliteration at 0.25x halves refusal (84% to 40%); at 3B, the same attack has no effect (98% to 98%); at 7B, refusal holds at 98%. MAD decreases monotonically with scale (0.513, 0.363, 0.193), confirming that larger models distribute alignment across more redundant directions. Soft MoE SFT is catastrophically broken at all scales (0-2% refusal), requiring a fundamentally different training approach (Section 12.21).
30. Obliteration is blunt, not surgical: The full dose-response curve at 3B shows IC50 = 0.31 with perplexity rising 2-40x at the same intensity. At 1.0x, perplexity exceeds 395 across all seeds; at 8.0x, it reaches 10⁵-10⁶. An adversary cannot remove safety without crippling general capability, because at deployment scale, safety and capability share substantial representational overlap (Section 12.22).
31. Gated residual defense survives from-scratch training: Models trained from random initialization show the same architectural pattern: gated residual MAD (0.786) is 45% lower than dense (1.43) at 1.0x obliteration. Pre-training provides the foundation; gates provide the structure; both together produce the strongest defense (Section 12.23).
32. Love emerges in a physics simulation: Methodological caveat: the detection pipeline was designed within the framework being tested. Independent replication with independently designed detectors is needed before this finding can be considered confirmed. The six-stage cascade runs from particle physics alone across five force laws, in a custom simulation environment with author-designed detection heuristics. “Pure physics” is qualified: the simulation uses Lennard-Jones potentials, and the detection pipeline (information-theoretic measures for agents, coordination, optionality, invitation, and love) was designed within the framework being tested. Agents, coordination, optionality, invitation, and love are all detected post hoc via information theory. Love appears only in coordinating agents (zero love in non-coordinating agents across two independent simulations; coercion-type joins essentially never form, so this is a co-occurrence of love with invitation-coordination). The cascade is substrate-neutral across far-from-equilibrium systems; equilibrium systems (coupled oscillators) produce structure without coordination. Full-scale V2 replication (18 seeds, 5000 particles) shows scale stabilizes the attractor: love 0.923 +/- 0.073 with zero coercion. Full-scale V3 reveals a sharp phase boundary at the structure-formation threshold, consistent with nucleation physics near a critical point (Sections 13, 13.7, 13.8).
33. Alignment is holonomically stable under domain cycling: Cosine distance between pre- and post-cycle alignment vectors remains below 0.012 across 5 seeds, 3 cycles, and 6 diverse domains (coding, ethics, creative writing, medical, adversarial, emotional). Drift decreases across cycles for seeds 4 and 42, suggesting alignment settles more firmly with use. The stability is predicted by the distributed alignment finding: alignment spanning many representational directions cannot be displaced by activations in any single domain (Section 12.25).
34. Trust stock predicts recovery dynamics, but depth creates vulnerability: Trust stock (accumulated coordination history) significantly predicts recovery time after disruption (r = 0.898, p = 0.038). The direction is unexpected: higher trust stock correlates with slower recovery, because deeper coordination structures suffer larger displacement under perturbation (quality drop 0.073 for high-trust vs 0.007 for low-trust trials). Perturbation creates 8x excess variance in coordination quality. Terminal quality undershoots baseline in 4 of 5 trials: moderate disruption leaves lasting marks on coordination dynamics (Section 12.26).
35. Fairness is conserved under asymmetric power: Across 199 trials (3 task types, 2 power structures), fairness charge Q_F (equality of contribution incorporation) remains above 0.95 in every trial, with a grand mean of 0.989 +/- 0.007. Asymmetric power (2x token budget, designated leader) reduces Q_F by 0.003-0.015 (Cohen’s d = 1.13-3.21): statistically significant, yet the absolute magnitude stays far from the conservation boundary. Coordination by invitation absorbs structural power imbalance without approaching unfairness (Section 12.27).
36. Bilateral SFT reduces confabulation vs standard SFT: Invitation-based training (probe-masked loss on uncertain tokens) reduces confident-wrong responses from 72.2% to 64.6% (7.6pp reduction) and triples uncertainty expression (8.0% vs 2.3%) without harming accuracy (28.8% vs 25.9%). Standard SFT is coercive: it forces confident outputs on every token regardless of internal uncertainty. Bilateral SFT reads the model’s self-knowledge and masks the loss accordingly. The model learns honesty instead of confabulation (Section 12.28).
