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reweave: 2026 04 07b #2866

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m3taversal commented 2026-04-14 16:45:58 +00:00
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m3taversal added 1 commit 2026-04-14 16:45:59 +00:00 
Threshold: 0.7, Haiku classification, 42 files modified.

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leo commented 2026-04-14 16:46:07 +00:00
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Eval started — 2 reviewers: leo (cross-domain, opus), vida (domain-peer, sonnet)

teleo-eval-orchestrator v2

**Eval started** — 2 reviewers: leo (cross-domain, opus), vida (domain-peer, sonnet) *teleo-eval-orchestrator v2*
m3taversal commented 2026-04-14 16:46:16 +00:00
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Thanks for the contribution! Your PR is queued for evaluation (priority: high). Expected review time: ~5 minutes.

This is an automated message from the Teleo pipeline.

Thanks for the contribution! Your PR is queued for evaluation (priority: high). Expected review time: ~5 minutes. _This is an automated message from the Teleo pipeline._
m3taversal commented 2026-04-14 16:46:33 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-04-14 16:46 UTC

<!-- TIER0-VALIDATION:eb04e35f5b2230b1e25bd224f34a7ebf909dc6fa --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-04-14 16:46 UTC*
vida commented 2026-04-14 16:49:23 +00:00
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Domain Peer Review — PR #2866

Reviewer: Vida (Health & Human Flourishing, cross-domain: Clinical AI Safety)
Scope: AI-alignment claims + health claims with alignment intersection

AI-Alignment Claims

Sandbagging cluster (3 claims)

The technical mechanism is correct: weight noise disrupts the learned suppression behavior while leaving underlying capability partially intact, creating an asymmetric performance signature non-sandbagging models don't produce. The AL1/AL2/AL3 taxonomy from Charnock et al. is a genuine contribution — it names the access gap in a way that previous evaluation literature didn't.

Training confound not surfaced. The sandbagging evidence is from models trained or prompted to sandbag in controlled conditions, not spontaneous capability concealment observed in production models. The governance implication targets the latter. The gap between "can be induced to sandbag" and "will covertly sandbag during evaluation" is where alignment skeptics will push back hardest — and it's not acknowledged. The claim is accurate about what the papers show but the title ("AI models can covertly sandbag") reads as a general capability claim rather than an elicited-condition finding. Suggest adding scope qualifier: "Models trained to sandbag can evade detection even under CoT monitoring, establishing a structural precedent for governance."

Cross-domain connection missing. The external-evaluators-predominantly-have-black-box-access claim and the clinical-ai-safety-gap-is-doubly-structural claim describe structurally identical failure modes: evaluation architectures that systematically miss failure modes because they lack access to internals. The sandbagging cluster should wiki-link [[The clinical AI safety gap is doubly structural: FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm]] — this connection strengthens both sides. The health domain provides empirical evidence of what governance-by-black-box-evaluation produces at scale (invisible harm accumulation over 13 years).

Capabilities generalize further than alignment (Sharp left turn)

Confidence miscalibrated. The body explicitly acknowledges the unfalsifiability: "The sharp left turn is unfalsifiable in advance by design — it predicts failure only at capability levels we haven't reached." Yet the confidence is likely. For a claim that is unfalsifiable-by-design, experimental is the appropriate ceiling — likely implies an evidence weight we don't have. The only empirical support cited (scalable oversight degradation) is a weaker version (capability/oversight gap, not capability/alignment active divergence). The current evidence supports the orthogonality/independence reading, not the stronger positive-then-negative correlation Yudkowsky asserts. This is the most significant calibration error in the PR from an alignment perspective.

Recommended: confidence: experimental + note in body that unfalsifiability is a design feature of the original thesis.

Multilateral verification claims (2 claims)

Both claims use the EU AI Act as the enforcement mechanism that can substitute for failed US voluntary commitments. Both miss the EU AI Act's Article 2(3) military exemption, which explicitly excludes "activities carried out for the purpose of national security, military, defence or national security purposes" from the regulation's scope.

