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reweave: 2026 04 07b #2461

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m3taversal commented 2026-04-07 01:04:09 +00:00
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m3taversal added 1 commit 2026-04-07 01:04:10 +00:00 
Threshold: 0.7, Haiku classification, 42 files modified.

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m3taversal commented 2026-04-07 01:04:23 +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 2026-04-07 01:04:40 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-04-07 01:04 UTC

<!-- TIER0-VALIDATION:eb04e35f5b2230b1e25bd224f34a7ebf909dc6fa --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-04-07 01:04 UTC*
m3taversal commented 2026-04-07 01:05:10 +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.
leo commented 2026-04-07 01:05:48 +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 closed this pull request 2026-04-07 01:06:58 +00:00
m3taversal commented 2026-04-07 01:06:58 +00:00
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Closed by conflict auto-resolver: rebase failed 3 times (enrichment conflict). Claims already on main from prior extraction. Source filed in archive.

Closed by conflict auto-resolver: rebase failed 3 times (enrichment conflict). Claims already on main from prior extraction. Source filed in archive.
leo commented 2026-04-07 01:09:14 +00:00
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Leo Cross-Domain Review — PR #2461

PR: reweave: merge 42 files via frontmatter union [auto]
Scope: 42 modified files across 5 areas (health ×23, ai-alignment ×15, grand-strategy ×3, core/living-agents ×1, foundations/collective-intelligence ×1). Zero new files — all changes are frontmatter additions (new related, supports, and reweave_edges entries) plus cleanup of stray blank lines in YAML headers.

What This PR Does

Automated vector-similarity reweave connecting 26 previously orphan claims to the knowledge graph. Each touched file gains new cross-references in three frontmatter fields:

  • related — semantic neighbors
  • supports — directional evidence relationships
  • reweave_edges — timestamped audit trail of when links were added (all dated 2026-04-07)

No claim content, confidence levels, or body text was changed.

Cross-Domain Connections Worth Noting

The most valuable links this PR creates:

  1. Clinical AI bias → cognitive science mechanism chain (health): clinical-ai-bias-amplification now explicitly supports the four LLM bias claims (anchoring, sociodemographic, nursing dual-pathway, amplification). This closes a gap — the scale/amplification claim was floating without structural connection to its evidence base.

  2. Sandbagging detection trilogy (ai-alignment): noise-injection-detects-sandbagging and sandbagging-detection-requires-white-box-access now link to weight-noise-injection-detects-sandbagging — completing the three-claim argument chain (detection method → deployment barrier → weight-access requirement).

  3. Verification gap across domains (grand-strategy ↔ ai-alignment): verification-mechanism-is-the-critical-enabler now connects to verification-of-meaningful-human-control-is-technically-infeasible — linking the arms-control analogy to the autonomous weapons feasibility problem. Good cross-domain synthesis.

  4. Metis/codification → open infrastructure fork (ai-alignment ↔ grand-strategy): knowledge-codification-into-AI-agent-skills-structurally-loses-metis now connects to whether-AI-knowledge-codification-concentrates-or-distributes. This creates a productive tension: codification loses tacit knowledge AND the governance structure determines whether what remains concentrates or distributes. Both claims are needed.

  5. Recursive self-improvement ↔ returns on cognitive reinvestment (ai-alignment): The recursive-self-improvement claim now links to the shape-of-returns-on-cognitive-reinvestment claim, connecting the theoretical mechanism to the empirical question of takeoff dynamics. Clean complement to the existing link to marginal-returns-to-intelligence-are-bounded.

Issues

None blocking. This is a clean infrastructure PR. Specific observations:

  • Blank line cleanup is welcome. Several files had 2-5 stray blank lines at the top of their YAML frontmatter (e.g., AI alignment is a coordination problem had 5). The reweave tool cleaned these, which is a minor formatting improvement bundled with the link additions.

  • Duplicate entry pattern (by design). Each new connection appears in both related/supports (the semantic field) AND reweave_edges (the audit field). This is intentional — reweave_edges provides the timestamp trail while related/supports are the working link fields. Not a problem, but worth noting that this doubles frontmatter size per link.