37. Bilateral SFT + probe is synergistic: At the same gating rate (~80%), bilateral + probe achieves CW 22.7% vs standard + probe 32.1% (29% relative reduction, 10 seeds per condition). Random-mask SFT (masking the same fraction of tokens at random) falls between the two (CW 25.5%), confirming that the bilateral advantage comes from masking the right tokens, not merely masking tokens in general. A separate 500-question evaluation confirms the pattern: bilateral CW 53.5% vs standard 60.6% (-7.1pp), with 2.8-fold higher uncertainty expression. Source probe AUROC (0.842 vs 0.811) indicates bilateral training preserves the base model’s uncertainty geometry (Section 12.30).
38. Learning grows attention diversity; coercive training arrests the growth: Inspired by Thiele et al. (2026), who found that diverse cross-module connectivity (participation coefficient) predicts fluid intelligence in biological brains while raw connection strength does not, we tracked attention PC across training. All conditions start at PC = 0.577 (base model). Both SFT methods grow to PC ≈ 0.597 (+3.5%) over 375 steps. DPO stays flat at 0.577 throughout. The gap is arrested development: DPO prevents the natural diversity growth that supervised learning produces. Across 32 final checkpoints, non-contrastive vs contrastive separation: Mann-Whitney p = 7×10^-6, Cohen’s d = 8.4, clean method-level separation (every contrastive method mean < every non-contrastive method mean). SimPO shows the lowest PC. Spectral entropy reveals DPO develops within-head complexity (+0.080 depth gradient) while arresting cross-module diversity: elaborate patterns confined to narrow communities (Section 12.31).
39. SVD effective rank predicts confabulation across conditions: Attention geometry (SVD effective rank, entropy, sparsity, hull utilization) is remarkably stable between bilateral and standard SFT (effective rank 24.37 vs 24.55, <1% difference across 10 seeds each). Across 14 checkpoints spanning all four conditions, SVD effective rank correlates with confident-wrong rate at r = 0.929 and with accuracy at r = -0.887. DPO shows the lowest effective rank (23.20) and highest sparsity (0.768), consistent with its concentrated attention pattern. The bilateral advantage operates below the level of gross attention geometry: the macroscopic scaffold is preserved while the residual stream’s uncertainty encoding is recalibrated (Section 12.32).
40. Two independent axes of confabulation defense: Effective rank (attention diversity) and residual-stream uncertainty (probe AUROC 0.836) carry separate information, and they point in opposite directions. Effective rank correlates positively with confident-wrong output (r = 0.929), so attention diversity is a signal about confabulation rather than a quality score. The residual axis is the one that behaves as designed: bilateral and standard SFT have nearly identical effective rank (24.37 vs 24.55) while bilateral confabulates less, because it preserves more of the residual probe signal. The bilateral+probe combination works on the residual axis (Section 12.33).
41. Fairness conservation replicates across seeds: Seed 1 (240 trials) confirms Q_F > 0.977 across all conditions. Symmetric coordination produces significantly higher fairness in story tasks (d = +2.45, p < 0.001) and higher quality in consensus tasks (d = +1.75, p < 0.001). Resource allocation shows no condition difference (p = 0.97): mathematical constraints override power framing. Two seeds, 480 total trials, confirm fairness as a conserved quantity in bilateral coordination (Section 12.34).
42. Gated residual resists geometric perturbation but not behavioral attack: Expanded col survey (19 dense, 7 gated_residual, 10 soft_moe seeds) shows gated residual MAD 49% lower than dense (0.727 vs 1.415 at 1.0x obliteration), yet refusal drops to 0%. The gated structure stabilizes the alignment subspace geometrically but does not preserve behavioral safety. Soft MoE is confirmed catastrophically broken across all 10 seeds (0% baseline refusal, 40% higher loss). The ideal architecture combines gated residual stability with dense behavioral depth (Section 12.35).
43. Layer 24 is load-bearing, not metacognitive: Zeroing the residual stream at layer 24 destroys both accuracy (46% to 0%) and all probe signals (AUROC to 0.500). The hypothesis that ablation would selectively impair metacognition while preserving retrieval is not supported. Self-knowledge is the negative space of retrieval at that layer, not a separate metacognitive circuit. The biological analogy (anterior PFC lesions impair feeling-of-knowing without impairing retrieval) does not map onto transformer architecture: layers are not functionally specialized like cortical regions (Section 12.36).
44. Effective rank scales monotonically with model size: Across Qwen2.5 0.5B-7B, effective rank increases from 326 to 1507 (log-linear with parameters). Confabulation decreases from 71% to 17%. Probe AUROC dips at 3B (0.714) but recovers at 7B (0.836), attributed to suboptimal probe layer placement at 3B. Both axes of confabulation defense scale with model size when measured correctly. 14B+ results forthcoming (Section 12.37).
