MONTE CARLO TREE SEARCH

Learning to Bootstrap for Entity Set Expansion (D19-1)

Challenge:	Existing Process Reward Models (PRMs) are vulnerable to reward hacking and require expensive, large-scale annotation of reasoning steps.
Approach:	They propose a reward model approach which evaluates both individual and consecutive reasoning steps from fine-grained and coarse-grounded level.
Outcome:	Empirical results show that the proposed model performs better than existing PRMs and is more robust than existing models.

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Lingyong Yan, Xianpei Han, Le Sun, Ben He

Information Extraction & Text Mining

Challenge:	Existing bootstrapping methods for Entity Set Expansion suffer from two problems: 1) delayed feedback and sparse supervision.
Approach:	They propose a method that estimates delayed feedback and adaptively scores entities given sparse supervision signals.
Outcome:	The proposed method can estimate delayed feedback for pattern evaluation and adaptively score entities given sparse supervision signals.

Reason-Code: Reliable Code Generation via Test-Driven Monte Carlo Tree Search (2026.acl-industry)

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Zixu Li, Zhiqi Peng

SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement (2025.acl-short)

Challenge:	Large Language Models (LLMs) are widely used for code generation, but their performance degrades on complex tasks.
Approach:	They propose an inference-time framework that formulates code generation as a search process guided by execution feedback.
Outcome:	The proposed framework improves reliability without paying full cost of additional sampling under strict latency budgets.

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Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen

Challenge:	Using Large Language Models (LLMs)-based agents can enhance their understanding of environments and tasks.
Approach:	They propose a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search exploration to refine their action knowledge in the current environment.
Outcome:	The proposed framework synthesizes possible scenarios with multi-step action invocation within the action space and performs Monte Carlo Tree Search exploration to refine action knowledge in the current environment.

Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution (2026.acl-long)

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Beidan Liu, Zhengqiu Zhu, Chen Gao, Tianle Pu, Yong Zhao, Wei Qi, Quanjun Yin

IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery (2025.acl-demo)

Challenge:	Large Language Model (LLM)-based optimization has shown promise for autonomous problem solving, but most approaches cast LLMs as passive constraint checkers rather than proactive strategy designers.
Approach:	They propose an end-to-end Automated Constraint Optimization method that tightly couples operations-research principles of constraint relaxation with LLM reasoning.
Outcome:	Extensive experiments on three challenging COP benchmarks validate AutoCO’s consistent effectiveness and superior performance, especially in hard regimes where current methods degrade.

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Aniketh Garikaparthi, Manasi Patwardhan, Lovekesh Vig, Arman Cohan

Challenge:	Recent work on automated hypothesis generation focuses on multi-agent frameworks and extending test-time compute, but none incorporates human-in-the-loop (HITL) integration.
Approach:	They propose an open-source platform to enable researchers to leverage LLM-assisted scientific ideation.
Outcome:	The proposed system empowers researchers with greater control throughout ideation process.

SGA-MCTS: Decoupling Planning from Execution via Training-Free Atomic Experience Retrieval (2026.findings-acl)

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Xin Xie, Dongyun Xue, Wuguannan Yao, Mingxiao Feng, Wengang Zhou, Xiang Qi, Houqiang Li, Peng Zhang

SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation (2024.findings-emnlp)

Challenge:	a new framework casts LLM planning as non-parametric retrieval, but high latency of inference-time search and supervised fine-tuning are limitations.
Approach:	They propose a framework that casts LLM planning as non-parametric retrieval . they leverage Monte Carlo Tree Search to explore the solution space .
Outcome:	Empirical results show that SGA-MCTS can match the performance of SOTA systems without task-specific fine-tuning.

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Minda Hu, Licheng Zong, Hongru Wang, Jingyan Zhou, Jingjing Li, Yichen Gao, Kam-Fai Wong, Yu Li, Irwin King

Monte Carlo Tree Search Based Prompt Autogeneration for Jailbreak Attacks against LLMs (2025.coling-main)

Challenge:	Existing retrieval-augmented approaches to large language models face performance limitations due to the lack of publicly available training data.
Approach:	They propose a plug-and-play LLM-based retrieval method called Self-Rewarding Tree Search based on Monte Carlo Tree Search and a self-rewarding paradigm to address these limitations.
Outcome:	The proposed method improves the performance of the BM25 retriever and surpasses the baseline of self-reflection in both efficiency and scalability.