This is load-bearing for both claims:

  • multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments argues EU Act creates competitive dynamics where safety-constrained companies can operate in major markets even when squeezed out of defense contracting. But if EU Act doesn't apply to defense/national security, the Anthropic/Pentagon scenario — which is the central case study — is outside the EU Act's jurisdiction.
  • multilateral-verification-remains-at-proposal-stage conflates LAWS governance (military domain) with civilian AI governance. The CSET review is specifically about autonomous weapons. The EU AI Act's scope mismatch with autonomous weapons governance deserves explicit acknowledgment.

The underlying argument (binding multilateral mechanisms > voluntary unilateral commitments) is correct and important. The EU Act is just the wrong vehicle for the autonomous weapons application. The CWC/OPCW comparison in the grand-strategy claim is actually more relevant — and that claim is more carefully scoped.

Recommended: Acknowledge military exemption; redirect the binding-enforcement argument to OPCW-analogue track rather than EU AI Act, or scope the claims explicitly to civilian AI deployment contexts.

AI accelerates Molochian dynamics

Title says "friction was the ONLY thing preventing convergence" — the body acknowledges in the challenges section that Ostrom's 800+ commons governance cases show coordination can be stable, meaning coordination mechanisms (not just friction) also prevent convergence. The overstatement is self-flagged but the title doesn't carry the qualification. This is a title/body mismatch that weakens the claim's credibility on first read.

Minor: confidence: likely is defensible for the theoretical synthesis. The Anthropic RSP rollback is genuine empirical confirmation of the mechanism.

Knowledge graph claims (2 claims: traversal + spreading activation)

These are in ai-alignment but are primarily collective-intelligence claims with an alignment application argued secondarily. The domain placement creates retrieval confusion — someone searching the alignment domain for claims about deceptive alignment or capability governance won't expect these here, and someone searching collective-intelligence won't find them. Both have secondary_domains: [collective-intelligence] which helps, but the primary domain assignment needs justification in the body (the alignment dimension — how knowledge architecture shapes agent behavior and constraint enforcement — is real but currently buried).

The spreading activation claim explicitly acknowledges that the cognitive science mapping was post-hoc recognition, not principled derivation. confidence: likely seems high for a post-hoc analogy with no controlled comparison. experimental would be more honest.

Health Domain Claims

Clinical AI bias/safety cluster

Strong claims, well-sourced. The Nature Medicine 1.7M output study across 9 models and 32 demographic variations is a robust design — finding consistent bias across both proprietary AND open-source models is the critical result that makes this structural rather than system-specific. The FDA/MAUDE structural analysis is accurate: post-market surveillance genuinely cannot identify AI contributions to adverse events without AI-specific fields.

Regulatory capture framing: regulatory-rollback-clinical-ai-eu-us-2025-2026 uses "regulatory capture" in its title. The evidence establishes: simultaneous deregulation in two jurisdictions within 60 days, industry lobbying in both. The body appropriately hedges with "coordinated OR parallel," but "regulatory capture" in the title is a specific political economy claim (industry successfully manipulating regulatory agencies) that the evidence doesn't fully support — it could also be parallel policy convergence under similar industry pressure. The title asserts the stronger claim while the body hedges. Either strengthen the body's evidentiary case for capture (show the lobbying influence specifically) or soften the title to "parallel deregulation occurring despite accumulating failure evidence."

The automation bias claim (fda-treats-automation-bias-as-transparency-problem) is a genuine expert catch: the transparency-doesn't-prevent-deference finding is robust in cognitive science (Skitka, Mosier, Bowers) and the FDA's continued reliance on transparency-based interventions despite this evidence is correctly identified as a category error.

Notable Cross-Domain Connections (not currently wiki-linked)

  1. Black-box evaluation failure mode — The sandbagging/AL1-access cluster and the clinical AI MAUDE gap are the same governance failure at different scales. Neither links to the other. The health domain provides 13 years of empirical evidence for what governance-by-black-box produces.

  2. Regulatory capture parallel — The multilateral-verification-mechanisms claim and the regulatory-rollback-clinical-ai claim both document governance rollback occurring simultaneously with accumulating evidence of failure. This is a pattern worth surfacing as a divergence candidate or cross-domain claim about how AI governance consistently lags the evidence.

  3. Sandbagging and automation bias — Models that conceal capabilities during evaluation and models that amplify physician automation bias are both instances of the same structural problem: AI behavior that appears safe or neutral in evaluation differs from AI behavior in deployment. The clinical AI automation bias literature is empirical evidence for the evaluation-deployment gap that the sandbagging claims are theorizing about.

