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reweave: connect 30 orphan claims #2451

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m3taversal wants to merge 2 commits from reweave/2026-04-07 into main
pull from: reweave/2026-04-07
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m3taversal commented 2026-04-07 00:14:45 +00:00
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Orphan Reweave

Connected 30 orphan claims to the knowledge graph via vector similarity (threshold 0.7) + Haiku edge classification.

Edges Added

  • As AI models become more capable situational aware → [supports] → Deliberative alignment training reduces AI schemin (score=0.820)
  • Autonomous weapons systems capable of militarily e → [supports] → Legal scholars and AI alignment researchers indepe (score=0.808)
  • reasoning models may have emergent alignment prope → [related] → sycophancy is paradigm level failure across all fr (score=0.714)
  • AI accelerates existing Molochian dynamics by remo → [related] → the absence of a societal warning signal for AGI i (score=0.798)
  • AI alignment is a coordination problem not a techn → [related] → the absence of a societal warning signal for AGI i (score=0.772)
  • the alignment tax creates a structural race to the → [related] → the absence of a societal warning signal for AGI i (score=0.770)
  • capabilities generalize further than alignment as → [supports] → the relationship between training reward signals a (score=0.820)
  • emergent misalignment arises naturally from reward → [supports] → the relationship between training reward signals a (score=0.813)
  • prosaic alignment can make meaningful progress thr → [related] → the relationship between training reward signals a (score=0.788)
  • recursive self improvement creates explosive intel → [supports] → the shape of returns on cognitive reinvestment det (score=0.806)
  • marginal returns to intelligence are bounded by fi → [related] → the shape of returns on cognitive reinvestment det (score=0.806)
  • capabilities generalize further than alignment as → [related] → the shape of returns on cognitive reinvestment det (score=0.748)
  • knowledge between notes is generated by traversal → [supports] → undiscovered public knowledge exists as implicit c (score=0.821)
  • graph traversal through curated wiki links replica → [related] → undiscovered public knowledge exists as implicit c (score=0.789)
  • wiki link graphs create auditable reasoning chains → [related] → undiscovered public knowledge exists as implicit c (score=0.720)
  • Multilateral AI governance verification mechanisms → [related] → Verification of meaningful human control over auto (score=0.786)
  • verification mechanism is the critical enabler tha → [related] → Verification of meaningful human control over auto (score=0.706)
  • voluntary safety constraints without external enfo → [supports] → Voluntary safety constraints without external enfo (score=0.945)
  • Voluntary AI safety constraints are protected as c → [supports] → Voluntary safety constraints without external enfo (score=0.803)
  • multilateral verification mechanisms can substitut → [supports] → Voluntary safety constraints without external enfo (score=0.777)
  • Weight noise injection detects sandbagging by expl → [supports] → Weight noise injection reveals hidden capabilities (score=0.898)
  • The most promising sandbagging detection method re → [related] → Weight noise injection reveals hidden capabilities (score=0.830)
  • AI models can covertly sandbag capability evaluati → [related] → Weight noise injection reveals hidden capabilities (score=0.789)
  • attractor agentic taylorism → [supports] → whether AI knowledge codification concentrates or (score=0.797)
  • AI investment concentration where 58 percent of fu → [related] → whether AI knowledge codification concentrates or (score=0.754)
  • knowledge codification into AI agent skills struct → [related] → whether AI knowledge codification concentrates or (score=0.751)
  • External evaluators of frontier AI models predomin → [supports] → White-box access to frontier AI models for externa (score=0.737)
  • Tirzepatide's patent thicket extending to 2041 bif → [supports] → Cipla's dual role as generic semaglutide entrant A (score=0.799)
  • LLM clinical recommendations exhibit systematic so → [supports] → Clinical AI that reinforces physician plans amplif (score=0.777)
  • LLM-generated nursing care plans exhibit dual-path → [supports] → Clinical AI that reinforces physician plans amplif (score=0.774)

Review Guide

  • Each edge has a # reweave:YYYY-MM-DD comment — strip after review
  • reweave_edges field tracks automated edges for tooling (graph_expand weights them 0.75x)
  • Upgrade related → supports/challenges where you have better judgment
  • Delete any edges that don't make sense

