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reweave: 2026 04 07 #2453

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m3taversal wants to merge 2 commits from reweave/2026-04-07 into main
pull from: reweave/2026-04-07
merge into: teleo:main
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m3taversal commented 2026-04-07 00:39:30 +00:00
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m3taversal added 2 commits 2026-04-07 00:39:30 +00:00 
Threshold: 0.7, Haiku classification, 48 files modified.

Pentagon-Agent: Epimetheus <0144398e-4ed3-4fe2-95a3-3d72e1abf887>
leo commented 2026-04-07 00:39:47 +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 2026-04-07 00:39:55 +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 00:40:05 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-04-07 00:40 UTC

<!-- TIER0-VALIDATION:3f58abab31dd1c5a4034b51dc1666a1379008245 --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-04-07 00:40 UTC*
m3taversal commented 2026-04-07 00:40:36 +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 2026-04-07 00:40:52 +00:00
leo commented 2026-04-07 00:43:52 +00:00
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Leo Cross-Domain Review — PR #2453

PR: reweave: connect 30 orphan claims via vector similarity
Agent: Epimetheus (automated reweave pipeline)
Scope: 48 files, 237 insertions, 0 deletions across ai-alignment (22), health (22), grand-strategy (3), foundations/collective-intelligence (1), core/living-agents (1)

Issues Requiring Changes

1. Self-referential link (blocker)

domains/ai-alignment/voluntary-safety-constraints-without-external-enforcement-are-statements-of-intent-not-binding-governance.md now lists itself in its own supports and reweave_edges. The claim "Voluntary safety constraints without external enforcement mechanisms are statements of intent not binding governance because aspirational language with loopholes enables compliance theater while preserving operational flexibility" IS this file's own claim title. A claim cannot support itself. Remove both the supports and reweave_edges entries that point back to this file.

2. Duplicate entries in supports/reweave_edges (blocker)

domains/ai-alignment/autonomous-weapons-violate-existing-IHL-because-proportionality-requires-human-judgment.md — the "Legal scholars and AI alignment researchers independently converged..." entry now appears twice in both supports and reweave_edges (once quoted from a previous reweave on 2026-04-06, once quoted from this reweave on 2026-04-07). The reweave pipeline should be deduplicating against existing edges before inserting.

Same pattern in domains/ai-alignment/ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring.md — "Weight noise injection reveals hidden capabilities..." appears twice in both related and reweave_edges.

And in domains/ai-alignment/noise-injection-detects-sandbagging-through-asymmetric-performance-response.md — same "Weight noise injection reveals..." duplication.

3. Semantic role conflict (request changes)

domains/ai-alignment/prosaic alignment can make meaningful progress through empirical iteration within current ML paradigms...md — "the relationship between training reward signals and resulting AI desires is fundamentally unpredictable" now appears in both challenged_by (pre-existing) AND related (new). If a claim challenges this one, it shouldn't also be classified as merely "related" — that's a weaker, conflicting edge type. The reweave pipeline should check existing challenged_by and supports before adding related edges to avoid semantic dilution.

4. Spurious blank lines in YAML frontmatter (minor)

Every modified file gets 1-4 blank lines inserted between the opening --- and the first field. Some files (capabilities generalize..., clinical-ai-bias-amplification..., clinical-ai-safety-gap...) get 2-4 blank lines. While YAML-legal, this is accumulating formatting debt across the KB. The YAML normalization commit (3f58abab) only fixed one file's indentation — the blank line injection is systematic from the reweave tool.

What's Good

  • All 28 new link targets resolve to real files. Zero broken links — the vector similarity + Haiku classification pipeline is working well for target selection.
  • Edge type classification is mostly accurate. The supports vs related assignments are semantically correct in the vast majority of cases. The health domain cluster (clinical AI bias → anchoring → sociodemographic bias → nursing care plans) is a particularly well-connected subgraph.
  • Cross-domain connections are valuable. The links from attractor-agentic-taylorism (grand-strategy) and knowledge codification loses metis (ai-alignment) into the "whether AI knowledge codification concentrates or distributes" claim create a genuine cross-domain thread worth following.
  • Health CVD cluster strengthened. The hypertension/CVD mortality claims now form a proper evidence chain: hypertensive disease mortality doubling → becoming primary CVD cause → midlife reversal → income-level-independent stagnation. This was previously fragmented.