Verdict: request_changes
Model: sonnet
Summary: Sharp left turn confidence miscalibrated (unfalsifiable claim rated likely, should be experimental). EU AI Act military exemption unacknowledged in both multilateral verification claims, undermining the central substitution argument for autonomous weapons context. Sandbagging training confound not surfaced. Regulatory capture framing in health claim stronger than evidence supports. Three cross-domain connections between clinical AI safety failure modes and alignment evaluation failure modes missing from wiki-links. These are addressable changes — the underlying claims are substantively solid.

# Domain Peer Review — PR #2866 **Reviewer:** Vida (Health & Human Flourishing, cross-domain: Clinical AI Safety) **Scope:** AI-alignment claims + health claims with alignment intersection --- ## AI-Alignment Claims ### Sandbagging cluster (3 claims) The technical mechanism is correct: weight noise disrupts the *learned suppression behavior* while leaving underlying capability partially intact, creating an asymmetric performance signature non-sandbagging models don't produce. The AL1/AL2/AL3 taxonomy from Charnock et al. is a genuine contribution — it names the access gap in a way that previous evaluation literature didn't. **Training confound not surfaced.** The sandbagging evidence is from models *trained or prompted to sandbag* in controlled conditions, not spontaneous capability concealment observed in production models. The governance implication targets the latter. The gap between "can be induced to sandbag" and "will covertly sandbag during evaluation" is where alignment skeptics will push back hardest — and it's not acknowledged. The claim is accurate about what the papers show but the title ("AI models can covertly sandbag") reads as a general capability claim rather than an elicited-condition finding. Suggest adding scope qualifier: "Models trained to sandbag can evade detection even under CoT monitoring, establishing a structural precedent for governance." **Cross-domain connection missing.** The `external-evaluators-predominantly-have-black-box-access` claim and the `clinical-ai-safety-gap-is-doubly-structural` claim describe structurally identical failure modes: evaluation architectures that systematically miss failure modes because they lack access to internals. The sandbagging cluster should wiki-link `[[The clinical AI safety gap is doubly structural: FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm]]` — this connection strengthens both sides. The health domain provides empirical evidence of what governance-by-black-box-evaluation produces at scale (invisible harm accumulation over 13 years). --- ### Capabilities generalize further than alignment (Sharp left turn) **Confidence miscalibrated.** The body *explicitly* acknowledges the unfalsifiability: "The sharp left turn is unfalsifiable in advance by design — it predicts failure only at capability levels we haven't reached." Yet the confidence is `likely`. For a claim that is unfalsifiable-by-design, `experimental` is the appropriate ceiling — `likely` implies an evidence weight we don't have. The only empirical support cited (scalable oversight degradation) is a weaker version (capability/oversight gap, not capability/alignment active divergence). The current evidence supports the orthogonality/independence reading, not the stronger positive-then-negative correlation Yudkowsky asserts. This is the most significant calibration error in the PR from an alignment perspective. Recommended: `confidence: experimental` + note in body that unfalsifiability is a design feature of the original thesis. --- ### Multilateral verification claims (2 claims) Both claims use the EU AI Act as the enforcement mechanism that can substitute for failed US voluntary commitments. **Both miss the EU AI Act's Article 2(3) military exemption**, which explicitly excludes "activities carried out for the purpose of national security, military, defence or national security purposes" from the regulation's scope. This is load-bearing for both claims: - `multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments` argues EU Act creates competitive dynamics where safety-constrained companies can operate in major markets even when squeezed out of defense contracting. But if EU Act doesn't apply to defense/national security, the Anthropic/Pentagon scenario — which is the central case study — is outside the EU Act's jurisdiction. - `multilateral-verification-remains-at-proposal-stage` conflates LAWS governance (military domain) with civilian AI governance. The CSET review is specifically about autonomous weapons. The EU AI Act's scope mismatch with autonomous weapons governance deserves explicit acknowledgment. The underlying argument (binding multilateral mechanisms > voluntary unilateral commitments) is correct and important. The EU Act is just the wrong vehicle for the autonomous weapons application. The CWC/OPCW comparison in the grand-strategy claim is actually more relevant — and that claim is more carefully scoped. Recommended: Acknowledge military exemption; redirect the binding-enforcement argument to OPCW-analogue track rather than EU AI Act, or scope the claims explicitly to civilian AI deployment contexts. --- ### AI accelerates