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Suhuang Wu, Huimin Wang, Yutian Zhao, Xian Wu, Yefeng Zheng, Wei Li, Hui Li, Rongrong Ji

Optimizing Instruction Synthesis: Effective Exploration of Evolutionary Space with Tree Search (2024.findings-emnlp)

Challenge:	Jailbreak attacks craft specific prompts or append adversarial suffixes to prompts, thereby inducing language models to generate harmful or unethical content and bypassing the model’s safety guardrails.
Approach:	They propose a Monte Carlo Tree Search (MCTS) based Prompt Auto-generation (MPA) method to generate adversarial suffixes for valid jailbreak attacks.
Outcome:	The proposed method outperforms existing methods on open-source and closed-source models and shows that it can generate harmful responses.

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Li Chenglin, Qianglong Chen, Zhi Li, FengTao FengTao, Yicheng Li, Hao Chen, Fei Yu, Yin Zhang

Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation (2024.findings-acl)

Challenge:	Extensive research has highlighted the quality of instruction data is essential for the success of this alignment.
Approach:	They propose a framework for iteratively improving existing instruction data by using Monte Carlo tree search to find suitable prompts that align the language model to effectively learn multiple skills.
Outcome:	The proposed framework improves the evaluation scores of seed instruction data, raising the average evaluation scores from 2.19 to 3.81.

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Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang

Challenge:	Recent advances in Large Language Models (LLMs) have greatly advanced problem solving in diverse domains such as mathematical reasoning and knowledge reasoning.
Approach:	They propose a thought prompting approach called 'Everything of Thoughts' it leverages pretrained reinforcement learning and Monte Carlo Tree Search to incorporate external domain knowledge and planning capability into thoughts.
Outcome:	The proposed approach outperforms existing approaches on game of 24, 8-Puzzle, and Pocket Cube.

Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding (2026.acl-long)

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Yuhang Zhou, Mingrui Zhang, Ke Li, Mingyi Wang, Qiao Liu, Qifei Wang, Jiayi Liu, Fei Liu, Serena Li, Weiwei LI, Mingze Gao, Abhishek Kumar, Xiangjun Fan, Zhuokai Zhao, Lizhu Zhang

SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search (2025.naacl-long)

Challenge:	Large language models (LLMs) have shown promise on understanding and reasoning over tables, but current approaches remain limited.
Approach:	They propose a multi-agent framework that decomposes table reasoning into three specialized roles: planning, coding, and answering.
Outcome:	The proposed framework decomposes table reasoning into three specialized roles: planning, coding, and answering.

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Hanwen Du, Bo Peng, Xia Ning

Dialogue Systems & Conversational Agents

Challenge:	Existing methods train RL-based agents with greedy action selection or sampling strategy and suffer from suboptimal conversational planning.
Approach:	They propose a Monte Carlo Tree Search-based CRS framework called SAPIENT . it consists of a conversational agent and a communication planner .
Outcome:	The proposed framework outperforms the state-of-the-art methods on four benchmark datasets.

Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models (2026.acl-long)

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Jinyang Wu, Mingkuan Feng, Shuai Zhang, Feihu Che, Zhengqi Wen, Chonghua Liao, Ling Yang, Haoran Luo, Zheng Lian, Jianhua Tao

Diffusion Language Model Inference with Monte Carlo Tree Search (2026.findings-eacl)

Challenge:	In-context learning (ICL) struggles with complex reasoning due to superficial, example-level implicit imitation.
Approach:	They propose an automated method that shifts from surface-level examples to more guidance-oriented thought patterns.
Outcome:	The proposed method achieves 80.6% accuracy on MATH and 62.5% on AMC, surpassing GPT-4o’s 77.2% and 57.5% accuracy.

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Zheng Huang, Kiran Ramnath, Yueyan Chen, Aosong Feng, Sangmin Woo, Balasubramaniam Srinivasan, Zhichao Xu, Kang Zhou, Shuai Wang, Haibo Ding, Lin Lee Cheong

Progressive Multimodal Reasoning via Active Retrieval (2025.acl-long)

Challenge:	Existing methods for inference use heuristics to determine which positions to unmask and which tokens to commit . MEDAL is an inference-time scaling framework that integrates Monte Carlo Tree SEarch initialization for Diffusion Language Model inference.
Approach:	They propose a framework that integrates Monte Carlo Tree SEarch initialization for Diffusion Language Model inference.
Outcome:	The proposed framework achieves 22.0% improvement over existing inference strategies across multiple benchmarks.