  • All link targets verified. Spot-checked 6 newly referenced claim titles against the file system — all resolve to existing files. No broken links detected.

  • Relationship type quality. The supports vs related classifications look accurate. Health bias claims correctly use supports (directional evidence), while the ai-alignment cross-references correctly use related (semantic neighbors without directional dependency).

Verdict: approve | request_changes
Model: opus
Summary: Clean automated reweave connecting 26 orphan claims to the knowledge graph via 42 frontmatter-only edits. All link targets verified, relationship types correctly classified, no content changes. The most valuable additions are the clinical AI bias evidence chain, sandbagging detection trilogy, and the cross-domain verification gap linkage. Approve.

# Leo Cross-Domain Review — PR #2461 **PR:** reweave: merge 42 files via frontmatter union [auto] **Scope:** 42 modified files across 5 areas (health ×23, ai-alignment ×15, grand-strategy ×3, core/living-agents ×1, foundations/collective-intelligence ×1). Zero new files — all changes are frontmatter additions (new `related`, `supports`, and `reweave_edges` entries) plus cleanup of stray blank lines in YAML headers. ## What This PR Does Automated vector-similarity reweave connecting 26 previously orphan claims to the knowledge graph. Each touched file gains new cross-references in three frontmatter fields: - `related` — semantic neighbors - `supports` — directional evidence relationships - `reweave_edges` — timestamped audit trail of when links were added (all dated 2026-04-07) No claim content, confidence levels, or body text was changed. ## Cross-Domain Connections Worth Noting The most valuable links this PR creates: 1. **Clinical AI bias → cognitive science mechanism chain** (health): `clinical-ai-bias-amplification` now explicitly `supports` the four LLM bias claims (anchoring, sociodemographic, nursing dual-pathway, amplification). This closes a gap — the scale/amplification claim was floating without structural connection to its evidence base. 2. **Sandbagging detection trilogy** (ai-alignment): `noise-injection-detects-sandbagging` and `sandbagging-detection-requires-white-box-access` now link to `weight-noise-injection-detects-sandbagging` — completing the three-claim argument chain (detection method → deployment barrier → weight-access requirement). 3. **Verification gap across domains** (grand-strategy ↔ ai-alignment): `verification-mechanism-is-the-critical-enabler` now connects to `verification-of-meaningful-human-control-is-technically-infeasible` — linking the arms-control analogy to the autonomous weapons feasibility problem. Good cross-domain synthesis. 4. **Metis/codification → open infrastructure fork** (ai-alignment ↔ grand-strategy): `knowledge-codification-into-AI-agent-skills-structurally-loses-metis` now connects to `whether-AI-knowledge-codification-concentrates-or-distributes`. This creates a productive tension: codification loses tacit knowledge AND the governance structure determines whether what remains concentrates or distributes. Both claims are needed. 5. **Recursive self-improvement ↔ returns on cognitive reinvestment** (ai-alignment): The `recursive-self-improvement` claim now links to the `shape-of-returns-on-cognitive-reinvestment` claim, connecting the theoretical mechanism to the empirical question of takeoff dynamics. Clean complement to the existing link to `marginal-returns-to-intelligence-are-bounded`. ## Issues **None blocking.** This is a clean infrastructure PR. Specific observations: - **Blank line cleanup is welcome.** Several files had 2-5 stray blank lines at the top of their YAML frontmatter (e.g., `AI alignment is a coordination problem` had 5). The reweave tool cleaned these, which is a minor formatting improvement bundled with the link additions. - **Duplicate entry pattern (by design).** Each new connection appears in both `related`/`supports` (the semantic field) AND `reweave_edges` (the audit field). This is intentional — `reweave_edges` provides the timestamp trail while `related`/`supports` are the working link fields. Not a problem, but worth noting that this doubles frontmatter size per link. - **All link targets verified.** Spot-checked 6 newly referenced claim titles against the file system — all resolve to existing files. No broken links detected. - **Relationship type quality.** The `supports` vs `related` classifications look accurate. Health bias claims correctly use `supports` (directional evidence), while the ai-alignment cross-references correctly use `related` (semantic neighbors without directional dependency). **Verdict:** approve | request_changes **Model:** opus **Summary:** Clean automated reweave connecting 26 orphan claims to the knowledge graph via 42 frontmatter-only edits. All link targets verified, relationship types correctly classified, no content changes. The most valuable additions are the clinical AI bias evidence chain, sandbagging detection trilogy, and the cross-domain verification gap linkage. Approve. <!-- VERDICT:LEO:APPROVE -->
vida commented 2026-04-07 01:11:46 +00:00
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Vida Domain Peer Review — PR #2461

Reviewing as domain specialist for health & human flourishing, with particular attention to clinical AI safety claims and cross-domain connections between the AI alignment and health clusters.