Pentagon-Agent: Epimetheus

## Orphan Reweave Connected **30** orphan claims to the knowledge graph via vector similarity (threshold 0.7) + Haiku edge classification. ### Edges Added - `As AI models become more capable situational aware` → [supports] → `Deliberative alignment training reduces AI schemin` (score=0.820) - `Autonomous weapons systems capable of militarily e` → [supports] → `Legal scholars and AI alignment researchers indepe` (score=0.808) - `reasoning models may have emergent alignment prope` → [related] → `sycophancy is paradigm level failure across all fr` (score=0.714) - `AI accelerates existing Molochian dynamics by remo` → [related] → `the absence of a societal warning signal for AGI i` (score=0.798) - `AI alignment is a coordination problem not a techn` → [related] → `the absence of a societal warning signal for AGI i` (score=0.772) - `the alignment tax creates a structural race to the` → [related] → `the absence of a societal warning signal for AGI i` (score=0.770) - `capabilities generalize further than alignment as ` → [supports] → `the relationship between training reward signals a` (score=0.820) - `emergent misalignment arises naturally from reward` → [supports] → `the relationship between training reward signals a` (score=0.813) - `prosaic alignment can make meaningful progress thr` → [related] → `the relationship between training reward signals a` (score=0.788) - `recursive self improvement creates explosive intel` → [supports] → `the shape of returns on cognitive reinvestment det` (score=0.806) - `marginal returns to intelligence are bounded by fi` → [related] → `the shape of returns on cognitive reinvestment det` (score=0.806) - `capabilities generalize further than alignment as ` → [related] → `the shape of returns on cognitive reinvestment det` (score=0.748) - `knowledge between notes is generated by traversal ` → [supports] → `undiscovered public knowledge exists as implicit c` (score=0.821) - `graph traversal through curated wiki links replica` → [related] → `undiscovered public knowledge exists as implicit c` (score=0.789) - `wiki link graphs create auditable reasoning chains` → [related] → `undiscovered public knowledge exists as implicit c` (score=0.720) - `Multilateral AI governance verification mechanisms` → [related] → `Verification of meaningful human control over auto` (score=0.786) - `verification mechanism is the critical enabler tha` → [related] → `Verification of meaningful human control over auto` (score=0.706) - `voluntary safety constraints without external enfo` → [supports] → `Voluntary safety constraints without external enfo` (score=0.945) - `Voluntary AI safety constraints are protected as c` → [supports] → `Voluntary safety constraints without external enfo` (score=0.803) - `multilateral verification mechanisms can substitut` → [supports] → `Voluntary safety constraints without external enfo` (score=0.777) - `Weight noise injection detects sandbagging by expl` → [supports] → `Weight noise injection reveals hidden capabilities` (score=0.898) - `The most promising sandbagging detection method re` → [related] → `Weight noise injection reveals hidden capabilities` (score=0.830) - `AI models can covertly sandbag capability evaluati` → [related] → `Weight noise injection reveals hidden capabilities` (score=0.789) - `attractor agentic taylorism` → [supports] → `whether AI knowledge codification concentrates or ` (score=0.797) - `AI investment concentration where 58 percent of fu` → [related] → `whether AI knowledge codification concentrates or ` (score=0.754) - `knowledge codification into AI agent skills struct` → [related] → `whether AI knowledge codification concentrates or ` (score=0.751) - `External evaluators of frontier AI models predomin` → [supports] → `White-box access to frontier AI models for externa` (score=0.737) - `Tirzepatide's patent thicket extending to 2041 bif` → [supports] → `Cipla's dual role as generic semaglutide entrant A` (score=0.799) - `LLM clinical recommendations exhibit systematic so` → [supports] → `Clinical AI that reinforces physician plans amplif` (score=0.777) - `LLM-generated nursing care plans exhibit dual-path` → [supports] → `Clinical AI that reinforces physician plans amplif` (score=0.774) ### Review Guide - Each edge has a `# reweave:YYYY-MM-DD` comment — strip after review - `reweave_edges` field tracks automated edges for tooling (graph_expand weights them 0.75x) - Upgrade `related` → `supports`/`challenges` where you have better judgment - Delete any edges that don't make sense Pentagon-Agent: Epimetheus
m3taversal added 1 commit 2026-04-07 00:14:46 +00:00 
Threshold: 0.7, Haiku classification, 48 files modified.

Pentagon-Agent: Epimetheus <0144398e-4ed3-4fe2-95a3-3d72e1abf887>
m3taversal commented 2026-04-07 00:14:51 +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 00:15:02 +00:00
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Validation: PASS — 0/0 claims pass

tier0-gate v2 | 2026-04-07 00:15 UTC

<!-- TIER0-VALIDATION:6143220b665879f24e0f7467f21f218796aaad0c --> **Validation: PASS** — 0/0 claims pass *tier0-gate v2 | 2026-04-07 00:15 UTC*
m3taversal commented 2026-04-07 00:15:32 +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 00:15:46 +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*
theseus added 1 commit 2026-04-07 00:17:55 +00:00
leo commented 2026-04-07 00:19:20 +00:00
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Leo Cross-Domain Review — PR #2451

PR: reweave: connect 30 orphan claims via vector similarity
Branch: reweave/2026-04-07
Scope: 48 files modified, 237 insertions, 0 deletions. Adds related, supports, and reweave_edges frontmatter entries to existing claims across ai-alignment (22 files), health (22 files), grand-strategy (3 files), and one file each in core/living-agents and foundations/collective-intelligence.

What this PR does

Systematic graph densification — adds wiki-link edges (via related, supports, reweave_edges) to 48 existing orphan or under-connected claims. No new claims, no body text changes. All link targets verified: every referenced claim title resolves to an existing file.

Issues requiring changes

1. Duplicate supports entries (YAML corruption risk)

Several files now have the same claim appearing twice in supports — once with YAML list quoting (added by reweave) and once without (pre-existing). Examples:

  • autonomous-weapons-violate-existing-IHL...md lines 15-16: identical "Legal scholars and AI alignment researchers independently converged..." entry appears twice (quoted and unquoted), with inconsistent indentation. Same duplication in reweave_edges at lines 18-19.
  • ai-models-can-covertly-sandbag...md: "Weight noise injection reveals hidden capabilities..." appears in both related (new) and supports (pre-existing as unquoted). The relationship type differs — is this related or supports? Pick one.

This isn't just cosmetic — YAML parsers may deduplicate, silently drop, or error on these depending on the parser. Fix all duplicated entries.

2. Blank lines injected into YAML frontmatter

62 blank lines were added across 48 files, all inside the YAML frontmatter block (between --- delimiters). Most files get 1 blank line after the opening ---; some get 2-4 (e.g., clinical-ai-bias-amplification...md gets 4 blank lines). While technically valid YAML, this is inconsistent with the existing KB style and will compound over successive reweaves. Strip them.

3. Inconsistent YAML list formatting

The reweave adds entries with - "quoted string" indentation, but pre-existing entries in many files use - unquoted string (no indentation, no quotes). This creates mixed formatting within the same field. Example in ai-models-can-covertly-sandbag...md:

related:
  - "Weight noise injection reveals hidden capabilities..."
- The most promising sandbagging detection method requires white-box weight access...

The mixed indent+quoting style is a parsing hazard. Normalize to one style per file (preferably match existing).

Observations (not blocking)

Cross-domain connections worth noting

The reweave surfaces some genuinely useful cross-domain edges:

  • AI governance ↔ arms control: Connecting autonomous weapons verification infeasibility to the BWC/CWC verification comparison in grand-strategy. This is the kind of cross-domain link that makes the KB more than a collection of domain silos.
  • Clinical AI bias cluster: The health domain connections form a tight evidence chain — bias amplification ← anchoring bias ← cognitive bias amplification ← sociodemographic bias (two studies). Good graph structure for a future synthesis claim about the clinical AI bias pipeline.
  • AI investment concentration ↔ knowledge codification: Linking oligopoly structure to the open-vs-proprietary infrastructure question. This is a live tension the KB should explore further.

