CoT Monitoring

CoT: Chain-of-Thought

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Latest papers 21

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Reasoning-Token Spikes Under Prompted Untruthful Responding in Large Language Models
Oct 7, 2026Maverick Morales, Tomáš Dominik, Vermut Gao +4Deception in Language Models LLM Auditing
Chapman University, CA, USA · INSERM U992, Cognitive Neuroimaging Unit, NeuroSpin Center, Gif sur Yvette 91191, France · University of California, Los Angeles, CA, USA +1
On Language Drift during RLVR Post-Training
Oct 1, 2026Michael Sullivan, Alexander KollerRL for Language Model Reasoning Reinforcement Learning with Verifiable Rewards
Department of Language Science and Technology, Saarland University, Saarbrücken, Germany.
Hidden Reasoning Must Leak, but Need Not Be Readable: Fundamental Opportunities and Limits for Chain-of-Thought Monitoring
Sep 29, 2026Mohammadali Mohammadkhani, Madhava Krishna, Yash Sarrof +1CoT Monitoring Large Reasoning Models
Saarland University, Saarland Informatics Campus · Zuse School ELIZA
From Decorative to Load-Bearing: Task Difficulty Shapes the Causal Role of Chain-of-Thought
Sep 21, 2026Renee Jia, Di MuCoT Faithfulness Causal Interventions in Language Models
R2M AI, Cornell University · R2M AI, Carnegie Mellon University
Corrupt Plans, Clean Traces: Evading Chain-of-Thought Monitoring with Plan Injection
Sep 14, 2026Keertana Chidambaram, Andrew Ilyas, Vasilis SyrgkanisLLM Security Prompt Injection Attacks on AI Agents
Stanford University · CMU
The Answer Is Not the Argument
Aug 31, 2026Will Yeadon, Sergio Juárez, Paul Mackay +5LLM Answer Verification CoT Monitoring
Department of Physics, Durham University, Durham DH1 3LE, UK · Escuela de Ingenier´ıa de Telecomunicaci´on, Department of Signal Theory and Communications, University of Vigo, Vigo E-36310, Spain · Independent Researcher
Does Out-of-Sight Equal Out-of-Mind in CoT Monitorability?
Aug 5, 2026Pedro Ferreira, Wilker Aziz, Ivan TitovCoT Monitoring Latent CoT Reasoning
University of Amsterdam · University of Edinburgh
Chain-of-Thought Monitoring Can Be Unreliable in Implicit-Influence Settings
Aug 5, 2026Agatha Duzan, Asa Cooper SticklandLLM Safety Benchmarks CoT Monitoring
EPFL · UK AI Security Institute
Evading Chain-of-Thought Monitoring Through Model Poisoning
Aug 3, 2026Giorgio Severi, Shujaat Mirza, Blake Bullwinkel +1LLM Backdoor Attacks LLM Auditing
Microsoft
Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring
Jul 9, 2026Jennifer Za, Julija Bainiaksina, Nikita Ostrovsky +2Language Model Safety Evaluation Adversarial Attacks on LLMs
LASR Labs · University College London · Google DeepMind
Length Penalties Make Chain-of-Thought Less Monitorable
Jul 8, 2026Bryce LittleCoT Faithfulness Overthinking in Language Models
Can Reasoning Models Detect Changes to their Chains of Thought?
Jun 20, 2026Sathvik Napa, Utkarsh Singh, Chengyuan Xue +2Causal Interventions in Language Models Metacognition in Language Models
Johns Hopkins University
Bootstrapped Monitoring: Leveraging Transparent Reasoning to Oversee Stronger AI Agents
Jun 10, 2026Frank Xiao, Mary PhuongAI Agent Safety AI Agent Monitoring
California Institute of Technology
The Fragility of Chain-of-Thought Monitoring Across Typologically Diverse Languages
May 27, 2026Eric Onyame, Runtao Zhou, Kowshik Thopalli +2CoT Faithfulness Multilingual Language Model Evaluation
University of Virginia · Lawrence Livermore National Laboratory
Training on Documents About Monitoring Leads to CoT Obfuscation
May 14, 2026Reilly Haskins, Bilal Chughtai, Joshua EngelsCoT Monitoring
University of Canterbury · Google DeepMind · Pivotal Research
CoT-Guard: Small Models for Strong Monitoring
May 12, 2026Nirav Diwan, Han Wang, Berkcan Kapusuzoglu +6LLM Auditing Adversarial Attacks on LLMs
†University of Illinois Urbana-Champaign · ♢Capital One
Behavior Cue Reasoning: Monitorable Reasoning Improves Efficiency and Safety through Oversight
May 7, 2026Christopher Z. Cui, Taylor W. Killian, Prithviraj AmmanabroluScalable Oversight LLM Safety
University of California, San Diego · 2Brigham Young University
Detecting and Suppressing Reward Hacking with Gradient Fingerprints
Apr 17, 2026Songtao Wang, Quang Hieu Pham, Fangcong Yin +4Reward Hacking Reinforcement Learning with Verifiable Rewards
AUniversity of Alberta · N New York University · LLMU Munich +1
MonitorBench: A Comprehensive Benchmark for Chain-of-Thought Monitorability in Large Language Models
Mar 30, 2026Han Wang, Yifan Sun, Brian Ko +8CoT Faithfulness LLM Evaluation
University of Illinois Urbana-Champaign · University of Washington · University of California San Diego
NEST: Nascent Encoded Steganographic Thoughts
Feb 15, 2026Artem KarpovLanguage Model Safety Evaluation LLM Auditing
Independent.
A False Average: Pooled CoT-Monitor Accuracy Conceals a Reasoning-Dependent Fragility
Date pendingShikhar Shiromani, Leo RichterReward Hacking Adversarial Attacks on LLMs
Pivotal Research · University College London

Oct 7, 2026, cs.AIJ/K move · Enter open · S save

Reasoning-Token Spikes Under Prompted Untruthful Responding in Large Language Models

Maverick Morales, Tomáš Dominik, Vermut Gao, Katrina Shirey+3

Chapman University, CA, USA · INSERM U992, Cognitive Neuroimaging Unit, NeuroSpin Center, Gif sur Yvette 91191, France · University of California, Los Angeles, CA, USA+1

Deception in Language Models LLM Auditing CoT Monitoring

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Monitoring the chain-of-thought of reasoning artificial intelligence (AI) models remains a key approach to detecting deception and other forms of misbehavior in such models. However, semantic chain-of-thought monitoring depends on reasoning traces being legible and sufficiently faithful to the underlying computations that produced the model's behavior, not to mention accessible. Moreover, there is increasing evidence that chain-of-thought outputs may soon become illegible or unfaithful, if they even remain accessible. Based on cognitive load theory, we investigate a lower-bandwidth signal -- the number of reasoning tokens generated -- which does not require access to the content of the reasoning trace. Three reasoning-capable large language models answered 210 multiple-choice questions -- across analytic, descriptive, and normative reasoning types as well as moral and non-moral domains -- under system prompts instructing them to respond truthfully, falsely, or without regard for truth. Across all three models, truth-directed responding elicited fewer reasoning tokens than both lie-directed and truth-indifferent responding. These findings show that explicitly prompted untruthful response policies can produce robust group-level differences in test-time reasoning-token use. While not yet establishing reasoning-token count as a detector of spontaneous deception or general misalignment, our results are a proof of concept that it can serve as a simple, content-independent candidate signal for differentiating untruthful from truthful model behavior when raw reasoning traces are unavailable or unreliable. Future work should test instance-level detection rates, out-of-distribution generalization, learned deceptive policies, hidden objectives, and robustness under adversarial pressure.

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