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Thinking Machines: A Survey of LLM based Reasoning Strategies

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arxiv 2503.10814 v1 pith:LCNFAEUP submitted 2025-03-13 cs.CL

Dibyanayan Bandyopadhyay , Soham Bhattacharjee , Asif Ekbal This is my paper
classification cs.CL
keywords reasoninglanguagemodelsllmsartificialintelligencesurveyabilities
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs) are highly proficient in language-based tasks. Their language capabilities have positioned them at the forefront of the future AGI (Artificial General Intelligence) race. However, on closer inspection, Valmeekam et al. (2024); Zecevic et al. (2023); Wu et al. (2024) highlight a significant gap between their language proficiency and reasoning abilities. Reasoning in LLMs and Vision Language Models (VLMs) aims to bridge this gap by enabling these models to think and re-evaluate their actions and responses. Reasoning is an essential capability for complex problem-solving and a necessary step toward establishing trust in Artificial Intelligence (AI). This will make AI suitable for deployment in sensitive domains, such as healthcare, banking, law, defense, security etc. In recent times, with the advent of powerful reasoning models like OpenAI O1 and DeepSeek R1, reasoning endowment has become a critical research topic in LLMs. In this paper, we provide a detailed overview and comparison of existing reasoning techniques and present a systematic survey of reasoning-imbued language models. We also study current challenges and present our findings.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Chain-of-Models: Cross-Model Auditing for Bias-Robust LLM Judges

    cs.CL 2026-05 conditional novelty 6.0 of 10

    Per-bias selection of a cross-family LLM auditor lifts biased-judgment accuracy from 0.805/0.824 baselines to 0.884.

  2. Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers

    cs.CL 2026-01 conditional novelty 6.0 of 10

    A prompt combining the 'Okay' reasoning cue with the '</think>\n\n' no-think cue gives LLMs an intermediate reasoning budget without training, and also speeds up RL fine-tuning.

  3. Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning

    cs.CL 2025-06 conditional novelty 6.0 of 10

    RAR improves role-playing agents by distilling character-grounded reasoning traces and optimizing the reasoning style to fit the dialogue scene.

  4. Where You Go is Who You Are: Behavioral Theory-Guided LLMs for Inverse Reinforcement Learning

    cs.AI 2025-05 conditional novelty 6.0 of 10

    SILIC uses LLM-guided inverse reinforcement learning and Theory of Planned Behavior chain reasoning to infer age, gender, income, and employment from travel trajectories, reportedly beating SVM, XGBoost, CatBoost, and...

  5. (EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations

    cs.CR 2026-07 reject novelty 5.0 of 10

    An event-centric, multi-agent LLM framework explains network alerts through hypothesis-driven, retrieval-augmented investigation and claims to improve explanation quality and boundary-case classification.

  6. Agentic Enterprise: AI-Centric User to User-Centric AI

    cs.AI 2025-06 unverdicted novelty 5.0 of 10

    Enterprise AI should be reorganized into a user-centric market of specialized agents guided by six tenets rather than built around general-purpose assistants.

  7. Simulating LLM-to-LLM Tutoring for Multilingual Math Feedback

    cs.CL 2025-06 conditional novelty 5.0 of 10

    A large LLM-to-LLM math tutoring simulation across 11 languages shows English-language hints often yield the largest accuracy gains for student models, but the low-resource-language results lack statistical support.

  8. See, Think, Learn: A Self-Taught Multimodal Reasoner

    cs.CV 2025-12 conditional novelty 4.0 of 10

    A self-training loop that structures model rationales into image-caption, reasoning, and conclusion, and adds negative rationales, improves VLM accuracy on M3CoT over answer-only and STaR baselines.

  9. First Finish Search: Efficient Test-Time Scaling in Large Language Models

    cs.CL 2025-05 conditional novelty 4.0 of 10

    First Finish Search launches n parallel reasoning traces and returns the shortest one, improving accuracy on AIME benchmarks while cutting token use.

  10. Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle

    cs.CL 2025-09 conditional novelty 3.0 of 10

    A survey that maps reinforcement learning methods, datasets, benchmarks, and open-source tools across the full training lifecycle of large language models, focusing on verifiable-reward reasoning.

  11. Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models

    cs.CL 2025-05 conditional novelty 2.0 of 10

    A survey-style position paper claims that reinforcement fine-tuning powers reasoning in multimodal LLMs, summarizing over a hundred recent works and proposing five future research directions.

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