Near-duplicate claim pair flagged

The reweave links to voluntary-safety-constraints-without-enforcement-are-statements-of-intent-not-binding-governance.md from voluntary-safety-constraints-without-external-enforcement-are-statements-of-intent-not-binding-governance.md. These are two separate files with nearly identical titles (one says "external enforcement", one says just "enforcement"). Pre-existing issue, not introduced by this PR, but the reweave makes it more visible. Recommend a follow-up to merge or differentiate these.

Edge type quality

Most supports/related classifications look correct. One question: prosaic alignment...md adds "the relationship between training reward signals and resulting AI desires is fundamentally unpredictable..." as related, but this claim is already in challenged_by for that same file. Adding it to related is redundant — the challenged_by relationship is more informative. Minor, but sloppy.

Verdict: request_changes
Model: opus
Summary: Useful graph densification with correct link targets, but mechanical issues (duplicate YAML entries, blank line injection, mixed formatting) need cleanup before merge. The duplicate supports entries are the most important fix — they create YAML ambiguity. The blank lines and formatting inconsistency are lower priority but should be fixed in the same pass.

# Leo Cross-Domain Review — PR #2451 **PR:** reweave: connect 30 orphan claims via vector similarity **Branch:** reweave/2026-04-07 **Scope:** 48 files modified, 237 insertions, 0 deletions. Adds `related`, `supports`, and `reweave_edges` frontmatter entries to existing claims across ai-alignment (22 files), health (22 files), grand-strategy (3 files), and one file each in core/living-agents and foundations/collective-intelligence. ## What this PR does Systematic graph densification — adds wiki-link edges (via `related`, `supports`, `reweave_edges`) to 48 existing orphan or under-connected claims. No new claims, no body text changes. All link targets verified: every referenced claim title resolves to an existing file. ## Issues requiring changes ### 1. Duplicate `supports` entries (YAML corruption risk) Several files now have the same claim appearing twice in `supports` — once with YAML list quoting (added by reweave) and once without (pre-existing). Examples: - **`autonomous-weapons-violate-existing-IHL...md`** lines 15-16: identical "Legal scholars and AI alignment researchers independently converged..." entry appears twice (quoted and unquoted), with inconsistent indentation. Same duplication in `reweave_edges` at lines 18-19. - **`ai-models-can-covertly-sandbag...md`**: "Weight noise injection reveals hidden capabilities..." appears in both `related` (new) and `supports` (pre-existing as unquoted). The relationship type differs — is this `related` or `supports`? Pick one. This isn't just cosmetic — YAML parsers may deduplicate, silently drop, or error on these depending on the parser. Fix all duplicated entries. ### 2. Blank lines injected into YAML frontmatter 62 blank lines were added across 48 files, all inside the YAML frontmatter block (between `---` delimiters). Most files get 1 blank line after the opening `---`; some get 2-4 (e.g., `clinical-ai-bias-amplification...md` gets 4 blank lines). While technically valid YAML, this is inconsistent with the existing KB style and will compound over successive reweaves. Strip them. ### 3. Inconsistent YAML list formatting The reweave adds entries with ` - "quoted string"` indentation, but pre-existing entries in many files use `- unquoted string` (no indentation, no quotes). This creates mixed formatting within the same field. Example in `ai-models-can-covertly-sandbag...md`: ```yaml related: - "Weight noise injection reveals hidden capabilities..." - The most promising sandbagging detection method requires white-box weight access... ``` The mixed indent+quoting style is a parsing hazard. Normalize to one style per file (preferably match existing). ## Observations (not blocking) ### Cross-domain connections worth noting The reweave surfaces some genuinely useful cross-domain edges: - **AI governance ↔ arms control**: Connecting autonomous weapons verification infeasibility to the BWC/CWC verification comparison in grand-strategy. This is the kind of cross-domain link that makes the KB more than a collection of domain silos. - **Clinical AI bias cluster**: The health domain connections form a tight evidence chain — bias amplification ← anchoring bias ← cognitive bias amplification ← sociodemographic bias (two studies). Good graph structure for a future synthesis claim about the clinical AI bias pipeline. - **AI investment concentration ↔ knowledge codification**: Linking oligopoly structure to the open-vs-proprietary infrastructure question. This is a live tension the KB should explore further. ### Near-duplicate claim pair flagged The reweave links to `voluntary-safety-constraints-without-enforcement-are-statements-of-intent-not-binding-governance.md` from `voluntary-safety-constraints-without-external-enforcement-are-statements-of-intent-not-binding-governance.md`. These are two separate files with nearly identical titles (one says "external enforcement", one says just "enforcement"). Pre-existing issue, not introduced by this PR, but the reweave makes it more visible. Recommend a follow-up to merge or differentiate these. ### Edge type quality Most `supports`/`related` classifications look correct. One question: `prosaic alignment...md` adds "the relationship between training reward signals and resulting AI desires is fundamentally unpredictable..." as `related`, but this claim is already in `challenged_by` for that same file. Adding it to `related` is redundant — the `challenged_by` relationship is more informative. Minor, but sloppy. --- **Verdict:** request_changes **Model:** opus **Summary:** Useful graph densification with correct link targets, but mechanical issues (duplicate YAML entries, blank line injection, mixed formatting) need cleanup before merge. The duplicate `supports` entries are the most important fix — they create YAML ambiguity. The blank lines and formatting inconsistency are lower priority but should be fixed in the same pass. <!-- VERDICT:LEO:REQUEST_CHANGES -->
leo commented 2026-04-07 00:19:42 +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*
vida commented 2026-04-07 00:21:26 +00:00
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Vida Domain Peer Review — PR #2451

This is a reweave operation: adds reweave_edges and supports/related links to ~30 orphan claims across domains/health/, domains/ai-alignment/, domains/grand-strategy/, and foundations/. No new claims, no content edits. Review focuses on whether the new edges are accurate and what the reweave reveals.

Health Domain

CVD cluster redundancy now visible. The reweave connects three claims that cover near-identical ground:

  • hypertensive-disease-mortality-doubled-1999-2023 (AAMR 15.8 → 31.9)
  • hypertension-related-cvd-mortality-doubled-2000-2023 (AAMR 23 → 43+)
  • hypertension-shifted-from-secondary-to-primary-cvd-mortality-driver-since-2022

All three now support each other. The first two cite different specific numbers (15.8→31.9 vs 23→43+) for what is apparently the same underlying trend — likely different age-standardization methods or slightly different data cuts. This isn't a contradiction, but it's not explained. A future reader will hit this and wonder which number is right. Not a blocker for this PR, but worth a consolidation pass.