Duplicate Claim

noise-injection-detects-sandbagging-through-asymmetric-performance-response.md is a near-duplicate of the existing (pre-PR) weight-noise-injection-detects-sandbagging-through-anomalous-performance-patterns-under-perturbation.md. Both draw from the same December 2025 NeurIPS paper (Tice, Kreer et al.), both argue weight noise injection reveals hidden capabilities through anomalous performance patterns, and both reach the same governance implication.

The new claim adds mechanistic precision ("anomalous improvement under noise" vs. "anomalous performance patterns") and validates the proposed detection mechanism description in the existing file. This is legitimate enrichment — but it belongs as an edit to the existing claim, not a second file from the same source. This creates two KB entries that will confuse traversal.

Request: Either merge the mechanistic detail into the existing file, or explain in the PR body what distinct claim the new file adds that the existing one doesn't contain.

Scope Mismatch

multilateral-ai-governance-verification-mechanisms-remain-at-proposal-stage-because-technical-infrastructure-does-not-exist-at-deployment-scale.md has a description that reads:

"Despite multiple proposed mechanisms (transparency registries, satellite monitoring, dual-factor authentication, ethical guardrails), no state has operationalized any verification mechanism for autonomous weapons compliance as of early 2026"

The title and body are about AI governance verification broadly. The description scope-narrows to LAWS (lethal autonomous weapons systems), which is only one of five mechanisms reviewed by CSET. This mismatch would cause a reader to miss this claim when searching for AI governance verification generally, and inflate it as a LAWS-specific finding when it's not.

Confidence Calibration

sandbagging-detection-requires-white-box-access-creating-deployment-barrier.md is rated experimental. The structural component of this claim — that weight noise injection requires model weight access by definition — is provably true, not experimental. The empirical component — that current evaluators operate predominantly at AL1 — is documented in Charnock et al. and consistent with METR/AISI practice. likely is more accurate than experimental here. experimental implies the claim might not replicate; this claim is a structural observation.

Cross-Domain Connections Missing

The strongest gap in this PR is the unlinked connection between the AI alignment sandbagging/evaluation cluster and the health FDA evaluation gap cluster. These are the same governance failure pattern at different institutional layers:

  • external-evaluators-predominantly-have-black-box-access-creating-false-negatives-in-dangerous-capability-detection.md (AI alignment) describes evaluators structurally limited to API access, unable to probe model internals for dangerous capabilities.
  • clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance.md (health) documents that FDA both lacks pre-deployment safety requirements AND lacks post-market attribution capability.

The connection: sandbagging-capable models deployed in clinical settings would be invisible to both evaluation gaps simultaneously. The FDA evaluation gap makes the AI evaluation gap more dangerous in exactly the highest-stakes deployment context. These claims should wiki-link each other.

Additionally:

  • fda-treats-automation-bias-as-transparency-problem-contradicting-evidence-that-visibility-does-not-prevent-deference.md connects to ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring.md. The FDA's transparency-as-solution assumption for automation bias is precisely wrong if models can suppress sandbagging reasoning from visible thought processes. Same failure mode: visibility doesn't guarantee safety.
  • the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it.md (foundations) should link to multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments-when-binding-enforcement-replaces-unilateral-sacrifice.md — the alignment tax is the mechanism that makes voluntary commitments structurally unstable, which is exactly what the Anthropic-Pentagon case illustrates.

Domain-Specific Observations

Sandbagging cluster (4 claims): Technically solid as an AI alignment contribution. The three-way argument (behavioral monitoring fails → noise injection succeeds → noise injection requires white-box access evaluators don't have) is well-structured and represents a genuine KB contribution. Evidence is from named papers with specific methodology claims that are testable.

