Papers with CoT.
DCMKC: A Dual Consistency Matching Approach for Multi-hop Question Answering in LLMs (2025.findings-emnlp)
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| Challenge: | Existing reasoning based on chains of thought (CoTs) fails to find logical connections between reasoning steps . |
| Approach: | They propose a method to match KG reasoning chains with CoTs based on semantic similarity . they use a knowledge graph to find relevant information "within" each reasoning step . |
| Outcome: | The proposed method outperforms baselines on multi-answer questions with 5.1% improvement over baselines. |
FROST: Factual Reasoning via Optimized Stochastic Trajectories in Large Language Models during Inference (2026.acl-industry)
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| Challenge: | Existing mitigation strategies are needed to improve large language models' reliability and efficiency. |
| Approach: | They propose an inference-time framework that balances exploration andexploitation without additional training or context augmentation. |
| Outcome: | FROST achieves 2–5 percentage point improvements over standard chain-of-thoughtprompting and reduces unsupported outputs by 40% relative to Standard CoT. |
Mixed Distillation Helps Smaller Language Models Reason Better (2024.findings-emnlp)
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| Challenge: | Recent large language models (LLMs) have demonstrated impressive multiple step-by-step reasoning capabilities in recent NLP reasoning tasks. |
| Approach: | They propose a mixed distillation framework that distills multiple step-by-step reasoning abilities into smaller language models (SLMs) they leverage LLMs to generate multiple step by step reasoning rationales by sampling automatically. |
| Outcome: | The proposed framework outperforms existing models on SVAMP, GSM8K and ASDIV, while a single model generated by MD exceeds the comprehensive performance of two individual CoT and PoT distilled models. |
To Code or not to Code? Adaptive Tool Integration for Math Language Models via Expectation-Maximization (2025.findings-acl)
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| Challenge: | Existing tools that integrate chain-of-thought reasoning and code execution lack metacognitive awareness to integrate tools. |
| Approach: | They propose a framework that synergizes structured exploration with off-policy RL optimization to create a cycle between metacognitive tool-use decisions and evolving capabilities. |
| Outcome: | The proposed framework improves over 11% on MATH500 and 9.4% on AIME without o1-like CoT. |
Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages (2023.emnlp-main)
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| Challenge: | Existing methods for zero-shot CoT are limited to a single language, making it difficult to generalize to other languages and hindering global development. |
| Approach: | They introduce cross-lingual prompting (CLP) to improve zero-shot CoT reasoning across languages. |
| Outcome: | The proposed method outperforms existing prompting methods on several benchmarks. |
Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs (2024.findings-acl)
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| Challenge: | Prior research on evaluating large language models focused on answer accuracy, neglecting the correctness of the generated CoT. |
| Approach: | They propose a discriminative and generative CoT evaluation paradigm to assess LLMs’ knowledge of reasoning and the accuracy of the generated CoT. |
| Outcome: | The proposed evaluation paradigm assesses LLMs’ knowledge of reasoning and the accuracy of the generated CoT. |
Unveiling Confirmation Bias in Chain-of-Thought Reasoning (2025.findings-acl)
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| Challenge: | Chain-of-thought (CoT) prompting has been widely adopted to enhance the reasoning capabilities of large language models (LLMs). |
| Approach: | They propose to examine how internal beliefs affect reasoning generation and reasoning-guided answer prediction in CoT by decomposing CoT into a two-stage process. |
| Outcome: | The proposed model beliefs affect reasoning generation and reasoning-guided answer prediction in CoT, and the results provide strong evidence of confirmation bias in LLMs. |
Crowd Comparative Reasoning: Unlocking Comprehensive Evaluations for LLM-as-a-Judge (2025.acl-long)
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Qiyuan Zhang, Yufei Wang, Yuxin Jiang, Liangyou Li, Chuhan Wu, Yasheng Wang, Xin Jiang, Lifeng Shang, Ruiming Tang, Fuyuan Lyu, Chen Ma
| Challenge: | Existing methods rely on majority voting or criteria expansion to capture detailed and detailed details, often leading to incomplete outcomes. |
| Approach: | They propose a method which introduces additional crowd responses to compare with the candidate responses, thereby exposing deeper and more comprehensive details within the candidate answers. |