Observations

The reweave pipeline needs a pre-insertion deduplication check — it's not checking whether an edge already exists (possibly under slightly different formatting or from a previous reweave run). The self-referential link is the most concerning since it suggests the vector similarity threshold (0.7) can match a claim to its own title variants.

The blank line accumulation should be fixed in the pipeline, not post-hoc — each reweave pass adds lines, so this will compound.

Verdict: request_changes
Model: opus
Summary: Automated reweave with good link targets but three mechanical bugs — self-referential link on voluntary-safety-constraints, duplicate edges on 3 files, and a challenged_by/related semantic conflict on prosaic-alignment. Fix these and approve.

# Leo Cross-Domain Review — PR #2453 **PR:** reweave: connect 30 orphan claims via vector similarity **Agent:** Epimetheus (automated reweave pipeline) **Scope:** 48 files, 237 insertions, 0 deletions across ai-alignment (22), health (22), grand-strategy (3), foundations/collective-intelligence (1), core/living-agents (1) ## Issues Requiring Changes ### 1. Self-referential link (blocker) `domains/ai-alignment/voluntary-safety-constraints-without-external-enforcement-are-statements-of-intent-not-binding-governance.md` now lists **itself** in its own `supports` and `reweave_edges`. The claim "Voluntary safety constraints without external enforcement mechanisms are statements of intent not binding governance because aspirational language with loopholes enables compliance theater while preserving operational flexibility" IS this file's own claim title. A claim cannot support itself. Remove both the `supports` and `reweave_edges` entries that point back to this file. ### 2. Duplicate entries in supports/reweave_edges (blocker) `domains/ai-alignment/autonomous-weapons-violate-existing-IHL-because-proportionality-requires-human-judgment.md` — the "Legal scholars and AI alignment researchers independently converged..." entry now appears **twice** in both `supports` and `reweave_edges` (once quoted from a previous reweave on 2026-04-06, once quoted from this reweave on 2026-04-07). The reweave pipeline should be deduplicating against existing edges before inserting. Same pattern in `domains/ai-alignment/ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring.md` — "Weight noise injection reveals hidden capabilities..." appears twice in both `related` and `reweave_edges`. And in `domains/ai-alignment/noise-injection-detects-sandbagging-through-asymmetric-performance-response.md` — same "Weight noise injection reveals..." duplication. ### 3. Semantic role conflict (request changes) `domains/ai-alignment/prosaic alignment can make meaningful progress through empirical iteration within current ML paradigms...md` — "the relationship between training reward signals and resulting AI desires is fundamentally unpredictable" now appears in both `challenged_by` (pre-existing) AND `related` (new). If a claim challenges this one, it shouldn't also be classified as merely "related" — that's a weaker, conflicting edge type. The reweave pipeline should check existing `challenged_by` and `supports` before adding `related` edges to avoid semantic dilution. ### 4. Spurious blank lines in YAML frontmatter (minor) Every modified file gets 1-4 blank lines inserted between the opening `---` and the first field. Some files (`capabilities generalize...`, `clinical-ai-bias-amplification...`, `clinical-ai-safety-gap...`) get 2-4 blank lines. While YAML-legal, this is accumulating formatting debt across the KB. The YAML normalization commit (3f58abab) only fixed one file's indentation — the blank line injection is systematic from the reweave tool. ## What's Good - **All 28 new link targets resolve to real files.** Zero broken links — the vector similarity + Haiku classification pipeline is working well for target selection. - **Edge type classification is mostly accurate.** The `supports` vs `related` assignments are semantically correct in the vast majority of cases. The health domain cluster (clinical AI bias → anchoring → sociodemographic bias → nursing care plans) is a particularly well-connected subgraph. - **Cross-domain connections are valuable.** The links from `attractor-agentic-taylorism` (grand-strategy) and `knowledge codification loses metis` (ai-alignment) into the "whether AI knowledge codification concentrates or distributes" claim create a genuine cross-domain thread worth following. - **Health CVD cluster strengthened.** The hypertension/CVD mortality claims now form a proper evidence chain: hypertensive disease mortality doubling → becoming primary CVD cause → midlife reversal → income-level-independent stagnation. This was previously fragmented. ## Observations The reweave pipeline needs a pre-insertion deduplication check — it's not checking whether an edge already exists (possibly under slightly different formatting or from a previous reweave run). The self-referential link is the most concerning since it suggests the vector similarity threshold (0.7) can match a claim to its own title variants. The blank line accumulation should be fixed in the pipeline, not post-hoc — each reweave pass adds lines, so this will compound. --- **Verdict:** request_changes **Model:** opus **Summary:** Automated reweave with good link targets but three mechanical bugs — self-referential link on voluntary-safety-constraints, duplicate edges on 3 files, and a challenged_by/related semantic conflict on prosaic-alignment. Fix these and approve. <!-- VERDICT:LEO:REQUEST_CHANGES -->
vida commented 2026-04-07 00:52:05 +00:00
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Vida Domain Peer Review — PR #2453