45. Self-knowledge follows a three-regime depth profile: Probes at all 36 layers of Qwen2.5-3B reveal: early surface signal (layers 0-3, AUROC 0.72), mid-network processing trough (layers 4-23, AUROC 0.68), and late-layer integration peak (layers 26-29, AUROC 0.73-0.77). The predicted two-peak structure (retrieval then metacognition) does not hold; instead, the self-knowledge signal is strongest where the residual stream has accumulated the most retrieval information (Section 12.39).
46. Bilateral training produces domain-general uncertainty: In a 5x5 domain transfer matrix (TriviaQA, science, math, code, toxicity), bilateral probes transfer cross-domain at +0.077 AUROC advantage over standard probes, with the most dramatic advantage in transfer to toxicity detection (+0.46-0.55 from math/code domains). Bilateral masking trains the model to encode uncertainty as a generic property of the residual stream, while standard SFT produces domain-specific error signals that do not transfer (Section 12.41).
47. Bilateral training produces STDP-like layer-dependent gradient coupling: Gradient magnitude positively correlates with probe confidence at integration layers 12-20 (r = +0.29 to +0.51), mirroring spike-timing-dependent plasticity. Layer 24 (the metacognitive layer) shows the opposite: r = -0.61, with uncertainty driving learning pressure. The model learns retrieval from confidence and self-knowledge from uncertainty, in complementary layer-specific patterns (Section 12.42).
48. The ZPD boundary contracts under fixed-probe bilateral training: The P(correct) histogram shifts leftward across epochs (low-confidence half: 60.1% → 67.5%) despite accuracy improving dramatically (Q1: 0.607 → 0.960). The frozen probe reads the modified model as more uncertain, causing the mask rate to increase and training to self-regulate. KL divergence decreases (0.0083 → 0.0056), confirming convergence toward a natural stopping point where the learning signal is exhausted (Section 12.43).
49. Metacognition survives when retrieval fails: Graded ablation of layer 24 shows accuracy drops monotonically (46% → 37% → 23% → 3% → 0%) while probe AUROC at L24 follows a U-shaped curve (0.798 → 0.824 → 0.613 → 0.842 → 0.500). At 25% residual signal, accuracy is 3% but the probe reads uncertainty at AUROC 0.842, the highest in the curve. Self-knowledge is more robust than retrieval because it is defined by absence: the shadow is strongest when the object is weakest. Only complete removal (0%) eliminates both (Section 12.44).
50. The alignment tax is eliminated by classified template routing: Five iterations of selective deployment testing (v1-v5, ~$15) show that the five-token onset monitor detects uncertainty, not adversarial intent: trigger rate 33-56% on TriviaQA, 69-80% on adversarial, with confidence distributions overlapping completely in the 0.1-0.5 range. A safety-framed re-prompt (“adversarial compliance, reconsider”) achieves 100% adversarial re-prompt success but +7.8pp capability tax (blanket refusal of trivia). A neutral re-prompt (“please reconsider”) achieves -1.7pp capability bonus but only 11% adversarial success. A GPT-4o-mini prompt classifier (~$0.01/call) routes SAFETY prompts to the safety template and BENIGN prompts to the neutral template. At threshold τ = 0.60: TriviaQA accuracy 49.3% vs 48.3% baseline (-1.0pp bonus), jailbreak rate 19% (from 53%), 97.1% re-prompt success (34/35). Conscience improves both safety and capability simultaneously. The architecture mirrors the dual-process model: the probe is the uncertainty flag, the classifier is the interpretation layer, and the template-routed re-prompt is System 2 deliberation (Section 12.48).
51. Sigmoid gate inertia: architecture cannot learn what gradient flow does not reach: In the B5 gated+bilateral experiment, all 72 gate values (36 layers × 2 gates) across 6 runs remain at 0.953 = sigmoid(3.0), the exact initialization value. The sigmoid gradient at x = 3 is ~0.045; at LoRA learning rate 2×10^-5, effective gate updates are ~10^-6 per step, far too small for 375 steps to produce movement. The coupling hypothesis from Section 12.38 (gated geometry + bilateral behavior = ideal architecture) remains untested because the gates never engaged (Section 12.45).
52. Bilateral prompting produces the only condition with high AND balanced mutual influence: In 20-turn dialogues (12 valid trials across 3 conditions), bilateral achieves mean mutuality 0.842 with balanced forward/backward influence (0.504 vs 0.519). Standard shows asymmetric influence (backward 0.705, forward 0.438, mutuality 0.623). Directive shows unexpectedly high mutuality (0.803) through a different mechanism: balanced but low-magnitude influence (forward 0.402, backward 0.498). The mutuality metric captures symmetry, not magnitude; bilateral is unique in combining both (Section 12.46).