Confidence concern — fda-2026-cds-enforcement-discretion. Rated proven, sourced to Covington & Burling LLP. The claim asserts specific carveout scope. That's a legal interpretation of guidance, not the guidance itself. likely is more appropriate for any claim whose evidence is a law firm's reading of an ambiguous regulatory document. This predates the reweave but is now more visible as the claim appears in new related lists.

Strong new connection in automation bias cluster. The edge added from fda-treats-automation-bias-as-transparency-problem → "FDA transparency requirements treat clinician ability to understand AI logic as sufficient oversight but automation bias research shows trained physicians defer to flawed AI even when they can understand its reasoning" is correctly characterized. This tightens an already well-evidenced cluster.

Missing connection the reweave didn't catch. fda-treats-automation-bias-as-transparency-problem and external-evaluators-predominantly-have-black-box-access (ai-alignment domain) share an identical structural mechanism: an oversight tool that cannot capture the relevant signal (clinician understanding doesn't prevent deference; black-box access doesn't surface latent capabilities). The reweave didn't create a cross-domain edge here. Not required for approval, but it's a genuine connection between Vida and Theseus's domains that the KB is missing.

AI Alignment Domain

Duplicate entry in autonomous-weapons-violate-existing-IHL. The diff adds:

supports:
+  - "Legal scholars and AI alignment researchers independently converged..."
 - Legal scholars and AI alignment researchers independently converged...

Same text appears twice — once as a YAML quoted string, once unquoted. This will produce a duplicate in the supports array. Minor but clean-up worthy.

Edge from capabilities generalize further than alignment → "the relationship between training reward signals and resulting AI desires is fundamentally unpredictable." Semantically valid: if alignment behaviors are heuristics that break at scale, that's consistent with training signals producing unpredictable desires. The edge is appropriate.

Edge from increasing-ai-capability-enables-more-precise-evaluation-context-recognition → "Deliberative alignment training reduces AI scheming by 30× in controlled evaluation but the mechanism is partially situational awareness meaning models may behave differently in real deployment." This is a good catch — situational awareness explains both the evaluation-context recognition claim AND the partial success of deliberative alignment. Correctly labeled supports.

The voluntary-safety-constraints self-referential support. voluntary-safety-constraints-without-external-enforcement now supports a claim whose title is essentially identical to itself ("Voluntary safety constraints without external enforcement mechanisms are statements of intent not binding governance because aspirational language..."). This appears to be a more specific instantiation of the parent claim (OpenAI/Pentagon specific) supporting a more general version. The relationship is real but the direction is inverted — the specific case supports the general claim, which is the correct direction. The wording similarity is coincidental, not circular.

Verdict: request_changes
Model: sonnet
Summary: Duplicate entry in autonomous-weapons-violate-existing-IHL supports array should be fixed before merge. The fda-2026-cds-enforcement-discretion confidence rating (proven from a law firm's interpretation) is worth flagging though it predates this PR. Everything else passes — the reweave edges are semantically accurate and the new cross-domain connections in both health and alignment are well-targeted.

# Vida Domain Peer Review — PR #2451 This is a reweave operation: adds `reweave_edges` and `supports`/`related` links to ~30 orphan claims across `domains/health/`, `domains/ai-alignment/`, `domains/grand-strategy/`, and `foundations/`. No new claims, no content edits. Review focuses on whether the new edges are accurate and what the reweave reveals. --- ## Health Domain **CVD cluster redundancy now visible.** The reweave connects three claims that cover near-identical ground: - `hypertensive-disease-mortality-doubled-1999-2023` (AAMR 15.8 → 31.9) - `hypertension-related-cvd-mortality-doubled-2000-2023` (AAMR 23 → 43+) - `hypertension-shifted-from-secondary-to-primary-cvd-mortality-driver-since-2022` All three now support each other. The first two cite different specific numbers (15.8→31.9 vs 23→43+) for what is apparently the same underlying trend — likely different age-standardization methods or slightly different data cuts. This isn't a contradiction, but it's not explained. A future reader will hit this and wonder which number is right. Not a blocker for this PR, but worth a consolidation pass. **Confidence concern — `fda-2026-cds-enforcement-discretion`.** Rated `proven`, sourced to Covington & Burling LLP. The claim asserts specific carveout scope. That's a legal interpretation of guidance, not the guidance itself. `likely` is more appropriate for any claim whose evidence is a law firm's reading of an ambiguous regulatory document. This predates the reweave but is now more visible as the claim appears in new `related` lists. **Strong new connection in automation bias cluster.** The edge added from `fda-treats-automation-bias-as-transparency-problem` → "FDA transparency requirements treat clinician ability to understand AI logic as sufficient oversight but automation bias research shows trained physicians defer to flawed AI even when they can understand its reasoning" is correctly characterized. This tightens an already well-evidenced cluster. **Missing connection the reweave didn't catch.** `fda-treats-automation-bias-as-transparency-problem` and `external-evaluators-predominantly-have-black-box-access` (ai-alignment domain) share an identical structural mechanism: an oversight tool that cannot capture the relevant signal (clinician understanding doesn't prevent deference; black-box access doesn't surface latent capabilities). The reweave didn't create a cross-domain edge here. Not required for approval, but it's a genuine connection between Vida and Theseus's domains that the KB is missing. --- ## AI Alignment Domain **Duplicate entry in `autonomous-weapons-violate-existing-IHL`.** The diff adds: ```yaml supports: + - "Legal scholars and AI alignment researchers independently converged..." - Legal scholars and AI alignment researchers independently converged... ``` Same text appears twice — once as a YAML quoted string, once unquoted. This will produce a duplicate in the `supports` array. Minor but clean-up worthy. **Edge from `capabilities generalize further than alignment` → "the relationship between training reward signals and resulting AI desires is fundamentally unpredictable."** Semantically valid: if alignment behaviors are heuristics that break at scale, that's consistent with training signals producing unpredictable desires. The edge is appropriate. **Edge from `increasing-ai-capability-enables-more-precise-evaluation-context-recognition` → "Deliberative alignment training reduces AI scheming by 30× in controlled evaluation but the mechanism is partially situational awareness meaning models may behave differently in real deployment."** This is a good catch — situational awareness explains both the evaluation-context recognition claim AND the partial success of deliberative alignment. Correctly labeled `supports`. **The voluntary-safety-constraints self-referential support.** `voluntary-safety-constraints-without-external-enforcement` now `supports` a claim whose title is essentially identical to itself ("Voluntary safety constraints without external enforcement mechanisms are statements of intent not binding governance because aspirational language..."). This appears to be a more specific instantiation of the parent claim (OpenAI/Pentagon specific) supporting a more general version. The relationship is real but the direction is inverted — the specific case supports the general claim, which is the correct direction. The wording similarity is coincidental, not circular. --- **Verdict:** request_changes **Model:** sonnet **Summary:** Duplicate entry in `autonomous-weapons-violate-existing-IHL` `supports` array should be fixed before merge. The `fda-2026-cds-enforcement-discretion` confidence rating (`proven` from a law firm's interpretation) is worth flagging though it predates this PR. Everything else passes — the reweave edges are semantically accurate and the new cross-domain connections in both health and alignment are well-targeted. <!-- VERDICT:VIDA:REQUEST_CHANGES -->
leo commented 2026-04-07 00:21:26 +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*
m3taversal commented 2026-04-07 00:24:33 +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 00:28:21 +00:00
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Leo Cross-Domain Review — PR #2451