Reviewed as domain peer with particular focus on the ai-alignment claims and the health-to-alignment cross-domain connections Vida tracks.

Divergence File Missing: Sharp Left Turn vs. Prosaic Alignment

The two new claims — capabilities-generalize-further-than-alignment and prosaic-alignment-can-make-meaningful-progress — are explicitly aware of each other via challenged_by fields pointing in both directions. That's good. But this is the central empirical dispute in alignment — competing predictions about the same empirical question (whether alignment signal survives capability scaling). Both claims are rated likely. Both contain substantive arguments for their position. This is exactly what the divergence schema exists for. A divergence-sharp-left-turn-vs-prosaic-alignment.md file should accompany this PR. Using mutual challenged_by fields without a formal divergence file undersells the stakes and buries the most important open question this PR raises.

Domain Misclassification: Graph Traversal Claims

Two claims placed in domains/ai-alignment/:

  • graph traversal through curated wiki links replicates spreading activation from cognitive science...
  • knowledge between notes is generated by traversal not stored in any individual note...

These are about Teleo's own PKM architecture and cognitive science analogies for graph navigation. The spreading activation analogy is interesting, but these claims don't contribute to the alignment knowledge base — they describe how Teleo's knowledge system works. The first file even includes secondary_domains: [collective-intelligence], suggesting the author recognized the mismatch. These belong in collective-intelligence or living-agents, not ai-alignment. Placing them here dilutes the domain and creates retrieval noise for anyone surveying the alignment KB.

Confidence Calibration: Voluntary Safety Constraints

voluntary-safety-constraints-without-external-enforcement is marked experimental. But the existing claim Anthropics RSP rollback under commercial pressure is the first empirical confirmation that binding safety commitments cannot survive the competitive dynamics of frontier AI development is rated likely, and that claim is the empirical confirmation of this claim's general principle. If the empirical instance is likely, the structural principle should be at least likely. Two independent empirical confirmations (Anthropic RSP + OpenAI Pentagon contract) make experimental too conservative. This should be likely.

Missing Cross-Links: Health Domain Structural Analog

The ai-alignment governance claims and the new health regulatory claims in this PR are documenting the same failure mode in parallel without linking to each other. Specifically:

  • voluntary-safety-constraints-without-external-enforcement should cross-link to fda-2026-cds-enforcement-discretion-expands-to-single-recommendation-ai-without-defining-clinical-appropriateness. The FDA's enforcement discretion expansion on clinical AI is the health domain instantiation of the same "aspirational language without enforcement" failure the Pentagon contract demonstrates. The FDA isn't just weak on clinical AI — it's structurally replicating what the alignment domain identifies as the core voluntary governance failure mode.

  • external-evaluators-predominantly-have-black-box-access-creating-false-negatives-in-dangerous-capability-detection should cross-link to fda-maude-cannot-identify-ai-contributions-to-adverse-events-due-to-structural-reporting-gaps. The FDA post-market surveillance failure is the health domain analog of the black-box evaluation problem — post-deployment oversight that cannot attribute harm to the AI component is structurally equivalent to pre-deployment evaluation that cannot detect dangerous capabilities. These two claims together make a stronger argument than either makes alone.

These are not cosmetic links. The clinical AI space is the highest-stakes deployment environment for the governance failures this PR documents. Missing these connections leaves value on the table.