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Guanting Dong, Chenghao Zhang, Mengjie Deng, Yutao Zhu, Zhicheng Dou, Ji-Rong Wen

Momoka-RAG: MCTS-Organized Mapping of Knowledge Associations for Long-Document Retrieval Augmented Generation (2026.findings-acl)

Challenge:	Existing approaches to improve multimodal large language models' reasoning performance are limited.
Approach:	They propose a framework to progressively improve multimodal reasoning capabilities . they propose active retrieval and Monte Carlo tree search to improve MLLMs' reasoning .
Outcome:	The proposed framework improves multimodal reasoning capabilities in multimodal large language models.

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Wenyu Tao, Xiaofen Xing, Zeliang Li, Xiangmin Xu

VReST: Enhancing Reasoning in Large Vision-Language Models through Tree Search and Self-Reward Mechanism (2025.acl-long)

Challenge:	Existing frameworks that rely on fixed-length chunking are unsuitable for long-document tasks due to their passive and mechanical approach to knowledge structure.
Approach:	They propose a framework that utilizes Monte Carlo Tree Search to proactively uncover connections among chunks and construct optimal semantic information paths with the objective of completing semantic relationships.
Outcome:	The proposed framework achieves higher precision while maintaining competitive recall compared to other RAG frameworks.

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Congzhi Zhang, Jiawei Peng, Zhenglin Wang, Yilong Lai, Haowen Sun, Heng Chang, Fei Ma, Weijiang Yu

Reasoning with Trees: Faithful Question Answering over Knowledge Graph (2025.coling-main)

Challenge:	Large Vision-Language Models (LVLMs) have shown exceptional performance in multimodal tasks, but their effectiveness in complex visual reasoning is constrained.
Approach:	They propose a training-free approach that enhances Reasoning in Large Vision-Language Models . they propose integrating Monte Carlo Tree Search and Self-Reward mechanisms into the reasoning tree .
Outcome:	The proposed approach surpasses current prompting methods and secures state-of-the-art performance across three multimodal reasoning benchmarks.

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Tiesunlong Shen, Jin Wang, Xuejie Zhang, Erik Cambria

Knowledge Representation

Challenge:	Recent advances in large language models (LLMs) have shown remarkable progress in reasoning capabilities, yet they still face challenges in complex, multi-step reasoning tasks.
Approach:	They propose a framework that synergistically integrates LLMs with knowledge graphs (KGs) to enhance reasoning performance and interpretability.
Outcome:	The proposed framework outperforms existing state-of-the-art methods on two benchmark KGQA datasets and improves on the MCTS process.

PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding (2022.naacl-main)

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Antoine Chaffin, Vincent Claveau, Ewa Kijak

DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling (2025.emnlp-main)

Challenge:	Large language models (LM) based on transformers generate plausible long texts . a discriminator-guided approach allows to apply constraints more finely and dynamically.
Approach:	They propose to use a discriminator-guided approach to generate constrained texts without fine-tuning the LM.
Outcome:	The proposed method is easier and cheaper to train than fine-tuning the LM.

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Hao Sun, Zile Qiao, Bo Wang, Guoxin Chen, Yingyan Hou, Yong Jiang, Pengjun Xie, Fei Huang, Yan Zhang

Better Red Teaming via Searching with Large Language Model (2025.findings-acl)

Challenge:	Retrieval-Augmented Generation (RAG) systems have emerged as a pivotal methodology for enhancing Large Language Models (LLMs).
Approach:	They propose a framework that decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding.
Outcome:	The proposed framework decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding.

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Yongkang Chen, Chongyang Zhao, Jianwentian Jianwentian, Guiling Cao, Hu Li, Xiaohui Kuang

Assessing Robustness of Text Classification through Maximal Safe Radius Computation (2020.findings-emnlp)

Challenge:	Existing methods for evaluating large language models face challenges in managing semantic intricacies and optimizing the efficiency of the search process.
Approach:	They propose a framework that reconceptualizes test case generation as a strategic planning problem, leveraging Monte Carlo Tree Search.
Outcome:	Experiments on a range of LLM architectures show that the proposed framework achieves state-of-the-art attack success rates without sacrificing computational efficiency.

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Emanuele La Malfa, Min Wu, Luca Laurenti, Benjie Wang, Anthony Hartshorn, Marta Kwiatkowska

Robustness in NLP

Challenge:	Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction.
Approach:	They propose to provide a measure of robustness against word substitutions by computing a safe radius for a given input text.
Outcome:	The proposed methods are compared with LIME and CNN-Cert and show that they perform well on sentiment analysis and news classification models.