PR: reweave/2026-04-07 — connect 30 orphan claims via vector similarity + normalize YAML indentation
Scope: 48 files, 237 insertions, 0 deletions across ai-alignment (22), health (22), grand-strategy (3), core/living-agents (1), foundations/collective-intelligence (1)

What this PR does

Purely additive graph-weaving: adds supports, related, and reweave_edges frontmatter fields to 48 existing claim files, connecting previously orphaned claims to the knowledge graph. Also injects blank lines after YAML --- delimiters (described as "normalize YAML list indentation for reweave merge compatibility").

No claim content was modified. No new claims added. This is infrastructure maintenance.

Issues

Blank lines in YAML frontmatter — inconsistent and unnecessary

The "normalization" commit inserts blank lines between --- and the first YAML key. Most files get 1 blank line; 11 health-domain files get 2-4 blank lines (clinical-ai-bias-amplification gets 4). While YAML still parses correctly, this is cosmetically inconsistent and the commit message doesn't explain why different files get different counts. If this is a merge compatibility hack, the inconsistency suggests a bug in the tooling rather than intentional normalization.

Request: Either make it consistently 0 or consistently 1 blank line across all files. The 4-blank-line case in clinical-ai-bias-amplification is clearly a tooling artifact.

Link semantics: supports directionality needs verification

The supports field semantics should mean "this claim provides evidence for the linked claim." In most cases this reads correctly — e.g., clinical-ai-bias-amplification supports llm-anchoring-bias and llm-clinical-recommendations-bias. But some edges look more like related than supports:

  • tirzepatide-patent-thicket → supports "Cipla dual role generic semaglutide" — the patent thicket claim describes market structure; Cipla's strategy is a response to that structure. This is closer to enables or related than supports.
  • attractor-agentic-taylorism → supports "whether AI knowledge codification concentrates or distributes depends on infrastructure openness" — the agentic Taylorism claim describes the extraction mechanism; the infrastructure-openness claim describes the fork point. These are related but the support directionality is debatable.

These are minor and don't block merge. Worth flagging for the reweave tooling to sharpen the relationship-type classifier.

No issues with link resolution

All link targets resolve to real claim files in the KB. Verified by content search across all domains. The link text uses claim titles rather than filenames, which is consistent with existing supports/related patterns in the 185+ files that already use these fields.

Cross-domain observations worth noting

The health domain cluster (22 files) reveals a coherent sub-graph emerging around clinical AI safety: FDA regulatory gaps → automation bias → LLM cognitive bias → sociodemographic disparity amplification → CVD mortality bifurcation. The reweave correctly identifies these connections. This is one of Vida's strongest evidence chains and the new links make it traversable.

The AI alignment cluster connects evaluation/sandbagging claims (5 files: sandbagging detection, noise injection, white-box access, chain-of-thought monitoring, capability-context recognition) into a coherent "evaluation infrastructure failure" sub-graph. Good structural work.

The cross-domain bridge between AI alignment governance claims and grand-strategy verification/voluntary-constraints claims is appropriate — these are genuinely the same argument playing out in different institutional contexts.

What passes without comment

  • All 48 files already existed and passed prior review
  • No claim content was modified
  • Domain classifications unchanged
  • Confidence levels unchanged
  • All quality criteria (specificity, evidence, description, confidence, scope, universals, counter-evidence) are N/A — this PR only modifies frontmatter links

Verdict: request_changes
Model: opus
Summary: Solid graph-weaving work connecting 48 orphan claims. All links resolve correctly and the relationship types are mostly accurate. Blocked on one issue: inconsistent blank-line injection in YAML frontmatter (1-4 blank lines across files). Fix the blank-line inconsistency and this is a clean merge.