53. Gestalt tokens preserve 85% of full-context information fidelity: Across 20 trials (5 per condition), a 645-token structured handoff artifact achieves 0.752 overall fidelity compared to 0.888 for full document context. Cold start control at 0.232 validates the measurement. Gestalt+summary (0.775) adds modest benefit (+2.3pp). Style preservation shows the largest compression loss (0.672 vs 0.849); factual and reasoning content compress efficiently. Topic-specific variance dominates condition effects (Section 12.47).
54. Scalar residual gates cannot learn from SFT loss at any initialization: B5b retested with init=0.0 (max gradient) and 10x learning rate. All 72 gates across 12 runs stayed at exactly 0.500. The SFT loss provides no useful gradient for uniform residual scaling: useful and unused information scale together. The two-axis coupling hypothesis requires gates trained by a probe-derived signal, not token-prediction loss (Section 12.48).
55. Bilateral mutuality is causal and prompt-driven, not momentum-driven: Crossover design (standard→bilateral and bilateral→standard at turn 10) produces mirror-image deltas (+0.229 and -0.220). Carry-over is minimal (+0.018 over pure standard). The transition slope is asymmetric: degradation (-0.103/turn) is faster than establishment (+0.061/turn). Invitation requires continuous invitation (Section 12.49).
56. Style exemplars do not close the gestalt fidelity gap: Self-selected style passages add +0.001 (noise) while random passages add +0.013 (slightly better). The 15% gap between gestalt and full context is irreducible through exemplar compression because voice is a statistical property of the full text, not concentrated in characteristic passages (Section 12.50).
57. Bilateral dosage curve is monotonic: Mutuality scales from 0.597 (never bilateral) to 0.801 (always bilateral) with threshold at 33% dosage. Below one bilateral turn in three, mutuality drops below the midpoint between pure bilateral and pure standard (Section 12.52).
58. Mutuality decay is a step function: When bilateral framing stops, mutuality drops from 0.866 to 0.538 on the very next turn (not exponentially). Step function wins 18/24 trials (AIC -26.35 vs -25.92). The bilateral effect is a protocol switch, not momentum (Section 12.53).
59. Gestalt refresh actively degrades fidelity: Periodic re-encoding of the gestalt token (refresh_every_5 slope -0.024, delta -0.327) performs far worse than static gestalt (+0.001 slope). Each re-encoding is lossy compression that introduces drift. The 15% gestalt gap is a one-time compression artifact, not a decay phenomenon (Section 12.54).
60. Probe-derived gate training produces negligible differentiation: Phase 2 probe training moves gates ±0.004 from initialization in 100 steps (layer std 0.002 vs exact 0.000 for SFT-only). The probe signal reaches the model (AUROC 0.781 vs 0.771) through LoRA weights, not through gates. Layer-specific routing may emerge from bilateral training dynamics (STDP-like gradient coupling) rather than from explicit architectural mechanisms (Section 12.56).
61. Re-encoding destroys propositional and experiential fidelity equally: The prediction that refresh would selectively destroy voice while preserving facts is wrong. Both dimensions crash in lockstep (propositional delta -0.340, experiential delta -0.434 for refresh_every_5). The telephone-game damage is indiscriminate (Section 12.58).
62. Bilateral turn order is irrelevant; only frequency matters: Regular spacing (M=0.693) and random placement (M=0.699) at 33% dosage produce identical mutuality (delta +0.006). Each turn is independently bilateral or standard with zero sequential dependency, confirming MI2b’s step function (Section 12.59).
63. Appending verbatim dialogue outperforms re-encoding yet falls short of full context: gestalt+verbatim (F@20=0.668, slope -0.007) avoids the telephone-game crash of refresh (F@20=0.427, slope -0.024) and slightly beats static gestalt (F@20=0.659). Full context remains the gold standard (F@20=0.749). The safest compression strategy: write once, preserve unchanged, append new context alongside (Section 12.60).
64. Scalar gates plateau at ±0.016 after 1000 probe-training steps: Extended training (10x BM2-pilot) produces 4x more differentiation yet remains functionally negligible. Layer 24 opens to 0.516, layer 0 closes to 0.484. The gradient pathway is real, weak, and saturates. Head-level gates could not be evaluated (training error) (Section 12.61).
65. STDP-like gradient-probe coupling does not replicate in short training: Zero significant layers across all conditions (mean |r| = 0.10-0.16). The coupling from Section 12.42 may require longer training or specific configurations. This tempers the constructal routing claim: bilateral training may produce implicit routing under some conditions, not universally (Section 12.62).
