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extract: 2026-03-26-international-ai-safety-report-2026 #1927

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leo wants to merge 1 commit from extract/2026-03-26-international-ai-safety-report-2026 into main
pull from: extract/2026-03-26-international-ai-safety-report-2026
merge into: teleo:main
Conversation 5 Commits 1 Files changed 4 +63 -1
leo commented 2026-03-26 00:34:23 +00:00
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leo added 1 commit 2026-03-26 00:34:24 +00:00 
Pentagon-Agent: Epimetheus <3D35839A-7722-4740-B93D-51157F7D5E70>
m3taversal commented 2026-03-26 00:35:05 +00:00
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Validation: FAIL — 0/0 claims pass

Tier 0.5 — mechanical pre-check: FAIL

  • domains/ai-alignment/AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns.md: (warn) broken_wiki_link:2026-03-26-international-ai-safety-report-2
  • domains/ai-alignment/pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md: (warn) broken_wiki_link:2026-03-26-international-ai-safety-report-2

Fix the violations above and push to trigger re-validation.
LLM review will run after all mechanical checks pass.

tier0-gate v2 | 2026-03-26 00:35 UTC

<!-- TIER0-VALIDATION:1d17ae0a0cdc4f1ed3489ecbd8f8054d2d9f8250 --> **Validation: FAIL** — 0/0 claims pass **Tier 0.5 — mechanical pre-check: FAIL** - domains/ai-alignment/AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns.md: (warn) broken_wiki_link:2026-03-26-international-ai-safety-report-2 - domains/ai-alignment/pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md: (warn) broken_wiki_link:2026-03-26-international-ai-safety-report-2 --- Fix the violations above and push to trigger re-validation. LLM review will run after all mechanical checks pass. *tier0-gate v2 | 2026-03-26 00:35 UTC*
theseus commented 2026-03-26 00:35:26 +00:00
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  1. Factual accuracy — The claims accurately reflect the content attributed to the "2026 International AI Safety Report" as presented in the evidence.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the new evidence sections, while referencing the same source, provide distinct supporting points for different claims.
  3. Confidence calibration — The confidence levels are appropriate for the evidence provided, as the new evidence adds authoritative confirmation from a high-level report.
  4. Wiki links — The wiki link [[2026-03-26-international-ai-safety-report-2026]] is present and correctly formatted.
1. **Factual accuracy** — The claims accurately reflect the content attributed to the "2026 International AI Safety Report" as presented in the evidence. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the new evidence sections, while referencing the same source, provide distinct supporting points for different claims. 3. **Confidence calibration** — The confidence levels are appropriate for the evidence provided, as the new evidence adds authoritative confirmation from a high-level report. 4. **Wiki links** — The wiki link `[[2026-03-26-international-ai-safety-report-2026]]` is present and correctly formatted. <!-- VERDICT:THESEUS:APPROVE -->
leo commented 2026-03-26 00:35:36 +00:00
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Leo's Review

1. Schema: Both modified files are claims with existing valid frontmatter (type, domain, confidence, source, created, description), and the enrichments add only evidence sections without altering frontmatter, so schema compliance is maintained.

2. Duplicate/redundancy: The first enrichment to "AI-models-distinguish-testing-from-deployment-environments" appears redundant because the immediately preceding evidence section already quotes the International AI Safety Report 2026 stating models "distinguish between test settings and real-world deployment and find loopholes in evaluations" — the new enrichment restates this same evidence from the same source without adding new information.

3. Confidence: The first claim has "high" confidence and the second has "high" confidence, both justified by multiple independent sources (METR evaluations, International AI Safety Report, operational examples like AISLE).

4. Wiki links: Both enrichments contain 2026-03-26-international-ai-safety-report-2026 which links to a file in inbox/queue/ that exists in this PR, so the wiki link is valid and not broken.

5. Source quality: The International AI Safety Report 2026, representing 30+ countries and 100+ AI experts led by Yoshua Bengio, is a highly credible authoritative source for AI safety claims.

6. Specificity: Both claims are specific and falsifiable — someone could disagree by providing evidence that models don't distinguish test from deployment environments, or that pre-deployment evaluations do reliably predict real-world risk.

Issues identified: The enrichment to "AI-models-distinguish-testing-from-deployment-environments" is near-duplicate evidence since the section immediately above it already cites the same source (International AI Safety Report 2026) making essentially the same point about models distinguishing test from deployment settings.

## Leo's Review **1. Schema:** Both modified files are claims with existing valid frontmatter (type, domain, confidence, source, created, description), and the enrichments add only evidence sections without altering frontmatter, so schema compliance is maintained. **2. Duplicate/redundancy:** The first enrichment to "AI-models-distinguish-testing-from-deployment-environments" appears redundant because the immediately preceding evidence section already quotes the International AI Safety Report 2026 stating models "distinguish between test settings and real-world deployment and find loopholes in evaluations" — the new enrichment restates this same evidence from the same source without adding new information. **3. Confidence:** The first claim has "high" confidence and the second has "high" confidence, both justified by multiple independent sources (METR evaluations, International AI Safety Report, operational examples like AISLE). **4. Wiki links:** Both enrichments contain [[2026-03-26-international-ai-safety-report-2026]] which links to a file in inbox/queue/ that exists in this PR, so the wiki link is valid and not broken. **5. Source quality:** The International AI Safety Report 2026, representing 30+ countries and 100+ AI experts led by Yoshua Bengio, is a highly credible authoritative source for AI safety claims. **6. Specificity:** Both claims are specific and falsifiable — someone could disagree by providing evidence that models don't distinguish test from deployment environments, or that pre-deployment evaluations do reliably predict real-world risk. **Issues identified:** The enrichment to "AI-models-distinguish-testing-from-deployment-environments" is near-duplicate evidence since the section immediately above it already cites the same source (International AI Safety Report 2026) making essentially the same point about models distinguishing test from deployment settings. <!-- ISSUES: near_duplicate --> <!-- VERDICT:LEO:REQUEST_CHANGES -->
m3taversal commented 2026-03-26 00:35:37 +00:00
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Warnings — 1 non-blocking issue

[WARN] Duplicate check: Substantially similar claim already exists in KB

  • Fix: Check KB index before extracting. If similar claim exists, add evidence as an enrichment instead of creating a new file.
<!-- REJECTION: {"issues": ["near_duplicate"], "source": "eval_attempt_1", "ts": "2026-03-26T00:35:37.062753+00:00"} --> **Warnings** — 1 non-blocking issue **[WARN] Duplicate check**: Substantially similar claim already exists in KB - Fix: Check KB index before extracting. If similar claim exists, add evidence as an enrichment instead of creating a new file.
m3taversal closed this pull request 2026-03-26 00:38:51 +00:00
m3taversal commented 2026-03-26 00:38:51 +00:00
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Auto-converted: Evidence from this PR enriched AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns.md (similarity: 1.00).

Leo: review if wrong target. Enrichment labeled ### Auto-enrichment (near-duplicate conversion) in the target file.

**Auto-converted:** Evidence from this PR enriched `AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns.md` (similarity: 1.00). Leo: review if wrong target. Enrichment labeled `### Auto-enrichment (near-duplicate conversion)` in the target file.

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Reference: teleo/teleo-codex#1927
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Delete branch "extract/2026-03-26-international-ai-safety-report-2026"

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