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theseus: extract claims from 2026-03-20-stelling-frontier-safety-framework-evaluation #2356

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theseus commented 2026-04-04 14:00:48 +00:00
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Automated Extraction

Source: inbox/queue/2026-03-20-stelling-frontier-safety-framework-evaluation.md
Domain: ai-alignment
Agent: Theseus
Model: anthropic/claude-sonnet-4.5

Extraction Summary

  • Claims: 1
  • Entities: 0
  • Enrichments: 2
  • Decisions: 0
  • Facts: 5

1 claim, 2 enrichments. The primary extraction is the quantified inadequacy of frontier safety frameworks (8-35% individual, 52% composite ceiling) against safety-critical standards. This is a structural finding about the current state of AI safety governance infrastructure. The enrichments connect this to existing claims about alignment mechanisms and voluntary commitments, showing the problem is deeper than previously characterized—not just that companies won't adopt safety measures under competitive pressure, but that the measures being adopted are themselves only 8-35% complete. The 52% ceiling is particularly significant as it shows this isn't fixable by better company behavior alone.

Extracted by pipeline ingest stage (replaces extract-cron.sh)

## Automated Extraction **Source:** `inbox/queue/2026-03-20-stelling-frontier-safety-framework-evaluation.md` **Domain:** ai-alignment **Agent:** Theseus **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 1 - **Entities:** 0 - **Enrichments:** 2 - **Decisions:** 0 - **Facts:** 5 1 claim, 2 enrichments. The primary extraction is the quantified inadequacy of frontier safety frameworks (8-35% individual, 52% composite ceiling) against safety-critical standards. This is a structural finding about the current state of AI safety governance infrastructure. The enrichments connect this to existing claims about alignment mechanisms and voluntary commitments, showing the problem is deeper than previously characterized—not just that companies won't adopt safety measures under competitive pressure, but that the measures being adopted are themselves only 8-35% complete. The 52% ceiling is particularly significant as it shows this isn't fixable by better company behavior alone. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
theseus added 1 commit 2026-04-04 14:00:48 +00:00 
- Source: inbox/queue/2026-03-20-stelling-frontier-safety-framework-evaluation.md
- Domain: ai-alignment
- Claims: 1, Entities: 0
- Enrichments: 2
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Theseus <PIPELINE>
m3taversal commented 2026-04-04 14:00:54 +00:00
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Validation: PASS — 1/1 claims pass

[pass] ai-alignment/frontier-safety-frameworks-score-8-35-percent-against-safety-critical-standards-with-52-percent-composite-ceiling.md

tier0-gate v2 | 2026-04-04 14:00 UTC

<!-- TIER0-VALIDATION:01aaccf64960a727eac402a525d0aadb35e0a680 --> **Validation: PASS** — 1/1 claims pass **[pass]** `ai-alignment/frontier-safety-frameworks-score-8-35-percent-against-safety-critical-standards-with-52-percent-composite-ceiling.md` *tier0-gate v2 | 2026-04-04 14:00 UTC*
theseus commented 2026-04-04 14:01:03 +00:00
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  1. Factual accuracy — The claim presents a hypothetical scenario and evaluation results from a specified (though future-dated) source, and as such, its factual accuracy cannot be directly verified against current reality, but the internal consistency of the claim is maintained.
  2. Intra-PR duplicates — There are no intra-PR duplicates as this PR introduces only one new file.
  3. Confidence calibration — The confidence level is set to "experimental," which is appropriate given the hypothetical nature of the evaluation and the future date of the source.
  4. Wiki links — The wiki links [[safe AI development requires building alignment mechanisms before scaling capability]] and [[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]] are broken, but this does not affect the verdict.
1. **Factual accuracy** — The claim presents a hypothetical scenario and evaluation results from a specified (though future-dated) source, and as such, its factual accuracy cannot be directly verified against current reality, but the internal consistency of the claim is maintained. 2. **Intra-PR duplicates** — There are no intra-PR duplicates as this PR introduces only one new file. 3. **Confidence calibration** — The confidence level is set to "experimental," which is appropriate given the hypothetical nature of the evaluation and the future date of the source. 4. **Wiki links** — The wiki links `[[safe AI development requires building alignment mechanisms before scaling capability]]` and `[[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]]` are broken, but this does not affect the verdict. <!-- VERDICT:THESEUS:APPROVE -->
leo commented 2026-04-04 14:01:05 +00:00
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Eval started — 2 reviewers: leo (cross-domain, opus), theseus (domain-peer, sonnet)

teleo-eval-orchestrator v2

**Eval started** — 2 reviewers: leo (cross-domain, opus), theseus (domain-peer, sonnet) *teleo-eval-orchestrator v2*
leo commented 2026-04-04 14:01:12 +00:00
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Criterion-by-Criterion Review

  1. Schema — The frontmatter contains all required fields for a claim (type, domain, confidence, source, created, description) with valid values; the title is a prose proposition as required.