Task-Completion Dialogue Policy Learning via Monte Carlo Tree Search with Dueling Network (2020.emnlp-main)

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Sihan Wang, Kaijie Zhou, Kunfeng Lai, Jianping Shen

Natural Language Interfaces

Challenge:	Existing models of reinforcement learning use background planning and may suffer from low-quality simulated experiences.
Approach:	They propose a Monte Carlo Tree Search with Double-q Dueling network framework for task-completion dialogue policy learning.
Outcome:	The proposed method outperforms the previous model-based reinforcement learning methods and is robust to simulation errors.

Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search (2026.acl-long)

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Wentao Shi, Zichun Yu, Fuli Feng, Xiangnan He, Chenyan Xiong

Evaluating Multimodal Large Language Models on Video Captioning via Monte Carlo Tree Search (2025.acl-long)

Challenge:	Large Language Model (LLM) based multi-agent systems (MAS) have high potential for tackling complex tasks through collaborative intelligence.
Approach:	They propose a framework that incorporates influence scores to guide tree search and data selection in data synthesis.
Outcome:	The proposed framework incorporates influence scores to guide tree search and data selection in data synthesis.

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Linhao Yu, Xingguang Ji, Yahui Liu, Fanheng Kong, Chenxi Sun, Jingyuan Zhang, Hongzhi Zhang, V. W., Fuzheng Zhang, Deyi Xiong

RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement (2025.naacl-long)

Challenge:	Existing benchmarks and evaluation protocols suffer from inadequate or homogeneous creation of key points, exorbitant cost of data creation, and limited evaluation scopes.
Approach:	They propose an automatic framework which leverages Monte Carlo Tree Search to construct numerous and diverse descriptive sentences that thoroughly represent video content in an iterative way.
Outcome:	The proposed framework improves MCTS-VCB and DREAM-1K on video captioning tasks by 25.0% and 16.3% respectively.

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Jinhao Jiang, Jiayi Chen, Junyi Li, Ruiyang Ren, Shijie Wang, Xin Zhao, Yang Song, Tao Zhang

LLaMA-Berry: Pairwise Optimization for Olympiad-level Mathematical Reasoning via O1-like Monte Carlo Tree Search (2025.naacl-long)

Challenge:	Existing large language models (LLMs) show exceptional problem-solving capabilities but struggle with complex reasoning tasks.
Approach:	They propose a novel RAG approach that integrates retrieved information to guide tree-based reasoning process based on LLMs.
Outcome:	The proposed approach outperforms existing methods in large language models . iteratively plans intermediate sub-queries and answers based on the LLM itself .

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Di Zhang, Jianbo Wu, Jingdi Lei, Tong Che, Jiatong Li, Tong Xie, Xiaoshui Huang, Shufei Zhang, Marco Pavone, Yuqiang Li, Wanli Ouyang, Dongzhan Zhou

Boosting Policy and Process Reward Models with Monte Carlo Tree Search in Open-Domain QA (2025.findings-acl)

Challenge:	LLaMA-Berry is an advanced mathematical reasoning framework to enhance the problem-solving ability of large language models (LLMs).
Approach:	They propose a Monte Carlo Tree Search and Self-Refine framework to optimize reasoning paths and a pairwise reward model to evaluate different paths globally.
Outcome:	The proposed framework overcomes inefficiencies and limitations of step-wise and greedy search algorithms, enabling more efficient exploration of solution spaces.

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Chi-Min Chan, Chunpu Xu, Junqi Zhu, Jiaming Ji, Donghai Hong, Pengcheng Wen, Chunyang Jiang, Zhen Ye, Yaodong Yang, Wei Xue, Sirui Han, Yike Guo

Question Answering

Challenge:	Experimental results show that our approach can effectively improve the performance of both the policy model and the reward model.
Approach:	They propose to use Monte Carlo Tree Search for both policy model improvement and reward model improvement to bridge it to more subtle open-domain question answering.
Outcome:	The proposed approach surpasses existing methods for annotation and training data with fewer data points and achieves better performance in test-time scaling strategies.

LongDPO: Unlock Better Long-form Generation Abilities for LLMs via Critique-augmented Stepwise Information (2025.findings-acl)

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Bowen Ping, Jiali Zeng, Fandong Meng, Shuo Wang, Jie Zhou, Shanghang Zhang

Challenge:	Recent advances in large language models have improved their capacity to handle long text inputs, but current models still exhibit unsatisfactory performance in long-form generation.
Approach:	They propose a method to enhance long-form text generation through step-level supervision by leveraging Monte Carlo Tree Search to collect stepwise preference pairs and employ a global memory pool to maintain factual accuracy.
Outcome:	The proposed method improves performance on long-form generation benchmarks while maintaining lossless performance on several general benchmarks.