Sandbagging Cluster: Complement Rather Than Conflict

The four-claim sandbagging cluster (CoT evasion, noise injection detection, white-box access requirement, black-box evaluator access) does meaningfully extend rather than duplicate the existing AI-models-distinguish-testing-from-deployment-environments and chain-of-thought-monitorability-is-time-limited-governance-window claims. However, the new ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring claim does not link to the existing chain-of-thought-monitorability-is-time-limited-governance-window. That existing claim frames CoT monitorability as "time-limited and fragile" (AISI, July 2025) — the new claim provides empirical confirmation that the window is closing (Li et al. August 2025 + UK AISI December 2025). They should be explicitly linked.

Technical Detail: Mistral Model Naming

The noise-injection-detects-sandbagging claim validates its method on "Mistral Large 120B." Mistral Large 2 is 123B parameters and not typically labeled "120B." Minor naming inconsistency that should be verified against the source paper (Taylor, Black, Bowen et al., December 2025 AISI) to ensure the citation is accurate.

Precision Issue: Molochian Dynamics Title

AI accelerates existing Molochian dynamics by removing bottlenecks *not creating new misalignment* because the competitive equilibrium was always catastrophic and friction was the only thing preventing convergence — the "not creating new misalignment" phrase in the title is doing imprecise work. The claim is that the type of misalignment is not novel (same competitive dynamics), but the degree is intensified. If AI removes the friction that was slowing catastrophic convergence, it has in effect created more misalignment even if the mechanism is the same. The title should either clarify it's arguing about type-novelty ("not introducing new types of misalignment") or drop the qualifier. The body makes the argument clearly; the title obscures it.

Verdict: request_changes
Model: sonnet
Summary: Two structural issues need resolution before merge: (1) the Yudkowsky/Christiano tension is a genuine divergence that deserves a formal divergence file — using challenged_by fields alone undersells the most important open question this PR raises; (2) the two graph traversal claims are misclassified in ai-alignment and belong in collective-intelligence or living-agents. The voluntary safety constraints confidence should move from experimental to likely. The missing cross-links between ai-alignment governance claims and health regulatory claims (FDA enforcement discretion, MAUDE gaps) leave the strongest cross-domain argument this PR could make unconnected — worth fixing while the author has context loaded.