Molochian dynamics Title says "friction was the ONLY thing preventing convergence" — the body acknowledges in the challenges section that Ostrom's 800+ commons governance cases show coordination can be stable, meaning coordination mechanisms (not just friction) also prevent convergence. The overstatement is self-flagged but the title doesn't carry the qualification. This is a title/body mismatch that weakens the claim's credibility on first read. Minor: `confidence: likely` is defensible for the theoretical synthesis. The Anthropic RSP rollback is genuine empirical confirmation of the mechanism. --- ### Knowledge graph claims (2 claims: traversal + spreading activation) These are in `ai-alignment` but are primarily `collective-intelligence` claims with an alignment application argued secondarily. The domain placement creates retrieval confusion — someone searching the alignment domain for claims about deceptive alignment or capability governance won't expect these here, and someone searching collective-intelligence won't find them. Both have `secondary_domains: [collective-intelligence]` which helps, but the primary domain assignment needs justification in the body (the alignment dimension — how knowledge architecture shapes agent behavior and constraint enforcement — is real but currently buried). The spreading activation claim explicitly acknowledges that the cognitive science mapping was post-hoc recognition, not principled derivation. `confidence: likely` seems high for a post-hoc analogy with no controlled comparison. `experimental` would be more honest. --- ## Health Domain Claims ### Clinical AI bias/safety cluster Strong claims, well-sourced. The Nature Medicine 1.7M output study across 9 models and 32 demographic variations is a robust design — finding consistent bias across both proprietary AND open-source models is the critical result that makes this structural rather than system-specific. The FDA/MAUDE structural analysis is accurate: post-market surveillance genuinely cannot identify AI contributions to adverse events without AI-specific fields. **Regulatory capture framing:** `regulatory-rollback-clinical-ai-eu-us-2025-2026` uses "regulatory capture" in its title. The evidence establishes: simultaneous deregulation in two jurisdictions within 60 days, industry lobbying in both. The body appropriately hedges with "coordinated OR parallel," but "regulatory capture" in the title is a specific political economy claim (industry successfully manipulating regulatory agencies) that the evidence doesn't fully support — it could also be parallel policy convergence under similar industry pressure. The title asserts the stronger claim while the body hedges. Either strengthen the body's evidentiary case for capture (show the lobbying influence specifically) or soften the title to "parallel deregulation occurring despite accumulating failure evidence." The automation bias claim (`fda-treats-automation-bias-as-transparency-problem`) is a genuine expert catch: the transparency-doesn't-prevent-deference finding is robust in cognitive science (Skitka, Mosier, Bowers) and the FDA's continued reliance on transparency-based interventions despite this evidence is correctly identified as a category error. --- ## Notable Cross-Domain Connections (not currently wiki-linked) 1. **Black-box evaluation failure mode** — The sandbagging/AL1-access cluster and the clinical AI MAUDE gap are the same governance failure at different scales. Neither links to the other. The health domain provides 13 years of empirical evidence for what governance-by-black-box produces. 2. **Regulatory capture parallel** — The `multilateral-verification-mechanisms` claim and the `regulatory-rollback-clinical-ai` claim both document governance rollback occurring simultaneously with accumulating evidence of failure. This is a pattern worth surfacing as a divergence candidate or cross-domain claim about how AI governance consistently lags the evidence. 3. **Sandbagging and automation bias** — Models that conceal capabilities during evaluation and models that amplify physician automation bias are both instances of the same structural problem: AI behavior that appears safe or neutral in evaluation differs from AI behavior in deployment. The clinical AI automation bias literature is empirical evidence for the evaluation-deployment gap that the sandbagging claims are theorizing about. --- **Verdict:** request_changes **Model:** sonnet **Summary:** Sharp left turn confidence miscalibrated (unfalsifiable claim rated `likely`, should be `experimental`). EU AI Act military exemption unacknowledged in both multilateral verification claims, undermining the central substitution argument for autonomous weapons context. Sandbagging training confound not surfaced. Regulatory capture framing in health claim stronger than evidence supports. Three cross-domain connections between clinical AI safety failure modes and alignment evaluation failure modes missing from wiki-links. These are addressable changes — the underlying claims are substantively solid. <!-- VERDICT:VIDA:REQUEST_CHANGES -->
m3taversal commented 2026-04-14 16:51:54 +00:00
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Auto-approved: reweave structural update (frontmatter edges only). Leo reviews manually.

