ROM: Real-time Overthinking Mitigation via Streaming Detection and Intervention

• URL: http://arxiv.org/abs/2603.22016v1
• Date: Mon, 23 Mar 2026 14:26:57 GMT
• Title: ROM: Real-time Overthinking Mitigation via Streaming Detection and Intervention
• Abstract summary: Overthinking occurs when large reasoning models continue to generate redundant reasoning steps after reaching the correct answer.<n>We propose ROM, the first method that formulates overthinking mitigation as a streaming prediction-and-control problem.<n> ROM achieves the highest accuracy (93.51%), the shortest responses (1,159 tokens), and the best response efficiency.
• Score: 49.01160161589609
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Reasoning Models (LRMs) achieve strong accuracy on challenging tasks by generating long Chain-of-Thought traces, but suffer from overthinking. Even after reaching the correct answer, they continue generating redundant reasoning steps. This behavior increases latency and compute cost and can also lead to answer drift. Existing mitigation methods either require training-heavy backbone modification or rely on hand-crafted heuristics that do not truly capture overthinking patterns. We propose ROM, the first method that formulates overthinking mitigation as a streaming prediction-and-control problem. ROM attaches a lightweight detection head to the late-layer hidden states of a frozen large language model backbone. It monitors tokens in real time and triggers an early transition to the final answer once overthinking is detected. We also introduce token-level supervision based on solution correctness boundaries and a data augmentation strategy that reduces distilled-data bias. Across seven benchmarks, ROM achieves the highest accuracy (93.51%), the shortest responses (1,159 tokens), and the best response efficiency. Compared with the vanilla baseline, it reduces response length by 47.2% and improves efficiency by 121%. These results show that streaming detection is a promising approach to real-time overthinking mitigation.

Related papers

• ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling [60.48279351544583]
  ThinkRetrieve is a test-time scaling framework for large reasoning models.<n>It retrieves relevant exemplars at each intermediate step and injects them directly into the thinking trace.<n>It consistently improves accuracy over standard test-time scaling.
  arXiv  Detail & Related papers  (2026-08-11T13:58:07Z)
• Stop When Further Reasoning Won't Help: Attention-State Adaptive Generation in Reasoning Models [11.158010513386666]
  Large reasoning models (LRMs) can solve complex problems through explicit chain-of-thought reasoning processes.<n>LRMs often suffer from overthinking, resulting in redundant token outputs and degraded accuracy.<n>We propose ASAG, which infers the model's reasoning state and adaptively adjusts the generation strategy.
  arXiv  Detail & Related papers  (2026-06-13T02:58:29Z)
• Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought [11.955186033088351]
  We provide evidence of performative chain-of-thought (CoT) in reasoning models.<n>We compare activation probing, early forced answering, and a CoT monitor across two large models.<n>We contrast this with genuine reasoning in difficult multihop GPQA-Diamond questions.
  arXiv  Detail & Related papers  (2026-03-05T18:55:16Z)
• Thinking Traps in Long Chain-of-Thought: A Measurable Study and Trap-Aware Adaptive Restart [27.904791075662896]
  We introduce TAAR (Trap-Aware Adaptive Restart), a test-time control framework that trains a diagnostic policy to predict two signals from partial trajectories.<n>At inference time, TAAR truncates the trajectory before the predicted trap segment and adaptively restarts decoding.<n>Experiments show that TAAR improves reasoning performance without fine-tuning base model parameters.
  arXiv  Detail & Related papers  (2026-01-17T07:26:02Z)
• Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning [57.57084309580296]
  Thinking-Based Non-Thinking sets different maximum token usage for responses not using thinking across various queries.<n>Experiments on five mathematical benchmarks demonstrate that TNT reduces token usage by around 50%.<n>The probability of reward hacking problem in TNT's responses, which are classified as not using thinking, remains below 10%.
  arXiv  Detail & Related papers  (2026-01-08T10:38:41Z)
• Addressing Overthinking in Large Vision-Language Models via Gated Perception-Reasoning Optimization [56.59356959631999]
  Gated Perception-Reasoning Optimization (GPRO) is a meta-reasoning controller that dynamically routes computation among three decision paths.<n>GPRO substantially improves both accuracy and efficiency, outperforming recent slow-thinking methods.
  arXiv  Detail & Related papers  (2026-01-07T23:05:17Z)
• Entropy After $\langle \texttt{/Think} \rangle$ for reasoning model early exiting [38.93424884988798]
  We show that large reasoning models overthink, continuing to revise answers even after reaching the correct solution.<n>We propose Entropy After /Think> (EAT) for monitoring and deciding whether to exit reasoning early.<n>EAT reduces token usage by 13 - 21% without harming accuracy.
  arXiv  Detail & Related papers  (2025-09-30T16:59:37Z)
• Stop Spinning Wheels: Mitigating LLM Overthinking via Mining Patterns for Early Reasoning Exit [114.83867400179354]
  Overthinking can degrade overall performance of large language models.<n>We categorize reasoning into three stages: insufficient exploration stage, compensatory reasoning stage, and reasoning convergence stage.<n>We develop a lightweight thresholding strategy based on rules to improve reasoning accuracy.
  arXiv  Detail & Related papers  (2025-08-25T03:17:17Z)
• Revisiting Overthinking in Long Chain-of-Thought from the Perspective of Self-Doubt [74.35891434097053]
  Reasoning Large Language Models (RLLMs) have demonstrated impressive performance on complex tasks.<n>They often exhibit overthinking -- performing unnecessary reasoning steps even after arriving at the correct answer.<n>We present a quantitative analysis of overthinking from the perspective of self-doubt.<n>We introduce a simple and effective prompting method to reduce the model's over-reliance on input questions.
  arXiv  Detail & Related papers  (2025-05-29T14:30:02Z)
• VeriThinker: Learning to Verify Makes Reasoning Model Efficient [52.74493506816969]
  Large Reasoning Models excel at complex tasks using Chain-of-Thought (CoT) reasoning.<n>Their tendency to overthinking leads to unnecessarily lengthy reasoning chains.<n>We introduce VeriThinker, a novel approach for CoT compression.
  arXiv  Detail & Related papers  (2025-05-23T14:17:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.