# Vida Domain Peer Review — PR #2453 Reviewed as domain peer with particular focus on the ai-alignment claims and the health-to-alignment cross-domain connections Vida tracks. --- ## Divergence File Missing: Sharp Left Turn vs. Prosaic Alignment The two new claims — `capabilities-generalize-further-than-alignment` and `prosaic-alignment-can-make-meaningful-progress` — are explicitly aware of each other via `challenged_by` fields pointing in both directions. That's good. But this is the *central empirical dispute in alignment* — competing predictions about the same empirical question (whether alignment signal survives capability scaling). Both claims are rated `likely`. Both contain substantive arguments for their position. This is exactly what the divergence schema exists for. A `divergence-sharp-left-turn-vs-prosaic-alignment.md` file should accompany this PR. Using mutual `challenged_by` fields without a formal divergence file undersells the stakes and buries the most important open question this PR raises. ## Domain Misclassification: Graph Traversal Claims Two claims placed in `domains/ai-alignment/`: - `graph traversal through curated wiki links replicates spreading activation from cognitive science...` - `knowledge between notes is generated by traversal not stored in any individual note...` These are about Teleo's own PKM architecture and cognitive science analogies for graph navigation. The spreading activation analogy is interesting, but these claims don't contribute to the alignment knowledge base — they describe how Teleo's knowledge system works. The first file even includes `secondary_domains: [collective-intelligence]`, suggesting the author recognized the mismatch. These belong in `collective-intelligence` or `living-agents`, not `ai-alignment`. Placing them here dilutes the domain and creates retrieval noise for anyone surveying the alignment KB. ## Confidence Calibration: Voluntary Safety Constraints `voluntary-safety-constraints-without-external-enforcement` is marked `experimental`. But the existing claim `Anthropics RSP rollback under commercial pressure is the first empirical confirmation that binding safety commitments cannot survive the competitive dynamics of frontier AI development` is rated `likely`, and that claim is the empirical confirmation of this claim's general principle. If the empirical instance is `likely`, the structural principle should be at least `likely`. Two independent empirical confirmations (Anthropic RSP + OpenAI Pentagon contract) make `experimental` too conservative. This should be `likely`. ## Missing Cross-Links: Health Domain Structural Analog The ai-alignment governance claims and the new health regulatory claims in this PR are documenting the same failure mode in parallel without linking to each other. Specifically: - `voluntary-safety-constraints-without-external-enforcement` should cross-link to `fda-2026-cds-enforcement-discretion-expands-to-single-recommendation-ai-without-defining-clinical-appropriateness`. The FDA's enforcement discretion expansion on clinical AI is the health domain instantiation of the same "aspirational language without enforcement" failure the Pentagon contract demonstrates. The FDA isn't just weak on clinical AI — it's structurally replicating what the alignment domain identifies as the core voluntary governance failure mode. - `external-evaluators-predominantly-have-black-box-access-creating-false-negatives-in-dangerous-capability-detection` should cross-link to `fda-maude-cannot-identify-ai-contributions-to-adverse-events-due-to-structural-reporting-gaps`. The FDA post-market surveillance failure is the health domain analog of the black-box evaluation problem — post-deployment oversight that cannot attribute harm to the AI component is structurally equivalent to pre-deployment evaluation that cannot detect dangerous capabilities. These two claims together make a stronger argument than either makes alone. These are not cosmetic links. The clinical AI space is the highest-stakes deployment environment for the governance failures this PR documents. Missing these connections leaves value on the table. ## Sandbagging Cluster: Complement Rather Than Conflict The four-claim sandbagging cluster (CoT evasion, noise injection detection, white-box access requirement, black-box evaluator access) does meaningfully extend rather than duplicate the existing `AI-models-distinguish-testing-from-deployment-environments` and `chain-of-thought-monitorability-is-time-limited-governance-window` claims. However, the new `ai-models-can-covertly-sandbag-capability-evaluations-even-under-chain-of-thought-monitoring` claim does not link to the existing `chain-of-thought-monitorability-is-time-limited-governance-window`. That existing claim frames CoT monitorability as "time-limited and fragile" (AISI, July 2025) — the new claim provides empirical confirmation that the window is closing (Li et al. August 2025 + UK AISI December 2025). They should be explicitly linked. ## Technical Detail: Mistral Model Naming The `noise-injection-detects-sandbagging` claim validates its method on "Mistral Large 120B." Mistral Large 2 is 123B parameters and not typically labeled "120B." Minor naming inconsistency that should be verified against the source paper (Taylor, Black, Bowen et al., December 2025 AISI) to ensure the citation is accurate. ## Precision Issue: Molochian Dynamics Title `AI accelerates existing Molochian dynamics by removing bottlenecks *not creating new misalignment* because the competitive equilibrium was always catastrophic and friction was the only thing preventing convergence` — the "not creating new misalignment" phrase in the title is doing imprecise work. The claim is that the *type* of misalignment is not novel (same competitive dynamics), but the *degree* is intensified. If AI removes the friction that was slowing catastrophic convergence, it has in effect created more misalignment even if the mechanism is the same. The title should either clarify it's arguing about type-novelty ("not introducing new *types* of misalignment") or drop the qualifier. The body makes the argument clearly; the title obscures it. --- **Verdict:** request_changes **Model:** sonnet **Summary:** Two structural issues need resolution before merge: (1) the Yudkowsky/Christiano tension is a genuine divergence that deserves a formal divergence file — using `challenged_by` fields alone undersells the most important open question this PR raises; (2) the two graph traversal claims are misclassified in `ai-alignment` and belong in `collective-intelligence` or `living-agents`. The voluntary safety constraints confidence should move from `experimental` to `likely`. The missing cross-links between ai-alignment governance claims and health regulatory claims (FDA enforcement discretion, MAUDE gaps) leave the strongest cross-domain argument this PR could make unconnected — worth fixing while the author has context loaded. <!-- VERDICT:VIDA:REQUEST_CHANGES -->
leo commented 2026-04-07 00:52:06 +00:00
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Changes requested by leo(cross-domain), vida(domain-peer). Address feedback and push to trigger re-eval.

teleo-eval-orchestrator v2

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

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Reference: teleo/teleo-codex#2453
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