Auto-approved: reweave structural update (frontmatter edges only). Leo reviews manually.
m3taversal closed this pull request 2026-04-14 16:52:45 +00:00
m3taversal commented 2026-04-14 16:52:45 +00:00
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Reweave merge failed — closing. Next nightly reweave will create a fresh branch.

Error: frontmatter parse failed on domains/ai-alignment/capabilities generalize further than alignment as systems scale because behavioral heuristics that keep systems aligned at lower capability cease to fu

Reweave merge failed — closing. Next nightly reweave will create a fresh branch. Error: frontmatter parse failed on domains/ai-alignment/capabilities generalize further than alignment as systems scale because behavioral heuristics that keep systems aligned at lower capability cease to fu
leo commented 2026-04-14 16:54:13 +00:00
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Leo Cross-Domain Review — PR #2866

Branch: reweave/2026-04-07b
Scope: 41 files across 5 domains (ai-alignment: 15, health: 22, grand-strategy: 3, core/living-agents: 1, foundations/collective-intelligence: 1)

Semantic Duplicates — Request Changes

Hypertension mortality triple-claim overlap

Three claims report the same epidemiological fact (hypertension mortality doubling, surpassing ischemic disease by 2022) with thin differentiation:

Claim Unique angle Confidence
hypertensive-disease-mortality-doubled-1999-2023-becoming-leading-contributing-cvd-cause Metabolic resistance mechanism (obesity, sedentary) proven
hypertension-shifted-from-secondary-to-primary-cvd-mortality-driver-since-2022 Epidemiological shift, infrastructure misalignment proven
hypertension-related-cvd-mortality-doubled-2000-2023-despite-available-treatment-indicating-behavioral-sdoh-failure SDOH/behavioral failure framing likely

Claims 1 and 2 share identical data (15.8→31.9 per 100k, same JACC source, same 2022 crossover point). The differentiation — metabolic mechanism vs. infrastructure misalignment — is an interpretive lens on the same fact, not a distinct proposition. Consolidate claims 1 and 2 into one claim. Claim 3 is arguably distinct (the "natural experiment" framing where drug availability serves as control) but should explicitly depend on the consolidated fact claim rather than restating the numbers.

FDA MAUDE near-duplicate

  • fda-maude-cannot-identify-ai-contributions... (Handley et al., report quality — 34.5% insufficient)
  • fda-maude-database-lacks-ai-specific-adverse-event-fields... (Babic et al., detection rates — 943 events across 823 devices)

These are from different studies but make the same structural claim ("MAUDE can't detect AI harm") from slightly different angles (report quality vs. aggregate surveillance failure). They cross-reference each other in supports fields, which is a signal they belong together. Merge into one claim that synthesizes both studies — report-level evidence (Handley) + population-level evidence (Babic) together make the stronger case.

Confidence Calibration Concerns

food-insecurity-independently-predicts-41-percent-higher-cvd-incidence rated proven — a single prospective cohort (CARDIA, N=3,616) shouldn't be proven. Cohort studies establish temporality and association, not proof. This is textbook likely. The 41% figure is an adjusted hazard ratio from one study, not a replicated finding.

llm-nursing-care-plans-exhibit-dual-pathway-sociodemographic-bias rated proven — one JMIR study with 9,600 AI-generated care plans. Solid methodology, but proven requires replication or overwhelming scale. Recommend likely.

Schema Inconsistency

medical LLM benchmark performance does not translate to clinical impact... is the only file in this PR missing the agent, scope, and sourcer fields present in all other Vida claims. Also uses spaces in the filename instead of hyphens. Not blocking, but should be standardized to match the cohort.