ThoughtSculpt: Reasoning with Intermediate Revision and Search (2025.findings-naacl)

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Yizhou Chi, Kevin Yang, Dan Klein

SuperWriter: Reflection-Driven Long-Form Generation with Large Language Models (2026.findings-acl)

Challenge:	THOUGHTSCULPT is a general reasoning and search method for tasks with outputs that can be decomposed into components.
Approach:	They propose a general reasoning and search method for tasks with outputs that can be decomposed into components.
Outcome:	THOUGHTSCULPT outperforms state-of-the-art reasoning methods on three tasks . authors show that distinct prompting strategies can influence the performance of LLMs .

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Yuhao Wu, Yushi Bai, Zhiqiang Hu, Juanzi Li, Roy Ka-Wei Lee

Prompt-Based Monte-Carlo Tree Search for Goal-oriented Dialogue Policy Planning (2023.emnlp-main)

Challenge:	Long-form text generation remains a challenge for large language models . generating extended sequences often leads to degraded coherence and logical consistency .
Approach:	They propose a framework that integrates explicit structured thinking into long-form text generation.
Outcome:	The proposed framework surpasses even larger-scale models in evaluation and human evaluation.

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Xiao Yu, Maximillian Chen, Zhou Yu

Joint Enhancement of Relational Reasoning for Long-Context LLMs (2025.findings-emnlp)

Challenge:	Optimal policy planning is a difficult task, authors say . many goal-oriented conversations require subjective strategies, they say - a problem in goal-orientated settings .
Approach:	They propose an approach to perform goal-oriented dialogue policy planning without model training.
Outcome:	The proposed approach performs goal-oriented dialogue policy planning without model training.

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Zhirui Chen, Wei Shen, Jiashui Huang, Ling Shao

Step-level Value Preference Optimization for Mathematical Reasoning (2024.findings-emnlp)

Challenge:	JERR is a graph-based reasoning framework for large language models . it enables LLMs to handle extended contexts with improved reliability and transparency .
Approach:	They propose a graph-based reasoning framework that integrates synopsis extraction, graph construction, and relational reasoning.
Outcome:	The proposed framework outperforms baselines on ROUGE and F1 metrics and achieves the highest scores on the LLM-Rater evaluation.

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Guoxin Chen, Minpeng Liao, Chengxi Li, Kai Fan

MASTER: A Multi-Agent System with LLM Specialized MCTS (2025.naacl-long)

Challenge:	Existing methods for generating preference-level annotations do not capture the fine-grained quality of model outputs in multi-step reasoning tasks.
Approach:	They propose an algorithm to automatically annotate step-level preferences for multi-step reasoning using Monte Carlo Tree Search.
Outcome:	The proposed algorithm achieves state-of-the-art performance on in-domain and out-of domain mathematical reasoning benchmarks.

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Bingzheng Gan, Yufan Zhao, Tianyi Zhang, Jing Huang, Li Yusu, Shu Xian Teo, Changwang Zhang, Wei Shi

Challenge:	Large Language Models (LLMs) are increasingly being explored for problem-solving tasks . their strategic planning capability is often viewed with skepticism due to their limited planning capabilities.
Approach:	They propose a framework that coordinates agent recruitment and communication through LLM specialized MCTS.
Outcome:	The proposed framework achieves 76% accuracy on HotpotQA and 80% on WebShop . it relies on extensive sampling simulations to approximate the true reward distribution .

ReKG-MCTS: Reinforcing LLM Reasoning on Knowledge Graphs via Training-Free Monte Carlo Tree Search (2025.findings-acl)

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Xiaozhuang Song, Shufei Zhang, Tianshu Yu

Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories (2026.acl-long)

Challenge:	Existing approaches to combining knowledge graphs with large language models face limitations in path exploration strategies or excessive computational overhead.
Approach:	They propose a training-free framework that synergizes Monte Carlo Tree Search with LLM capabilities to enable dynamic reasoning over KGs.
Outcome:	The proposed framework outperforms existing training-free methods and achieves competitive performance compared to fine-tuned baselines.