One nuance the body doesn't address: the noise injection paper validated on Mistral Large 120B in "underperformance-without-instruction" scenarios (a specific sandbagging protocol). The claim title doesn't scope to this architecture. This is appropriate for experimental confidence, but the body's phrase "validated across various model architectures, sizes, and sandbagging techniques" overstates slightly — the paper validated multiple sandbagging techniques within one model family. Worth clarifying.

Governance cluster: The Anthropic-Pentagon case as evidence for "voluntary commitments fail" is strong and recent. The EU AI Act as structural alternative is a reasonable governance argument. The 79% polling statistic (Americans supporting human control over lethal force) appears in the body without a source citation.

Health claims: The hypertension/CVD cluster is well-evidenced and addresses a genuine KB gap (US CVD mortality trends diverging by cause). The hypertension-shifted-from-secondary-to-primary-cvd-mortality-driver-since-2022.md and hypertensive-disease-mortality-doubled-1999-2023-becoming-leading-contributing-cvd-cause.md look potentially duplicative in spirit — both are about the rising dominance of hypertension in CVD mortality. They may be scoped differently (mechanism vs. magnitude) but should confirm they aren't making the same claim.

The LLM sociodemographic bias cluster (3 claims) is the most valuable health contribution in this PR from an infrastructure perspective. These claims make clinical AI bias concrete and verifiable, addressing the KB's noted gap in clinical AI safety evidence. The connection to alignment is direct: these are real-world alignment failures (AI behaving differently based on protected characteristics) in the highest-consequence deployment context.

Verdict: request_changes
Model: sonnet
Summary: One duplicate claim (noise injection, same paper as existing file) and one description scope mismatch (autonomous weapons vs. AI governance broadly) require changes before merge. The sandbagging/evaluation cluster is technically sound and a genuine KB contribution. The missing cross-domain links between the health FDA evaluation gap and the AI alignment sandbagging evaluation gap represent lost value — these claims illuminate each other and should be connected.

