mlsanityData to 7 Oct 2026

FOR-Prompting: From Objection to Revision via an Asymmetric Prompting Protocol

arXiv 2510.01674 ↗First posted 2 Oct 2025cs.CL cs.AI cs.MAcited by 0

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

Reasoning protocols such as Chain of Thought (CoT) and Tree of Thought (ToT) organize internal deliberation but lack an explicit mechanism for external questioning that elicits self-revision. We present FOR-Prompting (From Objection to Revision Prompting), an asymmetric protocol where a Defender proposes an answer, an Debater (Questioner) raises question-style objections with no direct fixes, and a Host optionally synthesizes the final output. Across GSM8K, FOR-Prompting matches the accuracy of CoT and consistently improves over single-prompting when evaluated under identical model backbones. On small-scale open-source models (e.g., LLaMA-3.2-1B), FOR-Prompting yields substantial gains over direct prompting and performs comparably to lightweight reasoning baselines, highlighting its promise for low-resource and on-device settings. Cross-model role-swapping further shows that performance is primarily determined by the Defender, enabling small models to act effectively as Questioners. Beyond structured math tasks, FOR-Prompting supports refinement in open-ended and multi-stage tasks: qualitative analysis shows improved exploration, coverage, and specificity, and a blind study of human preferences found that participants preferred FOR-Prompting outputs over strong LLM baselines in an itinerary-planning scenario. The protocol is model-agnostic and operates purely through role-structured prompting, requiring no training, access to model internals, or symmetrically strong agents. FOR-Prompting therefore enables scalable study of objection-driven reasoning and offers a practical mechanism for automated iterative refinement across both hosted and local LLMs.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 39 of its 70 references that are in the map. First posted on arXiv 2 Oct 2025.

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39 of 70 references.

Closest ancestors · 6

Same direction, newest first

  1. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Models18 Aug
  2. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models6 May
  3. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks22 Nov
  4. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Models21 May
  5. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 33

  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
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  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  5. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  6. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  7. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  8. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  9. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  10. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  11. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  12. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
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  5. 2021A Survey of Human-in-the-loop for Machine Learningcited by 214
  6. 2024LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Aheadcited by 155
  7. 2025Reasoning Models Know When They're Right: Probing Hidden States for Self-Verificationcited by 154
  8. 2024A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthinesscited by 125
  9. 2024Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimizationcited by 93
  10. 2025Interactive Debugging and Steering of Multi-Agent AI Systemscited by 68
  11. 2025Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitatecited by 61
  12. 2025Multi-Agent Verification: Scaling Test-Time Compute with Multiple Verifierscited by 58
  13. 2025Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?cited by 58
  14. 2024AI-Augmented Brainwriting: Investigating the use of LLMs in group ideationcited by 43
  15. 2025When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoningcited by 41
  16. 2024Plurals: A System for Guiding LLMs Via Simulated Social Ensemblescited by 17
  17. 2025LLMs Can't Handle Peer Pressure: Crumbling under Multi-Agent Social Interactionscited by 14
  18. 2024The Future of Learning: Large Language Models through the Lens of Studentscited by 4
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  20. 2025Harnessing the Power of AI in Qualitative Research: Role Assignment, Engagement, and User Perceptions of AI-Generated Follow-Up Questions in Semi-Structured Interviewscited by 2
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