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"A framework for improving Chain-of-Thought monitor accuracy in LLMs using information theory and targeted training objectives."

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References (33)

[1]
Revisiting LLM Reasoning via Information Bottleneck
2025Shiye Lei, Zhihao Cheng et al.
[2]
Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
2025Tomasz Korbak, Mikita Balesni et al.
[3]
When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors
2025Scott Emmons, Erik Jenner et al.
[4]
Early Signs of Steganographic Capabilities in Frontier LLMs
2025Artur Zolkowski, Kei Nishimura-Gasparian et al.
[5]
Large language models can learn and generalize steganographic chain-of-thought under process supervision
2025Joey Skaf, Luis Ibañez-Lissen et al.
[6]
REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards
2025Zafir Stojanovski, Oliver Stanley et al.
[7]
Reasoning Models Don't Always Say What They Think
2025Yanda Chen, Joe Benton et al.
[8]
Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation
2025Bowen Baker, Joost Huizinga et al.
[9]
Chain-of-Thought Reasoning In The Wild Is Not Always Faithful
2025Iv'an Arcuschin, Jett Janiak et al.
[10]
MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking
2025Sebastian Farquhar, Vikrant Varma et al.
[11]
Alignment faking in large language models
2024R. Greenblatt, Carson E. Denison et al.
[12]
Frontier Models are Capable of In-context Scheming
2024Alexander Meinke, Bronson Schoen et al.
[13]
Understanding Chain-of-Thought in LLMs through Information Theory
2024Jean-François Ton, Muhammad Faaiz Taufiq et al.
[14]
Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs
2024Yohan Mathew, Ollie Matthews et al.
[15]
Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning
2024Debjit Paul, Robert West et al.
[16]
InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling
2024Yuchun Miao, Sen Zhang et al.
[17]
Secret Collusion among AI Agents: Multi-Agent Deception via Steganography
2024S. Motwani, Mikhail Baranchuk et al.
[18]
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
2024Zhihong Shao, Peiyi Wang et al.
[19]
Reward Model Ensembles Help Mitigate Overoptimization
2023Thomas Coste, Usman Anwar et al.
[20]
LEACE: Perfect linear concept erasure in closed form
2023Nora Belrose, David Schneider-Joseph et al.

Showing 20 of 33 references