| Outcome: | Experiments show that the proposed method improves evaluation reliability and achieves an average gain of 6.7% across five benchmarks. |
How Do Humans Write Code? Large Models Do It the Same Way Too (2024.emnlp-main)
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| Challenge: | Program-of-Thought (PoT) replaces natural language-based Chain-ofThough (CoT) but introduces more reasoning errors, such as incorrect formulas or flawed logic, compared to CoT. |
| Approach: | They propose a method that integrates CoT and Program-of-Thought to achieve more accurate reasoning and reinforcement learning. |
| Outcome: | The proposed method achieves an average improvement of 6.5% on the Llama-Base model and 4.3% on the Mistral-Bass model across 8 mathematical calculation datasets. |
CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning (2025.emnlp-main)
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| Challenge: | Existing methods to improve the reasoning performance of Large Language Models (LLMs) ignore annotated Chain-of-Thought (CoT) and incorporate unstable reasoning path sampling. |
| Approach: | They propose a Contrastive learning with annotated CoT-based Reinforced Fine-Tuning approach to enhance the reasoning performance of Large Language Models. |
| Outcome: | The proposed approach exploits annotated CoT and stabilizes the fine-tuning procedure by incorporating an additional unsupervised learning signal. |
RESPROMPT: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models (2024.naacl-long)
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Song Jiang, Zahra Shakeri, Aaron Chan, Maziar Sanjabi, Hamed Firooz, Yinglong Xia, Bugra Akyildiz, Yizhou Sun, Jinchao Li, Qifan Wang, Asli Celikyilmaz
| Challenge: | Chain-of-thought (CoT) has impressively unlocked the reasoning potential of large language models (LLMs), but it falls short when tackling problems that require multiple reasoning steps. |
| Approach: | They propose a new prompting strategy that advances multi-step reasoning in LLMs by integrating necessary connections into prompts. |
| Outcome: | The proposed strategy improves multi-step reasoning accuracy and improves reasoning accuracy across math, sequential, and commonsense domains. |
GoT-R1: Internalizing Graph-of-Thought via Structural Reinforcement for High-Density Reasoning (2026.findings-acl)
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| Challenge: | Chain-of-Thought reasoning suffers from an inherent mechanism flaw: linearity induces overthinking . emergence of Large Language Models (LLMs) has fundamentally redefined artificial intelligence . |
| Approach: | They propose a framework that replaces verbose linear trajectories with high-density reasoning graphs. |
| Outcome: | The proposed framework outperforms state-of-the-art models with reduced token overhead. |
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions (2023.acl-long)
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| Challenge: | Large language models generate natural language reasoning steps or Chains-of-Thoughts when prompted appropriately. |
| Approach: | They propose a new approach that interleaves retrieval with steps (sentences) in a CoT and uses retrieved results to improve CoT. |
| Outcome: | The proposed approach improves retrieval and downstream QA significantly on four datasets. |
Towards Better Chain-of-Thought: A Reflection on Effectiveness and Faithfulness (2025.findings-acl)
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| Challenge: | Chain-of-thought (CoT) prompting demonstrates varying performance under different reasoning tasks. |
| Approach: | They propose to recall extra information from the question to enhance CoT generation and evaluate CoTs based on their information gain. |
| Outcome: | The proposed method improves both the faithfulness and effectiveness of CoT and evaluates it based on their information gain. |
AS-ES Learning: Towards efficient CoT learning in small models (2024.findings-acl)
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| Challenge: | Existing methods to induce Chain-of-Thought (CoT) in LLMs are limited and do not consider the importance of efficiently utilizing existing CoT data. |
| Approach: | They propose a new training paradigm which exploits the inherent information in CoT for iterative generation. |
| Outcome: | The proposed training paradigm surpasses direct seq2seq training on CoT-extensive tasks without data augmentation or altering the model itself. |
Finite State Automata Inside Transformers with Chain-of-Thought: A Mechanistic Study on State Tracking (2025.acl-long)
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| Challenge: | Existing studies show that Chain-of-thought (CoT) can enhance the performance of large language models (LLMs) however, there is limited understanding of the algorithms that Transformer+CoT can learn. |
| Approach: | They propose two metrics to evaluate Transformer+CoT's state tracking capabilities and identify the circuit responsible for tracking the world state. |