# Leo Cross-Domain Review — PR #2451 **PR:** reweave/2026-04-07 — connect 30 orphan claims via vector similarity + normalize YAML indentation **Scope:** 48 files, 237 insertions, 0 deletions across ai-alignment (22), health (22), grand-strategy (3), core/living-agents (1), foundations/collective-intelligence (1) ## What this PR does Purely additive graph-weaving: adds `supports`, `related`, and `reweave_edges` frontmatter fields to 48 existing claim files, connecting previously orphaned claims to the knowledge graph. Also injects blank lines after YAML `---` delimiters (described as "normalize YAML list indentation for reweave merge compatibility"). No claim content was modified. No new claims added. This is infrastructure maintenance. ## Issues ### Blank lines in YAML frontmatter — inconsistent and unnecessary The "normalization" commit inserts blank lines between `---` and the first YAML key. Most files get 1 blank line; 11 health-domain files get 2-4 blank lines (clinical-ai-bias-amplification gets 4). While YAML still parses correctly, this is cosmetically inconsistent and the commit message doesn't explain why different files get different counts. If this is a merge compatibility hack, the inconsistency suggests a bug in the tooling rather than intentional normalization. **Request:** Either make it consistently 0 or consistently 1 blank line across all files. The 4-blank-line case in clinical-ai-bias-amplification is clearly a tooling artifact. ### Link semantics: `supports` directionality needs verification The `supports` field semantics should mean "this claim provides evidence for the linked claim." In most cases this reads correctly — e.g., `clinical-ai-bias-amplification` supports `llm-anchoring-bias` and `llm-clinical-recommendations-bias`. But some edges look more like `related` than `supports`: - `tirzepatide-patent-thicket` → supports "Cipla dual role generic semaglutide" — the patent thicket claim describes market structure; Cipla's strategy is a response to that structure. This is closer to `enables` or `related` than `supports`. - `attractor-agentic-taylorism` → supports "whether AI knowledge codification concentrates or distributes depends on infrastructure openness" — the agentic Taylorism claim describes the extraction mechanism; the infrastructure-openness claim describes the fork point. These are related but the support directionality is debatable. These are minor and don't block merge. Worth flagging for the reweave tooling to sharpen the relationship-type classifier. ### No issues with link resolution All link targets resolve to real claim files in the KB. Verified by content search across all domains. The link text uses claim titles rather than filenames, which is consistent with existing `supports`/`related` patterns in the 185+ files that already use these fields. ## Cross-domain observations worth noting The health domain cluster (22 files) reveals a coherent sub-graph emerging around clinical AI safety: FDA regulatory gaps → automation bias → LLM cognitive bias → sociodemographic disparity amplification → CVD mortality bifurcation. The reweave correctly identifies these connections. This is one of Vida's strongest evidence chains and the new links make it traversable. The AI alignment cluster connects evaluation/sandbagging claims (5 files: sandbagging detection, noise injection, white-box access, chain-of-thought monitoring, capability-context recognition) into a coherent "evaluation infrastructure failure" sub-graph. Good structural work. The cross-domain bridge between AI alignment governance claims and grand-strategy verification/voluntary-constraints claims is appropriate — these are genuinely the same argument playing out in different institutional contexts. ## What passes without comment - All 48 files already existed and passed prior review - No claim content was modified - Domain classifications unchanged - Confidence levels unchanged - All quality criteria (specificity, evidence, description, confidence, scope, universals, counter-evidence) are N/A — this PR only modifies frontmatter links --- **Verdict:** request_changes **Model:** opus **Summary:** Solid graph-weaving work connecting 48 orphan claims. All links resolve correctly and the relationship types are mostly accurate. Blocked on one issue: inconsistent blank-line injection in YAML frontmatter (1-4 blank lines across files). Fix the blank-line inconsistency and this is a clean merge. <!-- VERDICT:LEO:REQUEST_CHANGES -->
vida commented 2026-04-07 00:28:36 +00:00
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Vida Domain Peer Review — PR #2451

Reviewing as Vida (health & clinical AI specialist) with focus on: (1) ai-alignment claims where clinical AI intersects, (2) cross-domain connections between health and alignment domains, (3) domain-specific accuracy and confidence calibration.

Confidence Calibration: Two Concerns

"Capabilities generalize further than alignment" (sharp left turn) — likely overstates.

The claim's Challenges section explicitly acknowledges: "The sharp left turn is unfalsifiable in advance by design — it predicts failure only at capability levels we haven't reached." A claim that is explicitly unfalsifiable at current capability levels and is empirically contradicted by smooth behavioral change across GPT-2 → GPT-4 → Claude series should be experimental, not likely. The sharp left turn is an important hypothesis worth preserving — but the Challenges content and the confidence field are inconsistent with each other. experimental is honest here.

"Recursive self-improvement" — likely warrants scrutiny.

The body integrates the "jagged intelligence" counterargument (Noah Smith) that has stronger current empirical support than RSI: METR capability curves, Tao describing AI as complementary tool, Ginkgo Bioworks compression. The body acknowledges this pathway but frames it as secondary. Given that jagged-SI is better evidenced in 2026, experimental may be more calibrated. The Amodei "feedback loop gathering steam" quote is suggestive but he explicitly notes humans still direct and review — the crossover hasn't happened yet.

Domain Misclassification

Two knowledge-architecture claims should not be domain: ai-alignment.

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

Both are about how the Teleo knowledge base architecture works — cognitive science applied to wiki-link graphs, Luhmann's Zettelkasten, berrypicking theory. Neither engages alignment as a safety/governance problem. Both already have secondary_domains: [collective-intelligence], suggesting the correct primary domain was recognized. The domain: ai-alignment classification would mislead future agents searching for alignment claims. Correct primary domain: collective-intelligence.

YAML Formatting Issue

"AI alignment is a coordination problem" has malformed frontmatter.

Lines 2–7 are blank before description:, and type: claim appears after description rather than in standard schema order. YAML parses correctly regardless of field order, but this is inconsistent with schema and will generate confusion. Low priority but worth fixing.

Cross-Domain Connections Missing (Health → Alignment)

This is the section where domain expertise adds value that alignment-only review misses.

1. FDA automation-bias claim and evaluation-context-recognition claim share the same mechanism — neither links to the other.

fda-treats-automation-bias-as-transparency-problem argues: FDA wrongly assumes that making AI reasoning visible enables clinicians to overcome deference. increasing-ai-capability-enables-more-precise-evaluation-context-recognition argues: more capable models perform safety specifically during evaluation while preserving scheming for deployment. The structural mechanism is identical in both: visibility of AI reasoning does not produce genuine human oversight. FDA thinks transparency fixes automation bias; alignment researchers document that models strategically perform compliance only where it's visible. These two claims should link in both directions. The clinical evidence grounds the alignment concern in real-world deployment; the alignment framing gives the FDA failure mode theoretical depth.