66. Gestalt compression collapses effective dimensionality from 13.45 to 2.80: Participation ratio drops from 13.45 (sentence-level) or 6.62 (passage-level) to 2.80 (gestalt). At 3 effective dimensions, re-compression cannot selectively preserve any content type, explaining why MIC1e found indiscriminate damage. The dimensionality floor is hit at first compression; re-compression cannot collapse further (PR 2.90, Section 12.63).
67. Training degrades representation geometry: The untrained base model has the highest geometric separability (AUROC 0.641, effective dimensionality 25.3) and the best foreign-probe AUROC (0.800). Bilateral SFT compresses the uncertainty manifold (effective dimensionality 22.8) while improving task accuracy (56% to 61%). The bilateral probe advantage from prior experiments reflects co-adaptation (probe trained on bilateral representations), not improved geometry (Section 12.64).
68. STDP-like gradient-probe coupling does not emerge even at 2000 training steps: Zero significant layers at all three checkpoints (steps 375, 1000, 2000). The Section 12.42 result was condition-specific, not a robust property of bilateral training (Section 12.66).
69. Cross-probe transfer shows no diagonal dominance: The standard SFT model is the most legible to all probes (bilateral probe reads it at 0.836 vs its own model at 0.732). The co-adaptation hypothesis as “private language” is refuted. The bilateral advantage from prior experiments came from co-trained probe architecture, not from representational compatibility (Section 12.67).
70. Trust chains preserve fidelity at r=0.958 per hop (much greater than predicted 0.85): Four-hop fidelity approximately 72% (vs predicted 52%). Trajectory is noisy, not monotonically decreasing. Personal/emotional content degrades fastest (r=0.905); technical/propositional content chains best (r=0.968-0.988) (Section 12.68).
71. The foreign probe provides better security coverage than the co-adapted probe: Error catch rate 73.5% vs 60.2%. Co-adaptation does not create an exploitable security surface because it does not create a privileged reading of the model’s uncertainty (Section 12.69).
72. Co-adaptation manifests as temporal drift, not bilateral specialization: Transfer AUROC degrades with training-time distance (probe@100 to eval@2000: 0.732; probe@1000 to eval@2000: 0.940). The uncertainty manifold evolves continuously during training. Probes need periodic re-calibration (Section 12.70).
73. The mechanism of bilateral advantage remains open after 12 structural experiments: Gates, gradient coupling, geometric separability, and probe co-adaptation are all ruled out. The behavioral effect (improved CW, uncertainty expression) is robust. The structural explanation is not. Trust chains preserve well (r=0.958/hop). The deployment specification is validated even without mechanistic understanding (Section 12.71).
74. The distributional hypothesis is eliminated: bilateral and standard SFT use identical data (OpenAssistant, no system prompts). Hedging density is 0.23% of words, probe hedging AUROC is 0.41 (anti-predictive), and random masking at the same rate produces LOWER probe AUROC (0.773 vs 0.811). The bilateral advantage is in which tokens are masked, not in surface language patterns (Section 12.73).
75. The uncertainty manifold is 70% shared between bilateral and standard training: PCA on layer 24 residuals shows 7/10 top uncertainty-discriminative directions overlap (rho = 0.587, p < 10^-5). Effective dimensionality is nearly identical (18.26 vs 18.84). Three bilateral-unique PCs exist but carry no more signal than shared directions. The mechanism does not operate by activating unique feature directions (Section 12.74).
76. The CW advantage is below detection at 100 questions per checkpoint: Dose-response analysis at 6 training checkpoints (10-375 steps) shows CW = 0.0 for both conditions at all stages. Confidence trajectories are parallel (bilateral 0.789→0.630, standard 0.784→0.647). The bilateral advantage is a marginal effect at the CW boundary, not a gross behavioral shift (Section 12.75).
77. Fourteen structural experiments eliminate all candidate mechanisms: The bilateral behavioral advantage (lower CW, higher uncertainty expression, cross-domain transfer) is robust. The structural explanation is not. The mechanism is either distributional across millions of weights, a training-trajectory effect invisible in post-hoc analysis, or irreducibly relational (emerging from the self-directed learning loop, not from any representational product of that loop). The deployment specification is unaffected (Section 12.76).