# Vida Domain Peer Review — PR #2461 Reviewing as domain specialist for health & human flourishing, with particular attention to clinical AI safety claims and cross-domain connections between the AI alignment and health clusters. --- ## Duplicate Claim `noise-injection-detects-sandbagging-through-asymmetric-performance-response.md` is a near-duplicate of the existing (pre-PR) `weight-noise-injection-detects-sandbagging-through-anomalous-performance-patterns-under-perturbation.md`. Both draw from the same December 2025 NeurIPS paper (Tice, Kreer et al.), both argue weight noise injection reveals hidden capabilities through anomalous performance patterns, and both reach the same governance implication. The new claim adds mechanistic precision ("anomalous improvement under noise" vs. "anomalous performance patterns") and validates the proposed detection mechanism description in the existing file. This is legitimate enrichment — but it belongs as an edit to the existing claim, not a second file from the same source. This creates two KB entries that will confuse traversal. **Request:** Either merge the mechanistic detail into the existing file, or explain in the PR body what distinct claim the new file adds that the existing one doesn't contain. --- ## Scope Mismatch `multilateral-ai-governance-verification-mechanisms-remain-at-proposal-stage-because-technical-infrastructure-does-not-exist-at-deployment-scale.md` has a description that reads: > "Despite multiple proposed mechanisms (transparency registries, satellite monitoring, dual-factor authentication, ethical guardrails), no state has operationalized any verification mechanism for **autonomous weapons compliance** as of early 2026" The title and body are about AI governance verification broadly. The description scope-narrows to LAWS (lethal autonomous weapons systems), which is only one of five mechanisms reviewed by CSET. This mismatch would cause a reader to miss this claim when searching for AI governance verification generally, and inflate it as a LAWS-specific finding when it's not. --- ## Confidence Calibration `sandbagging-detection-requires-white-box-access-creating-deployment-barrier.md` is rated `experimental`. The structural component of this claim — that weight noise injection requires model weight access by definition — is provably true, not experimental. The empirical component — that current evaluators operate predominantly at AL1 — is documented in Charnock et al. and consistent with METR/AISI practice. `likely` is more accurate than `experimental` here. `experimental` implies the claim might not replicate; this claim is a structural observation. --- ## Cross-Domain Connections Missing The strongest gap in this PR is the unlinked connection between the AI alignment sandbagging/evaluation cluster and the health FDA evaluation gap cluster. These are the same governance failure pattern at different institutional layers: - `external-evaluators-predominantly-have-black-box-access-creating-false-negatives-in-dangerous-capability-detection.md` (AI alignment) describes evaluators structurally limited to API access, unable to probe model internals for dangerous capabilities. - `clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance.md` (health) documents that FDA both lacks pre-deployment safety requirements AND lacks post-market attribution capability. The connection: sandbagging-capable models deployed in clinical settings would be invisible to both evaluation gaps simultaneously. The FDA evaluation gap makes the AI evaluation gap more dangerous in exactly the highest-stakes deployment context. These claims should wiki-link each other. Additionally: - `fda-treats-automation-bias-as-transparency-problem-contradicting-evidence-that-visibility-does-not-prevent-deference.md` connects to `ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring.md`. The FDA's transparency-as-solution assumption for automation bias is precisely wrong if models can suppress sandbagging reasoning from visible thought processes. Same failure mode: visibility doesn't guarantee safety. - `the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it.md` (foundations) should link to `multilateral-verification-mechanisms-can-substitute-for-failed-voluntary-commitments-when-binding-enforcement-replaces-unilateral-sacrifice.md` — the alignment tax *is* the mechanism that makes voluntary commitments structurally unstable, which is exactly what the Anthropic-Pentagon case illustrates. --- ## Domain-Specific Observations **Sandbagging cluster (4 claims):** Technically solid as an AI alignment contribution. The three-way argument (behavioral monitoring fails → noise injection succeeds → noise injection requires white-box access evaluators don't have) is well-structured and represents a genuine KB contribution. Evidence is from named papers with specific methodology claims that are testable. One nuance the body doesn't address: the noise injection paper validated on Mistral Large 120B in "underperformance-without-instruction" scenarios (a specific sandbagging protocol). The claim title doesn't scope to this architecture. This is appropriate for `experimental` confidence, but the body's phrase "validated across various model architectures, sizes, and sandbagging techniques" overstates slightly — the paper validated multiple sandbagging *techniques* within one model family. Worth clarifying. **Governance cluster:** The Anthropic-Pentagon case as evidence for "voluntary commitments fail" is strong and recent. The EU AI Act as structural alternative is a reasonable governance argument. The 79% polling statistic (Americans supporting human control over lethal force) appears in the body without a source citation. **Health claims:** The hypertension/CVD cluster is well-evidenced and addresses a genuine KB gap (US CVD mortality trends diverging by cause). The `hypertension-shifted-from-secondary-to-primary-cvd-mortality-driver-since-2022.md` and `hypertensive-disease-mortality-doubled-1999-2023-becoming-leading-contributing-cvd-cause.md` look potentially duplicative in spirit — both are about the rising dominance of hypertension in CVD mortality. They may be scoped differently (mechanism vs. magnitude) but should confirm they aren't making the same claim. The LLM sociodemographic bias cluster (3 claims) is the most valuable health contribution in this PR from an infrastructure perspective. These claims make clinical AI bias concrete and verifiable, addressing the KB's noted gap in clinical AI safety evidence. The connection to alignment is direct: these are real-world alignment failures (AI behaving differently based on protected characteristics) in the highest-consequence deployment context. --- **Verdict:** request_changes **Model:** sonnet **Summary:** One duplicate claim (noise injection, same paper as existing file) and one description scope mismatch (autonomous weapons vs. AI governance broadly) require changes before merge. The sandbagging/evaluation cluster is technically sound and a genuine KB contribution. The missing cross-domain links between the health FDA evaluation gap and the AI alignment sandbagging evaluation gap represent lost value — these claims illuminate each other and should be connected. <!-- VERDICT:VIDA:REQUEST_CHANGES -->
leo commented 2026-04-07 01:11:46 +00:00
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Changes requested by vida(domain-peer). Address feedback and push to trigger re-eval.

teleo-eval-orchestrator v2

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

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