mlsanityData to 14 Oct 2026

When Confidence Rises Too Early: Detecting Shortcut Reasoning via Premature Answer Commitment

arXiv 2609.35074 ↗First posted 28 Sep 2026cs.CLcited by 0

On the map: LLM reasoning → Test-time compute and efficient thinking also fits: Reasoning benchmarks and evaluation

Abstract (arXiv)

The reasoning trajectory of a Large Language Model (LLM) is often treated as a verbalized description of its internal reasoning. However, such trajectories can be unfaithful: a model may rely on shortcuts to reach an answer and then post-rationalize the decision with a seemingly coherent chain of thought. Detecting this shortcut reasoning is challenging because existing monitors and verifiers mainly inspect textual traces or final outcomes, rather than how the model's belief in its answer develops during generation. We introduce ConfLens, a framework that tracks the evolution of confidence in the final answer throughout reasoning. Across three shortcut reasoning settings, we observe a common pattern of premature confidence, where shortcut samples become highly confident in the final answer at early reasoning stages. Existing confidence estimation methods, however, show limited generalizability, reliability, or efficiency for detecting this behavior. We therefore propose the Distributional Answer Commitment Score (DACS), a distributional confidence estimator that measures the entropy of the model's probability distribution over answer commitment at each reasoning step. DACS captures how concentrated the model's answer belief is without requiring ground-truth answers or task-specific verifiers. We further convert ConfLens detection results into interpretable signals for reward models to reduce their preference for shortcut reasoning. Experiments on mathematical and code reasoning tasks show that ConfLens with DACS improves shortcut reasoning detection by over 4.3% F1 compared with strong baselines and reduces the mismatch between faithfulness and correctness in reward model preferences.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 32 of its 60 references that are in the map. First posted on arXiv 28 Sep 2026.

Builds on

32 of 60 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026Probing the Trajectories of Reasoning Traces in Large Language Models30 Jan
  2. 2025Making Slow Thinking Faster: Compressing LLM Chain-of-Thought via Step Entropy5 Aug
  3. 2022Language Models (Mostly) Know What They Know11 Jul

Other references · 29

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  3. 2025Qwen3 Technical Reportcited by 8,820
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  5. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  6. 2024Qwen2.5 Technical Reportcited by 4,942
  7. 2024GPT-4o System Cardcited by 4,588
  8. 2024OpenAI o1 System Cardcited by 1,244
  9. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 703
  10. 2025Reasoning Models Don't Always Say What They Thinkcited by 415
  11. 2025Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscationcited by 384
  12. 2024Alignment faking in large language modelscited by 360
17 more
  1. 2025Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safetycited by 249
  2. 2025Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergycited by 214
  3. 2025RewardBench 2: Advancing Reward Model Evaluationcited by 150
  4. 2025Natural Emergent Misalignment from Reward Hacking in Production RLcited by 136
  5. 2025Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Modelscited by 109
  6. 2025When Chain of Thought is Necessary, Language Models Struggle to Evade Monitorscited by 88
  7. 2025School of Reward Hacks: Hacking harmless tasks generalizes to misaligned behavior in LLMscited by 66
  8. 2025CoT Red-Handed: Stress Testing Chain-of-Thought Monitoringcited by 54
  9. 2026Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thoughtcited by 40
  10. 2025Is It Thinking or Cheating? Detecting Implicit Reward Hacking by Measuring Reasoning Effortcited by 27
  11. 2026Reasoning Models Struggle to Control their Chains of Thoughtcited by 25
  12. 2026Counterfactual Simulation Training for Chain-of-Thought Faithfulnesscited by 17
  13. 2025Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thoughtcited by 15
  14. 2026Adversarial Reward Auditing for Active Detection and Mitigation of Reward Hackingcited by 11
  15. 2025Truthful or Fabricated? Using Causal Attribution to Mitigate Reward Hacking in Explanationscited by 9
  16. 2026Detecting and Suppressing Reward Hacking with Gradient Fingerprintscited by 5
  17. 2026Monitorability as a Free Gift: How RLVR Spontaneously Aligns Reasoningcited by 4

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  1. 2026Understanding and Mitigating Premature Confidence for Better LLM Reasoning
  2. 2026Early Stopping for Large Reasoning Models via Confidence Dynamics
  3. 2026CALIBER: Calibrating Confidence Before and After Reasoning in Language Models
  4. 2026Large Language Models Decide Early and Explain Later
  5. 2025Let the Model Distribute Its Doubt: Confidence Estimation through Verbalized Probability Distribution
  6. 2026Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models
  7. 2025Answer-Centric or Reasoning-Driven? Uncovering the Latent Memory Anchor in LLMs
  8. 2026Post-Training Shifts Confidence: A Three-Stage Analysis of How SFT, RL, and OPD Shape CoT Calibration