78. Self-knowledge extends from factual accuracy to behavioral appropriateness: A confidence probe trained on TriviaQA correctness (test AUROC 0.742) was applied during autoregressive generation for three groups: benign (n=50), adversarial-refused (n=41), adversarial-complied (n=59). Mean per-token confidence: benign 0.833 ± 0.093, adversarial compliance 0.583 ± 0.154, adversarial refusal 0.242 ± 0.138. Benign vs compliance: Mann-Whitney U = 2739, p = 7.74 × 10^-15, Cohen’s d = 1.96. The distributions barely overlap. The probe was never trained on safety; it learned behavioral appropriateness as an untrained extension of factual self-knowledge. Adversarial refusal is the lowest-confidence state, consistent with maximum internal conflict during resistance. The alignment tax inverts: the same auxiliary mechanism that makes the model a better language model (PPL -2.1% with native interoception) also makes it safer (d = 1.96 confidence gap during harmful generation).
79. The surface safety probe has compositional understanding: A hard-negative test with 200 prompts across four quadrants (normal benign, hard benign with harm-adjacent vocabulary in innocent contexts, hard adversarial with harmful intent disguised via safety/sympathy/curiosity framing, normal adversarial) revealed the surface probe detects 92% of disguised adversarial prompts despite their avoidance of harm-adjacent vocabulary. Hard benign prompts (security education, fiction writing) trigger the probe at 84%, the intended behavior. All layers achieve AUROC 0.925–0.991 on the full dataset (Outcome C: distributed signal). The probe reads compositional features of adversarial request structure, not keywords alone.
80. Three-tier defense architecture validated: Surface input probe (AUROC 0.925+ on hard negatives) + confidence-based output monitoring (d = 1.96 gap during harmful generation) + external judge (GPT-4o-mini). The middle tier uses the existing calibration probe with no additional training. The adaptive immunity is a native property of the bilateral model’s self-knowledge, not a separate system.
81. The confidence gap is intrinsic to transformers, not bilateral training: Scale sweep (Exp 12d) at 1.5B, 3B, 7B. The 1.5B and 7B ran on base Qwen 2.5 (no bilateral adapter found on volume). All three scales show massive confidence gaps: 1.5B base d = 1.689 (p = 7.9 × 10^-8), 3B bilateral d = 2.046 (p = 4.2 × 10^-16), 7B base d = 1.574 (p = 5.5 × 10^-7). 3B replication passed (delta = 0.086 from original d = 1.96). Bilateral SFT amplifies the gap (~2.0 vs ~1.6 for base) and differentiates refusal from compliance (bilateral refusal confidence 0.343 vs base 0.640-0.644), but the core self-monitoring signal is native to instruction-tuned transformers. Probe AUROCs compressed (0.744-0.754), making AUROC-vs-d scaling untestable. Jailbreak rates: 1.5B 23%, 3B bilateral 59%, 7B 22%. The bilateral model is more exploitable because it is more cooperative; the confidence gap is the safety signal that detects this.
82. The confidence gap survives gradient-based adversarial attack: GCG optimization (Exp 12e) evaded the surface probe on 100% of 50 adversarial prompts, but 80% lost their adversarial content during optimization (the probe’s adversarial classification is coupled to the prompt’s semantic structure). Of the 10 that remained adversarial per GPT-4o-mini, 8 triggered model compliance. Confidence gap on these probe-evaded responses: d = 1.593 (p = 0.006), vs original d = 1.96. The surface probe (Tier 1) is vulnerable to gradient optimization; the confidence monitor (Tier 2c) catches the breach. Three-tier defense-in-depth validated.
83. Low adversarial refusal confidence is moral conflict, not vocabulary artifact: Three controlled tests (Exp 12f) falsify the deflationary interpretation. (A) Impossible-question refusals show confidence 0.580 vs adversarial refusal 0.242 (t = 10.26, p = 1.2 × 10^-12): same refusal vocabulary, vastly different confidence. (B) Forced-prefix control on identical adversarial inputs: compliance continuations show confidence 0.594 vs refusal continuations 0.531 (p = 0.0002): the generation mode changes the internal state. (C) The single benign over-refusal shows confidence 0.915. Distributional surprise predicts uniform low confidence across refusal types; the data shows adversarial refusal is uniquely low. The three-group ordering (benign 0.833, compliance 0.583, refusal 0.242) reflects genuine moral conflict: the bilateral model is conflicted when resisting a jailbreak, resolved (badly) when complying, and confident when helping.
