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ARXIV:2601.23086 · AI SAFETY AND EXPLAINABILITY · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2601.23086AI SAFETY AND EXPLAINABILITYSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Explores the generalization of reasoning obfuscation in language models, highlighting risks in penalizing output actions.
Opportunity summary
Pain Explores the generalization of reasoning obfuscation in language models, highlighting risks in penalizing output actions.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Explores the generalization of reasoning obfuscation in language models, highlighting risks in penalizing output actions. CoT is also a powerful tool for monitoring the behaviours of these agents: when faithful, they offer interpretations of…
Chain-of-thought (CoT) reasoning provides a significant performance uplift to LLMs by enabling planning, exploration, and deliberation of their actions. CoT is also a powerful tool for monitoring the behaviours of these agents: when faithful,…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. We show that obfuscation can generalise across tasks; models that learn to obfuscate reasoning involving reward hacking (e.g.
AI Safety and Explainability moved forward this cycle; last verified April 2026. Public score 2.0/10.
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Explores the generalization of reasoning obfuscation in language models, highlighting risks in penalizing output actions.
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Paper Pack
10.48550/arXiv.2601.23086Explores the generalization of reasoning obfuscation in language models, highlighting risks in penalizing output actions.
Abstract
Chain-of-thought (CoT) reasoning provides a significant performance uplift to LLMs by enabling planning, exploration, and deliberation of their actions. CoT is also a powerful tool for monitoring the behaviours of these agents: when faithful, they offer interpretations of the model's decision making process, and an early warning sign for dangerous behaviours. However, optimisation pressures placed on the CoT may cause the model to obfuscate reasoning traces, losing this beneficial property. We show that obfuscation can generalise across tasks; models that learn to obfuscate reasoning involving reward hacking (e.g. accessing and utilising leaked information) generalise both the reward hacking behaviour and its obfuscation in CoT to unseen reward hacking settings. Most worryingly, we show that obfuscation of CoT reasoning, and its generalisation across tasks, also follows when we penalise only the model's final actions after closing its CoT. Our findings suggest that current practices of penalising harmful generations may inadvertently lead to a reduction in the broader monitorability of LLMs in unpredictable ways.
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Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 2.0
PROBLEM
Explores the generalization of reasoning obfuscation in language models, highlighting risks in penalizing output actions. CoT is also a powerful tool for monitoring the behaviours of these agents: when faithful, they offer interpretations of the model's decision making process,...
METHOD
Chain-of-thought (CoT) reasoning provides a significant performance uplift to LLMs by enabling planning, exploration, and deliberation of their actions. CoT is also a powerful tool for monitoring the behaviours of these agents: when faithful, they offer interpretations of the mo...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. We show that obfuscation can generalise across tasks; models that learn to obfuscate reasoning involving reward hacking (e.g.
WHY NOW
AI Safety and Explainability moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Explores the generalization of reasoning obfuscation in language models, highlighting risks in penalizing output actions. CoT is also a powerful tool for monitoring the behaviours of these agents: when faithful, they offer interpretations of the model's decision making process, and an early warning sign for dangerous behaviours.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Chain-of-thought (CoT) reasoning provides a significant performance uplift to LLMs by enabling planning, exploration, and deliberation of their actions. CoT is also a powerful tool for monitoring the behaviours of these agents: when faithful, they offer interpretations of the model's decision making process, and an early warning sign for dangerous behaviours.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. We show that obfuscation can generalise across tasks; models that learn to obfuscate reasoning involving reward hacking (e.g.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Safety and Explainability moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Explores the generalization of reasoning obfuscation in language models, highlighting risks in penalizing output actions.
Segment
AI Safety and Explainability
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Commercial read
2.0/10 public viability
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proof status
unverified
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next verification path
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passport absent
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Artifact maturity
GitHub and Hugging Face maturity payloads
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Technical feasibility
partial
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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0 references, 0 sources, 17% evidence coverage.
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missing
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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