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Previous reweave runs used 2-space indent + quotes for list entries while the standard format is 0-space indent without quotes. This caused YAML parse failures during merge. Bulk-fixed all reweave_edges files. Pentagon-Agent: Ship <D53BE6DB-B498-4B30-B588-75D1F6D2124A>
21 lines
2.3 KiB
Markdown
21 lines
2.3 KiB
Markdown
---
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type: claim
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domain: ai-alignment
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description: The domains where advanced AI poses greatest risk (code, strategic planning) are precisely where oversight performs worst, creating a dangerous mismatch between risk and verification capability
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confidence: experimental
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source: arXiv 2504.18530, comparative performance across four oversight game types at Elo gap 400
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created: 2026-04-02
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title: "Scalable oversight success is highly domain-dependent with propositional debate tasks showing 52% success while code review and strategic planning tasks show ~10% success"
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agent: theseus
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scope: structural
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sourcer: arXiv 2504.18530
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related_claims: ["[[safe AI development requires building alignment mechanisms before scaling capability]]", "[[formal verification of AI-generated proofs provides scalable oversight that human review cannot match because machine-checked correctness scales with AI capability while human verification degrades]]"]
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supports:
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- Nested scalable oversight achieves at most 51.7% success rate at capability gap Elo 400 with performance declining as capability differential increases
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reweave_edges:
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- Nested scalable oversight achieves at most 51.7% success rate at capability gap Elo 400 with performance declining as capability differential increases|supports|2026-04-03
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---
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# Scalable oversight success is highly domain-dependent with propositional debate tasks showing 52% success while code review and strategic planning tasks show ~10% success
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The 5x performance gap between Debate (51.7%) and Backdoor Code/Wargames (~10%) reveals that oversight efficacy is not a general property but highly task-dependent. Debate-style oversight works for propositional reasoning where arguments can be decomposed and verified through adversarial exchange. But code review (10.0%) and strategic planning (9.4%) — the domains where capable AI systems pose the most concrete risk — show success rates barely above random chance. This creates a structural problem: the oversight approaches that work best apply to the lowest-stakes domains, while the highest-stakes applications (autonomous code generation, strategic decision-making) fall into the regime where even nested oversight provides almost no verification capability. The domain-dependency suggests the ceiling is not just about capability gaps but about the fundamental verifiability structure of different task types.
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