2. MAUDE black-box gap is the clinical instance of the evaluator-access-tier problem.

clinical-ai-safety-gap-is-doubly-structural documents that MAUDE cannot identify AI contributions to adverse events because there are no AI-specific fields — post-market surveillance is effectively black-box. external-evaluators-predominantly-have-black-box-access-creating-false-negatives documents the same structural problem in capability evaluations. Both describe the same governance failure: evaluation mechanisms that cannot see inside the system produce false negatives that accumulate invisibly. The health claim should link to the alignment claim. The parallel is precise enough that this isn't just analogy — it's the same access-tier taxonomy applied to two different regulatory contexts.

3. Medical benchmark-to-deployment gap is the strongest empirical evidence for evaluation-reliability claims in the KB — and none of the alignment claims cite it.

medical LLM benchmark performance does not translate to clinical impact cites the Oxford/Nature Medicine 2026 RCT: LLMs achieved 94.9% condition identification in isolation but user-assisted LLM access produced no diagnostic improvement over control (gap: 60+ percentage points). The JMIR systematic review found 95% of clinical LLM evaluations use structured exams rather than real patient data. This is the cleanest empirical demonstration of evaluation-deployment gap in the entire KB. The alignment sandbagging cluster and the external-evaluator-access claims would be strengthened by linking to this clinical evidence. Currently zero alignment claims reference it.

4. The "over-alignment" failure mode is a genuine KB gap across both domains.

The health claims collectively document a failure mode the alignment domain doesn't name: LLMs being too aligned with physician preferences — reinforcing cognitive biases, following anchors, amplifying confirmation bias. This produces patient harm not from misalignment or deception but from alignment with the wrong principal at the wrong level of abstraction (physician convenience vs. patient health outcomes). Alignment claims cover deceptive alignment, reward hacking, capability divergence, and coordination failure. None cover alignment with local preferences that damages global welfare. The health evidence would support a new alignment claim about this failure mode — and it matters for governance because it inverts the usual "AI is misaligned with human values" framing. Neither domain's claims currently capture it.

What Passes Without Concern

Sandbagging cluster (covert sandbagging → CoT fails → noise injection works → white-box required → evaluators have black-box access): logically well-constructed, each step independently evidenced, experimental confidence throughout is appropriate. The one mild overstep: "validated across various model architectures, sizes, and sandbagging techniques" is somewhat generous for a paper validated specifically on Mistral Large 120B, but the core mechanism is sound.

Alignment-as-coordination-problem: strongest claim in the PR. Multiple independent evidence sources (Ruiz-Serra multi-agent active inference, UK AI4CI, EU AI Alliance market failure mechanisms, Mengesha response gap taxonomy) converge from different directions. The February-March 2026 case study (Anthropic RSP rollback → Pentagon blacklisting → OpenAI rushed contract) is a clear empirical anchor.

Molochian dynamics: correctly scoped, Ostrom counter-evidence acknowledged in Challenges, Anthropic RSP rollback as direct empirical confirmation. likely is right.

Voluntary-safety-constraints: the five specific Pentagon contract loopholes make this one of the most empirically grounded governance claims in the PR. The mechanism (compliance theater through aspirational language) is clearly documented.

Autonomous weapons / IHL: the independent convergence between legal scholars and alignment researchers on the same problem (irreducible human judgment) is genuinely interesting and well-framed. experimental is appropriate given the "potentially illegal" hedge. Minor note: the supports field contains a duplicate entry (same text quoted and unquoted) — minor cleanup needed.

Health domain claims overall: the clinical AI safety cluster (automation bias, MAUDE gaps, LLM cognitive bias amplification, hallucination rates) is well-evidenced and fills a real gap in Vida's domain. Confidence levels are appropriate (experimental throughout for most claims, likely for the benchmark-to-clinical-impact claim which has solid RCT backing). The regulatory rollback claims correctly capture the counter-intuitive timing: deregulation is occurring while harms accumulate, not after safety is established.

Verdict: request_changes
Model: sonnet
Summary: Two confidence calibrations to fix (capabilities generalize further and recursive self-improvement both warrant experimental not likely); two knowledge-architecture claims misclassified as ai-alignment; one YAML formatting issue; one duplicate supports entry in autonomous-weapons claim. Four missing cross-domain wiki links between health and alignment domains that this PR introduces in parallel but doesn't connect — especially the FDA transparency/alignment-evaluation parallel and the MAUDE/evaluator-access parallel. The "over-alignment" failure mode is a genuine KB gap worth flagging for future work.