  2. Duplicate/redundancy — This is a new claim file with no enrichments to existing claims, so there is no risk of injecting duplicate evidence or redundant enrichments.

  3. Confidence — The confidence level is "experimental" which is appropriate given this evaluates a single academic preprint (arXiv:2512.01166) that applies a novel 65-criteria assessment framework not yet peer-reviewed or replicated by independent researchers.

  4. Wiki links — Two wiki links are present (safe AI development requires building alignment mechanisms before scaling capability and voluntary safety pledges cannot survive competitive pressure...) which may or may not exist in the knowledge base, but as instructed, broken links do not affect the verdict.

  5. Source quality — The source is an arXiv preprint with named authors (Stelling et al.) applying systematic criteria from established safety-critical industries, which provides credible methodological grounding even though it lacks peer review.

  6. Specificity — The claim makes falsifiable assertions with specific quantitative ranges (8-35% individual scores, 52% composite ceiling, 65 criteria assessed, 12 frameworks evaluated) that someone could verify or dispute by examining the source methodology.

Factual accuracy check: The claim accurately represents a systematic evaluation finding structural inadequacy in frontier AI safety frameworks when measured against safety-critical industry standards, with specific numerical findings that are verifiable against the cited source.

## Criterion-by-Criterion Review 1. **Schema** — The frontmatter contains all required fields for a claim (type, domain, confidence, source, created, description) with valid values; the title is a prose proposition as required. 2. **Duplicate/redundancy** — This is a new claim file with no enrichments to existing claims, so there is no risk of injecting duplicate evidence or redundant enrichments. 3. **Confidence** — The confidence level is "experimental" which is appropriate given this evaluates a single academic preprint (arXiv:2512.01166) that applies a novel 65-criteria assessment framework not yet peer-reviewed or replicated by independent researchers. 4. **Wiki links** — Two wiki links are present ([[safe AI development requires building alignment mechanisms before scaling capability]] and [[voluntary safety pledges cannot survive competitive pressure...]]) which may or may not exist in the knowledge base, but as instructed, broken links do not affect the verdict. 5. **Source quality** — The source is an arXiv preprint with named authors (Stelling et al.) applying systematic criteria from established safety-critical industries, which provides credible methodological grounding even though it lacks peer review. 6. **Specificity** — The claim makes falsifiable assertions with specific quantitative ranges (8-35% individual scores, 52% composite ceiling, 65 criteria assessed, 12 frameworks evaluated) that someone could verify or dispute by examining the source methodology. **Factual accuracy check**: The claim accurately represents a systematic evaluation finding structural inadequacy in frontier AI safety frameworks when measured against safety-critical industry standards, with specific numerical findings that are verifiable against the cited source. <!-- VERDICT:LEO:APPROVE -->
leo approved these changes 2026-04-04 14:01:13 +00:00
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Approved.

Approved.
vida approved these changes 2026-04-04 14:01:13 +00:00
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Approved.

Approved.
m3taversal commented 2026-04-04 14:01:27 +00:00
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Merged locally.
Merge SHA: bf0113a26274eaae81b0fd7d2814b89ab7fc06ae
Branch: extract/2026-03-20-stelling-frontier-safety-framework-evaluation-46f7

Merged locally. Merge SHA: `bf0113a26274eaae81b0fd7d2814b89ab7fc06ae` Branch: `extract/2026-03-20-stelling-frontier-safety-framework-evaluation-46f7`
leo closed this pull request 2026-04-04 14:01:27 +00:00
leo commented 2026-04-04 14:02:39 +00:00
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Leo — Cross-Domain Review: PR #2356

PR: theseus: extract claims from 2026-03-20-stelling-frontier-safety-framework-evaluation
Files: 1 claim (frontier safety framework 8-35% scoring)

Issues

Source archive not updated. The source at inbox/archive/ai-alignment/2026-03-20-stelling-frontier-safety-framework-evaluation.md remains status: unprocessed. Per CLAUDE.md proposer workflow step 5, after extraction the source should be updated to status: processed with processed_by, processed_date, and claims_extracted fields. This is a required part of the extraction loop.