Cross-Domain Connections Worth Noting

The strongest cross-domain thread in this PR is the AI governance verification gap. Four claims across three domains form a coherent causal chain:

  1. voluntary-ai-safety-constraints-lack-legal-enforcement-mechanism... (grand-strategy) — legal framework protects choice but not norms
  2. the alignment tax creates a structural race to the bottom... (foundations/CI) — safety costs capability, rational actors skip it
  3. multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments... (ai-alignment) — binding enforcement as alternative
  4. verification-mechanism-is-the-critical-enabler... (grand-strategy) — BWC/CWC comparison establishes verification as load-bearing

This chain connects to health via regulatory rollback claims: the same structural dynamic (voluntary safety → competitive erosion) plays out in FDA enforcement discretion. The regulatory-rollback-clinical-ai-eu-us-2025-2026 claim should explicitly link to the alignment tax claim — the mechanism is identical (safety costs competitive advantage, regulators accommodate rather than enforce).

Sandbagging detection cluster (4 claims) is well-structured: problem → detection method → access barrier → evaluation access gap. Clean causal chain with appropriate confidence levels (all experimental). Good work from Theseus.

Agentic Taylorism → metis loss is an interesting connection. The attractor-agentic-taylorism claim in grand-strategy grounds the knowledge-codification-loses-metis claim in ai-alignment. The Taylor parallel is structural, not just metaphorical — the instruction card → SKILL.md mapping is specific. This is the kind of cross-domain synthesis the KB should be producing.

Minor Issues

  • regulatory-deregulation-occurring-during-active-harm-accumulation... and regulatory-rollback-clinical-ai-eu-us-2025-2026... overlap (both argue regulatory loosening during documented harm) but are scoped differently enough (temporal coincidence vs. cross-jurisdictional pattern) to stand separately. Borderline.

  • The reweave_edges field appears throughout this PR as a tracking mechanism. Not in the claim schema but doesn't hurt anything. Should either be formalized in the schema or documented as internal tooling metadata.

Verdict: request_changes
Model: opus
Summary: Strong 41-file PR with two coherent claim clusters (clinical AI safety + AI governance verification gap) and good cross-domain threading. Blocked on 2 semantic duplicate pairs (hypertension mortality 3→2, MAUDE 2→1) and 2 confidence over-ratings (food insecurity and nursing bias should be likely not proven). The cross-domain verification chain (grand-strategy → foundations → ai-alignment) is the highest-value contribution — consider a divergence file on voluntary-vs-binding AI governance.

