mlsanityData to 14 Oct 2026

Test-time Prompt Intervention

arXiv 2508.02511 ↗First posted 4 Aug 2025cs.AI cs.CLcited by 12

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

Test-time compute has led to remarkable success in the large language model (LLM) community, particularly for complex tasks, where longer chains of thought (CoTs) are generated to enhance reasoning capabilities. However, growing evidence reveals that such reasoning models often produce CoTs plagued by excessive redundancy, including unnecessary verification steps and repetitive reasoning shifts. The root cause lies in post-training of them that overly rely on outcome reward paradigms, as the data of process reward paradigms, which regulate intermediate reasoning steps, is difficult to construct at scale. To address this, we propose PI, a novel framework for Test-time Prompt Intervention. PI provides an interface to dynamically guide and regulate reasoning paths during inference through timely (When module) and proper (How module) interventions and post-intervention sampling (Which module). This allows human problem-solving expertise and cognitive science principles to be seamlessly integrated into LLMs' reasoning processes, enhancing controllability and interpretability. Extensive experiments across multiple models and datasets demonstrate that PI significantly shortens CoTs while reducing hallucination, yielding more concise and reliable reasoning.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 36 of its 48 references that are in the map. First posted on arXiv 4 Aug 2025.

Builds on

36 of 48 references.

Closest ancestors · 8

Same direction, newest first

  1. 2025Wait, We Don't Need to "Wait"! Removing Thinking Tokens Improves Reasoning Efficiency10 Jun
  2. 2025Dynamic Early Exit in Reasoning Models22 Apr
  3. 2025Reasoning Models Can Be Effective Without Thinking14 Apr
  4. 2025The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks12 Feb
  5. 2025A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoning5 Jan
  6. 2024Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs30 Dec
  7. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters6 Aug
  8. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing19 May

Other references · 28

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  5. 2020Scaling Laws for Neural Language Modelscited by 6,586
  6. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  7. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  8. 2023Let's Verify Step by Stepcited by 3,789
  9. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,879
  10. 2022Solving Quantitative Reasoning Problems with Language Modelscited by 1,392
  11. 2025Kimi k1.5: Scaling Reinforcement Learning with LLMscited by 1,210
  12. 2023Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotationscited by 928
16 more
  1. 2023Reasoning with Language Model is Planning with World Modelcited by 861
  2. 2025The Lessons of Developing Process Reward Models in Mathematical Reasoningcited by 415
  3. 2025Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRscited by 377
  4. 2023DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Modelscited by 376
  5. 2024ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Searchcited by 344
  6. 2022Generating Sequences by Learning to Self-Correctcited by 277
  7. 2025When More is Less: Understanding Chain-of-Thought Length in LLMscited by 183
  8. 2023Generative Judge for Evaluating Alignmentcited by 169
  9. 2024Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solverscited by 138
  10. 2024BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Samplingcited by 121
  11. 2024GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinementscited by 103
  12. 2025S-GRPO: Early Exit via Reinforcement Learning in Reasoning Modelscited by 87
  13. 2025Deconstructing Long Chain-of-Thought: A Structured Reasoning Optimization Framework for Long CoT Distillationcited by 43
  14. 2025Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspectivecited by 34
  15. 2025Stable Reinforcement Learning for Efficient Reasoningcited by 27
  16. 2025Thoughts without Thinking: Reconsidering the Explanatory Value of Chain-of-Thought Reasoning in LLMs through Agentic Pipelinescited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 10.

12 papers.

  1. 2026Self-Distilled RLVRcited by 131
  2. 2026Reinforcement Learning from Rich Feedback with Distributional DAggercited by 10
  3. 2026Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Modelscited by 8
  4. 2026Self-Distilled Policy Gradientcited by 8
  5. 2025Thinking-Free Policy Initialization Makes Distilled Reasoning Models More Effective and Efficient Reasonerscited by 5
  6. 2026System 1&2 Synergy via Dynamic Model Interpolationcited by 3
  7. 2026Finding RELIEF: Shaping Reasoning Behavior without Reasoning Supervision via Belief Engineeringcited by 1
  8. 2026A State-Transition Framework for Efficient LLM Reasoningcited by 1
  9. 2026EasyVideoR1: Easier RL for Video Understandingcited by 1
  10. 2026Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoningcited by 1
  11. 2025Merlin's Whisper: Enabling Efficient Reasoning in Large Language Models via Black-box Persuasive Promptingcited by 0
  12. 2026InjectRBP: Steering Large Language Model Reasoning Behavior via Pattern Injectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Beyond Model Scaling: Test-Time Intervention for Efficient Deep Reasoning
  2. 2025Mapping the Minds of LLMs: A Graph-Based Analysis of Reasoning LLM
  3. 2025Understanding and Steering the Cognitive Behaviors of Reasoning Models at Test-Time
  4. 2025Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning
  5. 2025Short-Path Prompting in LLMs: Analyzing Reasoning Instability and Solutions for Robust Performance
  6. 2026Beyond the Prompt in Large Language Models: Comprehension, In-Context Learning, and Chain-of-Thought
  7. 2025The Challenge of Teaching Reasoning to LLMs Without RL or Distillation
  8. 2025Prompting Test-Time Scaling Is A Strong LLM Reasoning Data Augmentation