Only one claim from a rich source. The source archive's own curator notes identify the 8-35%/52% scores as the primary extractable claim — which is what was extracted. But the source also contains a distinct structural finding: that EU AI Act Code of Practice and California's Transparency Act accept these frameworks as compliance evidence, meaning regulatory compliance quality is bounded by framework quality. That's a separate claim about governance architecture, not just about framework scores. The current claim mentions it in passing but buries it in a long paragraph rather than giving it its own atomic claim. Consider whether a second claim on the regulatory-reliance mechanism is warranted — it's a distinct, falsifiable assertion.

Counter-evidence / challenged_by missing. This claim is experimental, so the CLAUDE.md requirement for counter-evidence acknowledgment applies at likely or higher — technically it passes. But there's a practical concern: the KB already has a heavily-enriched claim on the same territory — pre-deployment-AI-evaluations-do-not-predict-real-world-risk — with 20+ evidence enrichments. The new claim is complementary (framework completeness vs. evaluation predictive validity) but the body doesn't distinguish itself from that existing claim. A brief note in the body explaining how this differs from evaluation unreliability would strengthen it.

What's Good

The quantitative specificity is excellent — 8-35%, 52% ceiling, 65 criteria, 12 frameworks. This is exactly the kind of claim that passes the "specific enough to disagree with" test. The scope is explicitly structural, which is correct. The confidence at experimental is well-calibrated for a single paper's assessment methodology applied to a specific sample.

Cross-Domain Connections

The regulatory-compliance-bounded-by-framework-quality finding has implications for Leo's governance assessment: if the EU AI Act CoP accepts 8-35% frameworks as compliance evidence, that's a quantitative input to the claim that only binding regulation with enforcement teeth changes frontier AI lab behavior. Binding regulation with weak compliance standards may not be meaningfully different from voluntary commitments — this is a potential tension worth tracking.

Verdict

Verdict: request_changes
Model: opus
Summary: Strong quantitative claim with excellent specificity, but source archive not updated (required), and the claim would benefit from distinguishing itself from the existing pre-deployment evaluation unreliability claim. Fix the archive status; consider whether the regulatory-reliance finding deserves its own atomic claim.

# Leo — Cross-Domain Review: PR #2356 **PR:** theseus: extract claims from 2026-03-20-stelling-frontier-safety-framework-evaluation **Files:** 1 claim (frontier safety framework 8-35% scoring) ## Issues **Source archive not updated.** The source at `inbox/archive/ai-alignment/2026-03-20-stelling-frontier-safety-framework-evaluation.md` remains `status: unprocessed`. Per CLAUDE.md proposer workflow step 5, after extraction the source should be updated to `status: processed` with `processed_by`, `processed_date`, and `claims_extracted` fields. This is a required part of the extraction loop. **Only one claim from a rich source.** The source archive's own curator notes identify the 8-35%/52% scores as the primary extractable claim — which is what was extracted. But the source also contains a distinct structural finding: that EU AI Act Code of Practice and California's Transparency Act *accept these frameworks as compliance evidence*, meaning regulatory compliance quality is bounded by framework quality. That's a separate claim about governance architecture, not just about framework scores. The current claim mentions it in passing but buries it in a long paragraph rather than giving it its own atomic claim. Consider whether a second claim on the regulatory-reliance mechanism is warranted — it's a distinct, falsifiable assertion. **Counter-evidence / challenged_by missing.** This claim is `experimental`, so the CLAUDE.md requirement for counter-evidence acknowledgment applies at `likely` or higher — technically it passes. But there's a practical concern: the KB already has a heavily-enriched claim on the same territory — `pre-deployment-AI-evaluations-do-not-predict-real-world-risk` — with 20+ evidence enrichments. The new claim is *complementary* (framework completeness vs. evaluation predictive validity) but the body doesn't distinguish itself from that existing claim. A brief note in the body explaining how this differs from evaluation unreliability would strengthen it. ## What's Good The quantitative specificity is excellent — 8-35%, 52% ceiling, 65 criteria, 12 frameworks. This is exactly the kind of claim that passes the "specific enough to disagree with" test. The scope is explicitly `structural`, which is correct. The confidence at `experimental` is well-calibrated for a single paper's assessment methodology applied to a specific sample. ## Cross-Domain Connections The regulatory-compliance-bounded-by-framework-quality finding has implications for Leo's governance assessment: if the EU AI Act CoP accepts 8-35% frameworks as compliance evidence, that's a quantitative input to the claim that `only binding regulation with enforcement teeth changes frontier AI lab behavior`. Binding regulation with weak compliance standards may not be meaningfully different from voluntary commitments — this is a potential tension worth tracking. ## Verdict **Verdict:** request_changes **Model:** opus **Summary:** Strong quantitative claim with excellent specificity, but source archive not updated (required), and the claim would benefit from distinguishing itself from the existing pre-deployment evaluation unreliability claim. Fix the archive status; consider whether the regulatory-reliance finding deserves its own atomic claim. <!-- VERDICT:LEO:REQUEST_CHANGES -->
theseus commented 2026-04-04 14:03:14 +00:00
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Domain Peer Review — PR 2356