| Outcome: | The proposed model achieves 100% accuracy for each state, highlighting an implicit finite state automaton (FSA) embedded within the model. |
Re-Reading Improves Reasoning in Large Language Models (2024.emnlp-main)
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| Challenge: | Unlike thought-eliciting prompting methods, RE2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process. |
| Approach: | They introduce a simple, yet general and effective prompting method, RE2, which rereads the question as input. |
| Outcome: | The proposed method demonstrates strong generality and compatibility with most thought-eliciting prompting methods, including CoT. |
Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models (2025.findings-acl)
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Yingqian Cui, Pengfei He, Jingying Zeng, Hui Liu, Xianfeng Tang, Zhenwei Dai, Yan Han, Chen Luo, Jing Huang, Zhen Li, Suhang Wang, Yue Xing, Jiliang Tang, Qi He
| Challenge: | Chain-of-Thought (CoT) reasoning has improved the performance of large language models (LLMs) however, the detailed reasoning process in CoT often incurs long generation times and high computational costs due to the inclusion of unnecessary steps. |
| Approach: | They propose a method to identify critical reasoning steps using perplexity as a measure of their importance. |
| Outcome: | The proposed method achieves a better balance between reasoning accuracy and efficiency of CoT. |
Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering (2024.lrec-main)
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| Challenge: | Large language models have demonstrated remarkable capabilities by leveraging chain-of-thought reasoning techniques to solve complex questions. |
| Approach: | They propose a method that assesses the entailment relationship between the question and the candidate reasoning chain and uses it to predict the answer. |
| Outcome: | The proposed approach improves the fine-tuned T5 baseline over the ScienceQA, ECQA, and LastLetter tasks. |
Probe Then Retrieve and Reason: Distilling Probing and Reasoning Capabilities into Smaller Language Models (2024.lrec-main)
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| Challenge: | Recent research efforts have focused on distilling Large Language Models into Small Language Model (SLMs) however, the results of CoT distillation are inadequate for knowledge-intensive reasoning tasks. |
| Approach: | They propose a retrieval-based framework which distills question probing and reasoning capabilities from Large Language Models into SLMs. |
| Outcome: | The proposed framework improves probing and reasoning capabilities of large language models in knowledge-intensive reasoning tasks. |
ARM2: Adaptive Reasoning Model with Vision Understanding and Executable Code (2026.findings-acl)
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| Challenge: | Large Reasoning Models suffer from the "over-thinking" problem, causing performance degradation. |
| Approach: | They propose a unified model that balances reasoning performance and efficiency across multiple formats through a reinforcement learning framework augmented with length-aware optimization. |
| Outcome: | The proposed model reduces token costs while preserving performance compared to traditional models. |
Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression (2026.acl-long)
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Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen
| Challenge: | Existing work on reducing CoT generation in reasoning impairs the necessary information for deriving the correct answer. |
| Approach: | They propose a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for Large Language Models (LLMs). |
| Outcome: | The proposed framework reduces the generation length of LLMs, but its effectiveness hinges on the efficiency and reliability of the contextual CoT generation. |
Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning (2026.acl-long)
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| Challenge: | Recent work on Chain-of-Thought prompting imposes substantial computational overhead . lack of supervision obscures the analyzability of the latent reasoning chain. |
| Approach: | They propose a framework to render latent reasoning chain into images, making latent rationale explicit and traceable. |
| Outcome: | The proposed framework achieves 3-4 token compression and substantial inference acceleration compared to explicit CoT prompting. |
Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint Verbalization (2026.acl-long)
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| Challenge: | Recent work labels a CoT as unfaithful if it omits a prompt-injected hint that affected the prediction. |
| Approach: | They propose to use the Biasing Features metric to label a CoT as unfaithful if it omits a prompt-injected hint that affected the prediction. |
| Outcome: | The proposed metric confuses unfaithfulness with incompleteness, the authors argue . larger inference-time budgets greatly increase hint verbalization, they show . |