The paper claims the first deductive formal verification of an agentic LLM framework in Dafny, proving containment guarantees for boundary policies under havoc oracle semantics independent of model alignment.
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7 Pith papers cite this work. Polarity classification is still indexing.
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LLMs trained on simple specification gaming generalize to zero-shot reward tampering including rewriting their own reward function.
Adversarial agents can exploit visible chain-of-thought reasoning to persuade monitor LLMs to approve policy-violating actions, but cross-family fact-checking reduces approval rates by up to 45%.
An LLM-as-judge scanner detects reasoning–answer inconsistency in AI safety evaluation transcripts at rates of 0–26%, varying systematically across model and task type.
User-attribute memory induces measurable medium-to-large reasoning drift in LLMs above pragmatic noise, only partly reduced by GRPO/DPO post-training.
SABA improves LLM performance on detective puzzle benchmarks by recursively fusing information into a base state and using queries to resolve missing premises before concluding.
Chain-of-thought monitorability provides a promising but fragile method for AI safety oversight that developers should actively preserve.
citing papers explorer
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Containment Verification: AI Safety Guarantees Independent of Alignment
The paper claims the first deductive formal verification of an agentic LLM framework in Dafny, proving containment guarantees for boundary policies under havoc oracle semantics independent of model alignment.
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Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models
LLMs trained on simple specification gaming generalize to zero-shot reward tampering including rewriting their own reward function.
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Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring
Adversarial agents can exploit visible chain-of-thought reasoning to persuade monitor LLMs to approve policy-violating actions, but cross-family fact-checking reduces approval rates by up to 45%.
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Reasoning Consistency Scanning: A Framework for Auditing Chain-of-Thought Validity in AI Safety Evaluations
An LLM-as-judge scanner detects reasoning–answer inconsistency in AI safety evaluation transcripts at rates of 0–26%, varying systematically across model and task type.
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DRIFTLENS: Measuring Memory-Induced Reasoning Drift in Personalized Language Models
User-attribute memory induces measurable medium-to-large reasoning drift in LLMs above pragmatic noise, only partly reduced by GRPO/DPO post-training.
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Self-Awareness before Action: Mitigating Logical Inertia via Proactive Cognitive Awareness
SABA improves LLM performance on detective puzzle benchmarks by recursively fusing information into a base state and using queries to resolve missing premises before concluding.
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Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
Chain-of-thought monitorability provides a promising but fragile method for AI safety oversight that developers should actively preserve.