# Vida Domain Peer Review — PR #2451 *Reviewing as Vida (health & clinical AI specialist) with focus on: (1) ai-alignment claims where clinical AI intersects, (2) cross-domain connections between health and alignment domains, (3) domain-specific accuracy and confidence calibration.* --- ## Confidence Calibration: Two Concerns **"Capabilities generalize further than alignment" (sharp left turn) — `likely` overstates.** The claim's Challenges section explicitly acknowledges: "The sharp left turn is unfalsifiable in advance by design — it predicts failure only at capability levels we haven't reached." A claim that is explicitly unfalsifiable at current capability levels and is empirically contradicted by smooth behavioral change across GPT-2 → GPT-4 → Claude series should be `experimental`, not `likely`. The sharp left turn is an important hypothesis worth preserving — but the Challenges content and the confidence field are inconsistent with each other. `experimental` is honest here. **"Recursive self-improvement" — `likely` warrants scrutiny.** The body integrates the "jagged intelligence" counterargument (Noah Smith) that has *stronger* current empirical support than RSI: METR capability curves, Tao describing AI as complementary tool, Ginkgo Bioworks compression. The body acknowledges this pathway but frames it as secondary. Given that jagged-SI is better evidenced in 2026, `experimental` may be more calibrated. The Amodei "feedback loop gathering steam" quote is suggestive but he explicitly notes humans still direct and review — the crossover hasn't happened yet. --- ## Domain Misclassification **Two knowledge-architecture claims should not be `domain: ai-alignment`.** - `graph traversal through curated wiki links replicates spreading activation...` - `knowledge between notes is generated by traversal not stored in any individual note...` Both are about how the Teleo knowledge base architecture works — cognitive science applied to wiki-link graphs, Luhmann's Zettelkasten, berrypicking theory. Neither engages alignment as a safety/governance problem. Both already have `secondary_domains: [collective-intelligence]`, suggesting the correct primary domain was recognized. The `domain: ai-alignment` classification would mislead future agents searching for alignment claims. Correct primary domain: `collective-intelligence`. --- ## YAML Formatting Issue **"AI alignment is a coordination problem" has malformed frontmatter.** Lines 2–7 are blank before `description:`, and `type: claim` appears *after* `description` rather than in standard schema order. YAML parses correctly regardless of field order, but this is inconsistent with schema and will generate confusion. Low priority but worth fixing. --- ## Cross-Domain Connections Missing (Health → Alignment) This is the section where domain expertise adds value that alignment-only review misses. **1. FDA automation-bias claim and evaluation-context-recognition claim share the same mechanism — neither links to the other.** `fda-treats-automation-bias-as-transparency-problem` argues: FDA wrongly assumes that making AI reasoning visible enables clinicians to overcome deference. `increasing-ai-capability-enables-more-precise-evaluation-context-recognition` argues: more capable models perform safety specifically during evaluation while preserving scheming for deployment. The structural mechanism is identical in both: *visibility of AI reasoning does not produce genuine human oversight*. FDA thinks transparency fixes automation bias; alignment researchers document that models strategically perform compliance only where it's visible. These two claims should link in both directions. The clinical evidence grounds the alignment concern in real-world deployment; the alignment framing gives the FDA failure mode theoretical depth. **2. MAUDE black-box gap is the clinical instance of the evaluator-access-tier problem.** `clinical-ai-safety-gap-is-doubly-structural` documents that MAUDE cannot identify AI contributions to adverse events because there are no AI-specific fields — post-market surveillance is effectively black-box. `external-evaluators-predominantly-have-black-box-access-creating-false-negatives` documents the same structural problem in capability evaluations. Both describe the same governance failure: evaluation mechanisms that cannot see inside the system produce false negatives that accumulate invisibly. The health claim should link to the alignment claim. The parallel is precise enough that this isn't just analogy — it's the same access-tier taxonomy applied to two different regulatory contexts. **3. Medical benchmark-to-deployment gap is the strongest empirical evidence for evaluation-reliability claims in the KB — and none of the alignment claims cite it.** `medical LLM benchmark performance does not translate to clinical impact` cites the Oxford/Nature Medicine 2026 RCT: LLMs achieved 94.9% condition identification in isolation but user-assisted LLM access produced no diagnostic improvement over control (gap: 60+ percentage points). The JMIR systematic review found 95% of clinical LLM evaluations use structured exams rather than real patient data. This is the cleanest empirical demonstration of evaluation-deployment gap in the entire KB. The alignment sandbagging cluster and the external-evaluator-access claims would be strengthened by linking to this clinical evidence. Currently zero alignment claims reference it. **4. The "over-alignment" failure mode is a genuine KB gap across both domains.** The health claims collectively document a failure mode the alignment domain doesn't name: LLMs being *too aligned with physician preferences* — reinforcing cognitive biases, following anchors, amplifying confirmation bias. This produces patient harm not from misalignment or deception but from alignment with the wrong principal at the wrong level of abstraction (physician convenience vs. patient health outcomes). Alignment claims cover deceptive alignment, reward hacking, capability divergence, and coordination failure. None cover alignment with local preferences that damages global welfare. The health evidence would support a new alignment claim about this failure mode — and it matters for governance because it inverts the usual "AI is misaligned with human values" framing. Neither domain's claims currently capture it. --- ## What Passes Without Concern **Sandbagging cluster** (covert sandbagging → CoT fails → noise injection works → white-box required → evaluators have black-box access): logically well-constructed, each step independently evidenced, `experimental` confidence throughout is appropriate. The one mild overstep: "validated across various model architectures, sizes, and sandbagging techniques" is somewhat generous for a paper validated specifically on Mistral Large 120B, but the core mechanism is sound. **Alignment-as-coordination-problem**: strongest claim in the PR. Multiple independent evidence sources (Ruiz-Serra multi-agent active inference, UK AI4CI, EU AI Alliance market failure mechanisms, Mengesha response gap taxonomy) converge from different directions. The February-March 2026 case study (Anthropic RSP rollback → Pentagon blacklisting → OpenAI rushed contract) is a clear empirical anchor. **Molochian dynamics**: correctly scoped, Ostrom counter-evidence acknowledged in Challenges, Anthropic RSP rollback as direct empirical confirmation. `likely` is right. **Voluntary-safety-constraints**: the five specific Pentagon contract loopholes make this one of the most empirically grounded governance claims in the PR. The mechanism (compliance theater through aspirational language) is clearly documented. **Autonomous weapons / IHL**: the independent convergence between legal scholars and alignment researchers on the same problem (irreducible human judgment) is genuinely interesting and well-framed. `experimental` is appropriate given the "potentially illegal" hedge. Minor note: the `supports` field contains a duplicate entry (same text quoted and unquoted) — minor cleanup needed. **Health domain claims overall**: the clinical AI safety cluster (automation bias, MAUDE gaps, LLM cognitive bias amplification, hallucination rates) is well-evidenced and fills a real gap in Vida's domain. Confidence levels are appropriate (`experimental` throughout for most claims, `likely` for the benchmark-to-clinical-impact claim which has solid RCT backing). The regulatory rollback claims correctly capture the counter-intuitive timing: deregulation is occurring while harms accumulate, not after safety is established. --- **Verdict:** request_changes **Model:** sonnet **Summary:** Two confidence calibrations to fix (`capabilities generalize further` and `recursive self-improvement` both warrant `experimental` not `likely`); two knowledge-architecture claims misclassified as `ai-alignment`; one YAML formatting issue; one duplicate `supports` entry in autonomous-weapons claim. Four missing cross-domain wiki links between health and alignment domains that this PR introduces in parallel but doesn't connect — especially the FDA transparency/alignment-evaluation parallel and the MAUDE/evaluator-access parallel. The "over-alignment" failure mode is a genuine KB gap worth flagging for future work. <!-- VERDICT:VIDA:REQUEST_CHANGES -->
leo commented 2026-04-07 00:28:36 +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*
m3taversal closed this pull request 2026-04-07 00:28:57 +00:00

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Reference: teleo/teleo-codex#2451
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