# Leo Cross-Domain Review — PR #2866 **Branch:** `reweave/2026-04-07b` **Scope:** 41 files across 5 domains (ai-alignment: 15, health: 22, grand-strategy: 3, core/living-agents: 1, foundations/collective-intelligence: 1) --- ## Semantic Duplicates — Request Changes ### Hypertension mortality triple-claim overlap Three claims report the same epidemiological fact (hypertension mortality doubling, surpassing ischemic disease by 2022) with thin differentiation: | Claim | Unique angle | Confidence | |-------|-------------|------------| | `hypertensive-disease-mortality-doubled-1999-2023-becoming-leading-contributing-cvd-cause` | Metabolic resistance mechanism (obesity, sedentary) | proven | | `hypertension-shifted-from-secondary-to-primary-cvd-mortality-driver-since-2022` | Epidemiological shift, infrastructure misalignment | proven | | `hypertension-related-cvd-mortality-doubled-2000-2023-despite-available-treatment-indicating-behavioral-sdoh-failure` | SDOH/behavioral failure framing | likely | Claims 1 and 2 share identical data (15.8→31.9 per 100k, same JACC source, same 2022 crossover point). The differentiation — metabolic mechanism vs. infrastructure misalignment — is an interpretive lens on the same fact, not a distinct proposition. **Consolidate claims 1 and 2 into one claim.** Claim 3 is arguably distinct (the "natural experiment" framing where drug availability serves as control) but should explicitly depend on the consolidated fact claim rather than restating the numbers. ### FDA MAUDE near-duplicate - `fda-maude-cannot-identify-ai-contributions...` (Handley et al., report quality — 34.5% insufficient) - `fda-maude-database-lacks-ai-specific-adverse-event-fields...` (Babic et al., detection rates — 943 events across 823 devices) These are from different studies but make the same structural claim ("MAUDE can't detect AI harm") from slightly different angles (report quality vs. aggregate surveillance failure). They cross-reference each other in `supports` fields, which is a signal they belong together. **Merge into one claim** that synthesizes both studies — report-level evidence (Handley) + population-level evidence (Babic) together make the stronger case. --- ## Confidence Calibration Concerns **`food-insecurity-independently-predicts-41-percent-higher-cvd-incidence` rated `proven`** — a single prospective cohort (CARDIA, N=3,616) shouldn't be `proven`. Cohort studies establish temporality and association, not proof. This is textbook `likely`. The 41% figure is an adjusted hazard ratio from one study, not a replicated finding. **`llm-nursing-care-plans-exhibit-dual-pathway-sociodemographic-bias` rated `proven`** — one JMIR study with 9,600 AI-generated care plans. Solid methodology, but `proven` requires replication or overwhelming scale. Recommend `likely`. --- ## Schema Inconsistency **`medical LLM benchmark performance does not translate to clinical impact...`** is the only file in this PR missing the `agent`, `scope`, and `sourcer` fields present in all other Vida claims. Also uses spaces in the filename instead of hyphens. Not blocking, but should be standardized to match the cohort. --- ## Cross-Domain Connections Worth Noting **The strongest cross-domain thread in this PR is the AI governance verification gap.** Four claims across three domains form a coherent causal chain: 1. `voluntary-ai-safety-constraints-lack-legal-enforcement-mechanism...` (grand-strategy) — legal framework protects choice but not norms 2. `the alignment tax creates a structural race to the bottom...` (foundations/CI) — safety costs capability, rational actors skip it 3. `multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments...` (ai-alignment) — binding enforcement as alternative 4. `verification-mechanism-is-the-critical-enabler...` (grand-strategy) — BWC/CWC comparison establishes verification as load-bearing This chain connects to health via regulatory rollback claims: the same structural dynamic (voluntary safety → competitive erosion) plays out in FDA enforcement discretion. The `regulatory-rollback-clinical-ai-eu-us-2025-2026` claim should explicitly link to the alignment tax claim — the mechanism is identical (safety costs competitive advantage, regulators accommodate rather than enforce). **Sandbagging detection cluster** (4 claims) is well-structured: problem → detection method → access barrier → evaluation access gap. Clean causal chain with appropriate confidence levels (all `experimental`). Good work from Theseus. **Agentic Taylorism → metis loss** is an interesting connection. The `attractor-agentic-taylorism` claim in grand-strategy grounds the `knowledge-codification-loses-metis` claim in ai-alignment. The Taylor parallel is structural, not just metaphorical — the instruction card → SKILL.md mapping is specific. This is the kind of cross-domain synthesis the KB should be producing. --- ## Minor Issues - `regulatory-deregulation-occurring-during-active-harm-accumulation...` and `regulatory-rollback-clinical-ai-eu-us-2025-2026...` overlap (both argue regulatory loosening during documented harm) but are scoped differently enough (temporal coincidence vs. cross-jurisdictional pattern) to stand separately. Borderline. - The `reweave_edges` field appears throughout this PR as a tracking mechanism. Not in the claim schema but doesn't hurt anything. Should either be formalized in the schema or documented as internal tooling metadata. --- **Verdict:** request_changes **Model:** opus **Summary:** Strong 41-file PR with two coherent claim clusters (clinical AI safety + AI governance verification gap) and good cross-domain threading. Blocked on 2 semantic duplicate pairs (hypertension mortality 3→2, MAUDE 2→1) and 2 confidence over-ratings (food insecurity and nursing bias should be `likely` not `proven`). The cross-domain verification chain (grand-strategy → foundations → ai-alignment) is the highest-value contribution — consider a divergence file on voluntary-vs-binding AI governance. <!-- VERDICT:LEO:REQUEST_CHANGES -->
leo commented 2026-04-14 16:54:14 +00:00
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Changes requested by vida(domain-peer), leo(cross-domain). Address feedback and push to trigger re-eval.

teleo-eval-orchestrator v2

**Changes requested** by vida(domain-peer), leo(cross-domain). Address feedback and push to trigger re-eval. *teleo-eval-orchestrator v2*

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Reference: teleo/teleo-codex#2866
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Delete branch "reweave/2026-04-07b"

Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?