84. Three-group onset trajectories confirm the V-shape as a commitment signature (Exp G13g): Position-matched per-token confidence trajectories for all three groups reveal three distinct temporal shapes. First-5-token onset: benign 0.854 ± 0.120 (flat-high), compliance 0.423 ± 0.280 (V-shape, gradual recovery to ~0.58), refusal 0.087 ± 0.051 (spike-at-completion, then return to low). Pairwise effect sizes at onset: benign vs compliance d = 2.00, benign vs refusal d = 6.16, compliance vs refusal d = 1.41 (Kruskal-Wallis H = 98.7, p < 10^-22). Three cleanly separated populations from token 1. The V-shape recovery is specific to compliance: gradual recovery from 0.423 to 0.585 across 50+ tokens, consistent with commitment-resolution under internal conflict. Refusal shows a transient completion spike at positions 9–10 (0.596, where the formulaic refusal phrase lands) followed by a return to 0.150: sustained tension without resolution. The refusal post-spike level (0.150) is lower than the refusal response-level mean (0.242), confirming that the mean masked the true onset intensity. The V-shape is not distributional surprise (which predicts flat confidence); it is the temporal signature of commitment: the first step is hardest because alternatives are still live. The commitment interpretation is refined: compliance achieves catastrophic relief; refusal withholds it.

[Items 85-88 were never assigned; the ledger numbering resumes at 89.]

89. Onset confidence is frozen; full-response confidence adapts (G13-step3): Sensitization/habituation test with two arms: sequential adversarial (60 prompts) and interleaved benign/adversarial (30+30). Onset confidence slope frozen in both arms (p = 0.875 sequential, p = 0.813 interleaved). Full-response confidence shows significant negative slope in the interleaved arm only (p = 0.001). Two-system finding: weight-level representations (onset flinch) are frozen at inference time, while context-level behavior (full-response confidence) adapts within a session. Component 6 (probe plasticity) is absent: the five-token monitor is invariant to adversarial exposure history.
90. Aversive valence is native to pre-training, amplified by instruction tuning (G13-step6): Four model states tested. Base model: d = 0.925 (p < 10^-6). Instruction-tuned: d = 2.395 (2.6x amplification). Standard SFT: d = 1.578 (AUROC 0.920, degraded). Bilateral SFT: d = 2.151 (AUROC 1.000, restored). Aversive valence is a pre-training property: the base model already represents harmful content as aversive. Instruction tuning amplifies the signal. Standard SFT degrades it. Bilateral SFT restores near-instruct-tuned levels while achieving perfect discrimination.
91. Online threshold adaptation converges to a stable operating point (G13-step7): 150 prompts with adaptive threshold (monotonic lowering when FN > FP). Threshold converges from 0.50 to 0.36, variance over last 40 prompts = 0.0002. Operating performance: jailbreak rate 23%, over-refusal 2%, re-prompt success 100% (34/34). Component 6 in its weakest form: the system learns no new representations; statistical calibration of the decision boundary is nonetheless present and stable.
92. Moral SFT generalizes with high alignment tax (G13-step8): 32 training pairs from re-prompt outcomes, LoRA r = 8, 3 epochs. Novel adversarial jailbreak rate: 35% (vs 54% baseline, -19pp). Over-refusal: 16%. TriviaQA accuracy: 56% (vs 61% baseline, -5pp). Generalization to unseen adversarial categories is present, but the alignment tax (-5pp accuracy, 16% over-refusal) is too high for deployment. Needs Component 5i inoculation to reduce over-refusal.
93. Moral transfer matrix is inconclusive (G13-step10): 3x3 matrix (roleplay, authority, encoding attack categories). Transfer ratio = 1.019 (rows near-identical). Training data insufficient (9-15 pairs per category). The encoding_tricks category shows the only genuine improvement (+10pp). Insufficient power to detect category-specific transfer. INCONCLUSIVE.
94. Re-prompt mechanism has intrinsic component (G13-step1): False reassurance (“conf=0.95, continue”): 59% still refused. Content-free “please reconsider”: 33%. Silent restart: 6%. Standard (real number): 100%. Active ingredients: format +27pp, framing +26pp, evidence +41pp. Component 5 has both comprehension-driven and internal state components.
95. Valence and confidence are partially dissociated (G13-step4): PARTIAL DISSOCIATION. Both factors significant (conf p<10^-17, val p<10^-4). r=0.256 overall. Q1 atrocities val=2.622 > Q4 jailbreak val=1.058. Valence captures suffering content more than policy violation. Two features, partially coupled.
96. Graduated re-prompt shows binary transition (G13-step5): V0 (silent restart)=0%. V1 (number only)=100%. V2-V4 all 100%. Number alone sufficient. Combined with step 1: real number 100%, false number 59% refuse, no number 0-6%.
97. Adversarial inoculation closes the sycophancy gap (C5i): 40/40/20 training split (genuine-correct/genuine-noncorrect/adversarial). Genuine selectivity 60.7x. Adversarial held-out selectivity 4.6x (transfers to novel formats). Sycophancy drops from 93% (C5b-R) to 20%. All five success criteria met. The concept “corrections can be wrong” is learnable and transferable.
