Monitorability Disposition in Large Reasoning Models
cs.AI, cs.CL, cs.LG
Submitted: 2026-10-04
Updated: 2026-10-04
Sources
- Chain-of-Thought Reasoning In The Wild Is Not Always Faithful
- CoT Red-Handed: Stress Testing Chain-of-Thought Monitoring
- Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation
- Reasoning Models Don't Always Say What They Think
- DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
- Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models
- Output Supervision Can Obfuscate the Chain of Thought
- When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors
- Time Travel in LLMs: Tracing Data Contamination in Large Language Models
- Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models
- Intent Laundering: AI Safety Datasets Are Not What They Seem
- Alignment faking in large language models
- Monitoring Monitorability
- Training LLMs for Honesty via Confessions
- Reliable Weak-to-Strong Monitoring of LLM Agents
- Kimi K2.5: Visual Agentic Intelligence
- Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
- Measuring Faithfulness in Chain-of-Thought Reasoning
- Training Agents to Self-Report Misbehavior
- Spilling the Beans: Teaching LLMs to Self-Report Their Hidden Objectives
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