</think> Doesn't Stop Reasoning: Analysis of Spurious CoT Termination

arXiv:2609.03633 · cs.CL, cs.AI · Submitted 2026-09-03 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-03

Updated: 2026-09-03

Code: https://github.com/Seunghee-Koh/Spurious-CoT-Termination

Terminology

• alpha — 64× in this paper · explained in 1 episode
• Benchmark — 30× in this paper · explained in 2 episodes
• Trace — 29× in this paper · explained in 2 episodes
• Accuracy — 28× in this paper · explained in 1 episode
• Transformer — 10× in this paper · explained in 2 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Exit-token Attention Biasing — 3× in this paper
• Thinking mode — 3× in this paper · explained in 2 episodes
• Chain-of-Thought (CoT) — 2× in this paper · explained in 4 episodes
• Chain-of-Thought (CoT) Reasoning — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Reasoning Traces — 2× in this paper · explained in 3 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Machine Unlearning — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• Thought Anchors: Which LLM Reasoning Steps Matter?
• Training Verifiers to Solve Math Word Problems
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach
• Gradient-Stable Attention Heads Signal LLM Correctness
• Thinking Sparks!: Emergent Attention Heads in Reasoning Models During Post Training
• Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
• Kimi k1.5: Scaling Reinforcement Learning with LLMs
• Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models
• Chain of Draft: Thinking Faster by Writing Less
• Qwen3 Technical Report
• Distilling System 2 into System 1
• When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning

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