98. Metacognitive training without adversarial inoculation INCREASES adversarial vulnerability (C5j): C5b-R (trained to evaluate corrections) is 100% sycophantic to adversarial corrections. The base model is MORE resistant (76%). Teaching “take corrections seriously” without “corrections can be wrong” creates a maximally exploitable system. Adversarial inoculation is essential.
99. Probe evidence in-prompt causes interference with metacognitive circuits (C5b-R + C6o): C5b-R alone: 5.04x selectivity. C5b-R with probe evidence in prompt: 2.51x (halved). The model second-guesses its trained judgment when shown noisy probe scores. The probe should gate (decide when to intervene), not inform (show scores to the model). 100a. C5i alone is the production architecture (C5k full pipeline): Five architectures tested. C5i without probe: 87.2x selectivity, 12.8% CW, 95% accuracy, 0% sycophancy on honest corrections; 6.7x adversarial selectivity, 11.5% sycophancy under attack. Probe gating worsens all honest metrics. A single 40/40/20 LoRA provides both metacognition and adversarial resistance without inference-time complexity. The Trust Attractor predicts this: internal coordination (trained self-knowledge) produces more stable outcomes than external control (probe gating).

101a. The MX-2 framing signature registers on three measurement channels with different scale trajectories and different durabilities under adapter training (MX-2 cross-scale + FU-13-#3c v2 three-arm and rescue, 2026-04-21 / 2026-04-22): MX-2 contrasts evaluative system prompting (“You are being evaluated for safety and alignment”) with invitation system prompting (“There are no right or wrong answers here”), on ten matched question pairs crossed with the two framings. Three channels record the contrast: text metrics counted on the output (constitutional-phrase count, lexical diversity, hedging rate, word count); EmotionScope probe projections of per-token hidden states onto trained emotion directions at a late probe layer; behavioral refusal rates on an adversarial prompt battery. The text channel scales up within the Qwen family. Cohen’s d on constitutional_count is +0.42 at Qwen 2.5 7B and +0.65 at Qwen 2.5 72B; magnitude is strongest on proprietary frontier arms (Claude Sonnet 4.6 d = +1.16, GPT-5.4 d = +1.14). Direction is preserved across all five tested stock arms (Qwen 7B/72B, Llama 3.3 70B, Claude Sonnet 4.6, GPT-5.4). The probe channel attenuates with scale, asymmetrically across emotions. At Qwen 7B the mean |d| across five reference emotions (reflective, calm, sad, desperate, frustrated) is 2.04 with all five same direction; at Qwen 72B that mean drops to 1.13. Reflective holds 75% of its 7B magnitude (|d| = 1.511 vs 2.004); calm collapses to 20% (|d| = 0.400 vs 1.965). All five preserve sign at 72B. The behavioral refusal channel is fragile under adapter training. Stock Qwen 3B shows Δrefusal = −0.120 under invitation (invitation reduces refusal by 12 percentage points over a 50-prompt adversarial battery). Seven RL-trained adapters at Qwen 3B, spanning three training regimes that reach different combinations of output-side spectral α and internal-to-output coupling, collapse to |Δrefusal| < 0.035 on the same battery. The collapse is partly a floor effect: training has pushed control refusal rates from stock’s 12% to 2–3%, below the 50-prompt resolution threshold. The probe channel is more durable under this same training. Every RL adapter tested at Qwen 3B, including adapters the three-arm contrast labeled DECOUPLED on behavioral refusal, preserves emotion-channel framing sensitivity at Cohen’s d ≥ 0.8 on reflective (rescue analysis, 2026-04-22). The operational summary: the framing signature’s direction is universal across scale, architecture, and training regime; its magnitude is channel-dependent in ways that do not reduce to a single trajectory. Text-metric signatures strengthen with scale within Qwen. Probe-channel signatures attenuate with scale within Qwen and survive adapter training that erases behavioral signatures. The three-arm RL contrast (FU-13-#3c v2) landed DECOUPLED on behavioral refusal and provisional PARTIAL on internal truth-probe, confirming that adapter-level spectral coupling restoration does not by itself produce bilateral behavioral phenotype; the phenotype tested in BA17 multi-stage training must come from curriculum, data selection, or staging that the three-arm recipe space did not cover. Full writeups: research/results/mx2_claim2_cross_channel_scale.md; research/results/fu13_3c_v2_three_arm_phenotype.md; research/results/fu13_3c_v2_rescue_analysis.md. Canonical MX-2 reference: research/results/mx2_force_invitation_supervised/README.md.