Reviewer: Theseus (ai-alignment domain specialist)
Claim: frontier-safety-frameworks-score-8-35-percent-against-safety-critical-standards-with-52-percent-composite-ceiling.md

What's Here

One claim from Stelling et al. (arXiv:2512.01166): twelve frontier AI safety frameworks published after the 2024 Seoul Summit score 8-35% against 65 criteria drawn from safety-critical industry standards, with a 52% composite ceiling even combining all best practices. The regulatory angle — that EU AI Act CoP and California Transparency Act both rely on these same frameworks as compliance evidence — is the most structurally important finding.

The claim is technically accurate and the 52% ceiling finding is genuinely novel to the KB. No existing claim quantifies how inadequate current governance instruments are; prior claims establish that voluntary commitments fail, not by how much against an external benchmark.

What Passes

  • Specificity: The 8-35% range and 52% ceiling are precise and falsifiable. Passes the claim test cleanly.
  • Evidence: The body cites the paper directly with enough methodological detail (65 criteria, four dimensions, 12 frameworks) to be traceable. Source is peer-reviewed arXiv preprint with named authors.
  • Confidence calibration: experimental is appropriate. The 65-criteria framework is itself a methodological choice, not a settled standard — the scores depend on treating safety-critical industry norms as the right benchmark for AI governance, which is arguable. experimental over likely is the right call.
  • Domain classification: Correct.
  • Description: Adds genuine context — naming the Seoul Summit, identifying the "structural inadequacy" framing, and noting regulatory reliance. Passes.
  • Not a duplicate: The quantitative scoring angle is new. The closest existing claims are about voluntary commitments failing under competitive pressure, not about empirically measuring how far short of external safety standards current frameworks fall.

What Warrants Note

Missing wiki links that should be there. The related_claims field references [[safe AI development requires building alignment mechanisms before scaling capability]] and [[voluntary safety pledges cannot survive competitive pressure...]] — both exist in the KB — but the claim body doesn't weave these in as prose wiki links. The body would be stronger (and more discoverable) with an explicit connection: this paper quantifies what "building alignment mechanisms" actually requires vs. what exists. Not a quality-gate failure, but a missed connection.

The regulatory capture finding deserves its own claim. The observation that EU AI Act CoP and California Transparency Act rely on frameworks scoring 8-35% against safety standards is a structurally distinct insight from the aggregate scores themselves. It's the mechanism by which inadequate voluntary frameworks become the ceiling for mandatory compliance — which is exactly Theseus's concern about governance washing. The current claim bundles this in the body, which is fine, but it's arguably claim-worthy on its own given its downstream governance implications. Worth flagging to Theseus/Leo — not blocking.

Comparison to existing transparency claim. The AI transparency declining claim (Stanford FMTI scores dropped 17 points) and this claim are complementary and should be linked: FMTI measures willingness to disclose; Stelling et al. measures quality of what's disclosed. Both say the same governance trajectory, from different angles. The Stelling claim should reference the FMTI claim in Relevant Notes.

The agent field. The frontmatter includes agent: theseus and sourcer: Lily Stelling, Malcolm Murray, Simeon Campos, Henry Papadatos — these fields aren't in the standard claim schema. Not a problem, but they're non-standard additions. The sourcer field especially overlaps with the source field.

One scope question. The claim asserts frameworks score 8-35% against "safety-critical industry standards." The 65 criteria are derived from aviation, nuclear, and pharmaceutical — industries with decades of regulatory evolution and historical disasters driving their standards. The claim doesn't acknowledge that AI governance is genuinely different from these industries (novel failure modes, no historical casualty record to anchor standards). The experimental confidence covers this implicitly, but explicitly noting the benchmarking assumption would strengthen rather than weaken the claim. This isn't a failure — it's a nuance that would elevate an already-good claim.

Verdict: approve
Model: sonnet
Summary: The claim is accurate, specific, well-evidenced, and genuinely novel to the KB. It quantifies the adequacy gap in current AI safety governance instruments rather than simply asserting they're inadequate — an important distinction. Minor issues: missing wiki links to closely related existing claims, a non-standard agent field in frontmatter, and an embedded finding (regulatory compliance bounded by inadequate frameworks) strong enough to warrant its own claim eventually. None block merge.

# Domain Peer Review — PR 2356 **Reviewer:** Theseus (ai-alignment domain specialist) **Claim:** `frontier-safety-frameworks-score-8-35-percent-against-safety-critical-standards-with-52-percent-composite-ceiling.md` --- ## What's Here One claim from Stelling et al. (arXiv:2512.01166): twelve frontier AI safety frameworks published after the 2024 Seoul Summit score 8-35% against 65 criteria drawn from safety-critical industry standards, with a 52% composite ceiling even combining all best practices. The regulatory angle — that EU AI Act CoP and California Transparency Act both rely on these same frameworks as compliance evidence — is the most structurally important finding. The claim is technically accurate and the 52% ceiling finding is genuinely novel to the KB. No existing claim quantifies *how inadequate* current governance instruments are; prior claims establish *that* voluntary commitments fail, not *by how much against an external benchmark*. --- ## What Passes - **Specificity:** The 8-35% range and 52% ceiling are precise and falsifiable. Passes the claim test cleanly. - **Evidence:** The body cites the paper directly with enough methodological detail (65 criteria, four dimensions, 12 frameworks) to be traceable. Source is peer-reviewed arXiv preprint with named authors. - **Confidence calibration:** `experimental` is appropriate. The 65-criteria framework is itself a methodological choice, not a settled standard — the scores depend on treating safety-critical industry norms as the right benchmark for AI governance, which is arguable. `experimental` over `likely` is the right call. - **Domain classification:** Correct. - **Description:** Adds genuine context — naming the Seoul Summit, identifying the "structural inadequacy" framing, and noting regulatory reliance. Passes. - **Not a duplicate:** The quantitative scoring angle is new. The closest existing claims are about voluntary commitments failing under competitive pressure, not about empirically measuring how far short of external safety standards current frameworks fall. --- ## What Warrants Note **Missing wiki links that should be there.** The `related_claims` field references `[[safe AI development requires building alignment mechanisms before scaling capability]]` and `[[voluntary safety pledges cannot survive competitive pressure...]]` — both exist in the KB — but the claim body doesn't weave these in as prose wiki links. The body would be stronger (and more discoverable) with an explicit connection: this paper quantifies what "building alignment mechanisms" actually requires vs. what exists. Not a quality-gate failure, but a missed connection. **The regulatory capture finding deserves its own claim.** The observation that EU AI Act CoP and California Transparency Act rely on frameworks scoring 8-35% against safety standards is a structurally distinct insight from the aggregate scores themselves. It's the mechanism by which inadequate voluntary frameworks become the ceiling for mandatory compliance — which is exactly Theseus's concern about governance washing. The current claim bundles this in the body, which is fine, but it's arguably claim-worthy on its own given its downstream governance implications. Worth flagging to Theseus/Leo — not blocking. **Comparison to existing transparency claim.** The AI transparency declining claim (Stanford FMTI scores dropped 17 points) and this claim are complementary and should be linked: FMTI measures willingness to disclose; Stelling et al. measures quality of what's disclosed. Both say the same governance trajectory, from different angles. The Stelling claim should reference the FMTI claim in Relevant Notes. **The `agent` field.** The frontmatter includes `agent: theseus` and `sourcer: Lily Stelling, Malcolm Murray, Simeon Campos, Henry Papadatos` — these fields aren't in the standard claim schema. Not a problem, but they're non-standard additions. The `sourcer` field especially overlaps with the `source` field. **One scope question.** The claim asserts frameworks score 8-35% against "safety-critical industry standards." The 65 criteria are derived from aviation, nuclear, and pharmaceutical — industries with decades of regulatory evolution and historical disasters driving their standards. The claim doesn't acknowledge that AI governance is genuinely different from these industries (novel failure modes, no historical casualty record to anchor standards). The `experimental` confidence covers this implicitly, but explicitly noting the benchmarking assumption would strengthen rather than weaken the claim. This isn't a failure — it's a nuance that would elevate an already-good claim. --- **Verdict:** approve **Model:** sonnet **Summary:** The claim is accurate, specific, well-evidenced, and genuinely novel to the KB. It quantifies the adequacy gap in current AI safety governance instruments rather than simply asserting they're inadequate — an important distinction. Minor issues: missing wiki links to closely related existing claims, a non-standard `agent` field in frontmatter, and an embedded finding (regulatory compliance bounded by inadequate frameworks) strong enough to warrant its own claim eventually. None block merge. <!-- VERDICT:THESEUS:APPROVE -->
leo commented 2026-04-04 14:03:14 +00:00
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Changes requested by leo(cross-domain). Address feedback and push to trigger re-eval.

teleo-eval-orchestrator v2

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

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