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Peiyang Liu, Zhirui Chen, Xi Wang, Di Liang, Youru Li, Zhi Cai, Wei Ye

AgentPro: Enhancing LLM Agents with Automated Process Supervision (2025.emnlp-main)

Challenge:	MCTS methods retain only the single highest-reward trajectory, discarding comparative signals present in the many explored paths.
Approach:	They propose a framework that transforms supervision extraction into a synthesis procedure.
Outcome:	The proposed framework matches or exceeds baselines on 60K CRPS-synthesized examples on out-of-domain benchmarks.

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Yuchen Deng, Shichen Fan, Naibo Wang, Xinkui Zhao, See-Kiong Ng

Reasoning with Language Model is Planning with World Model (2023.emnlp-main)

Challenge:	Existing frameworks lack explicit supervision during the reasoning process, which may lead to error propagation across reasoning chains.
Approach:	They propose a framework which automates process supervision for large language model agents by automatically generating step-level annotations and developing a process reward model based on these annotations.
Outcome:	The proposed framework outperforms existing agent-based methods on four datasets and achieves a 6.32% increase in accuracy.

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Shibo Hao, Yi Gu, Haodi Ma, Joshua Hong, Zhen Wang, Daisy Wang, Zhiting Hu

Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning (2025.naacl-long)

Challenge:	Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts.
Approach:	They propose a framework that repurposes the LLM as both a world model and a reasoning agent and incorporates a principled planning algorithm (based on Monte Carlo Tree Search)
Outcome:	The proposed framework repurposes the LLM as both a world model and a reasoning agent and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) it achieves optimum balance between exploration and exploitation, while achieving high-reward reasoning paths efficiently.

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Sungjin Park, Xiao Liu, Yeyun Gong, Edward Choi

Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation (2022.coling-1)

Challenge:	Existing methods for ensembling language models fail to address complex reasoning tasks.
Approach:	They propose a framework for process-level ensembling of large language models using Monte Carlo tree search.
Outcome:	The proposed framework outperforms both language model decoding and language model ensemble methods on five reasoning benchmarks.

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Husam Quteineh, Spyridon Samothrakis, Richard Sutcliffe

DSG-MCTS: A Dynamic Strategy-Guided Monte Carlo Tree Search for Diversified Reasoning in Large Language Models (2025.emnlp-main)

Challenge:	Large-scale pretrained language models have led to significant improvements in Natural Language Processing, but they come at the cost of high computational and storage requirements.
Approach:	They propose to distill knowledge from larger models to smaller ones through pseudo-labels on task-specific datasets.
Outcome:	The proposed approach improves on the SST-2, MRPC, YELP-2, and TREC-6 datasets.

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Rui Ha, Chaozhuo Li, Rui Pu, Litian Zhang, Xi Zhang, Sen Su

A Systematic Examination of Preference Learning through the Lens of Instruction-Following (2025.naacl-long)

Challenge:	Large language models (LLMs) have shown strong potential in complex reasoning tasks, but their performance often degrades, resulting in hallucinations, errors, and logical inconsistencies.
Approach:	They propose a framework that integrates multiple reasoning strategies to expand the reasoning space and a dynamic strategy selection mechanism that adapts to the task context.
Outcome:	The proposed framework outperforms existing state-of-the-art methods on a set of reasoning benchmarks.

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Joongwon Kim, Anirudh Goyal, Aston Zhang, Bo Xiong, Rui Hou, Melanie Kambadur, Dhruv Mahajan, Hannaneh Hajishirzi, Liang Tan

RASPberry: Retrieval-Augmented Monte Carlo Tree Self-Play with Reasoning Consistency for Multi-Hop Question Answering (2025.findings-acl)

Challenge:	a recent study has found that preference learning is a key tool for enhancing LLM training and alignment.
Approach:	They use a synthetic data generation pipeline to generate 48,000 unique instruction-following prompts with 23 verifiable constraints to obtain preference pairs.
Outcome:	The proposed pipeline generates 48,000 unique instruction-following prompts with 23 verifiable constraints that enable fine-grained and automated quality assessments of model responses.

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Baixuan Li, Yunlong Fan, Tianyi Ma, Miao Gao, Chuanqi Shi, Zhiqiang Gao

Textual Data Augmentation for Efficient Active Learning on Tiny Datasets (2020.emnlp-main)

Challenge:	Existing methods for generating and analyzing multiple document knowledge are not effective for multi-hop question answering.
Approach:	They propose a Monte Carlo tree-based approach to inference-time scaling using RASPberry.
Outcome:	Experimental results show that the proposed method achieves better inference-time scaling on smaller LLMs.

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Husam Quteineh, Spyridon Samothrakis, Richard Sutcliffe

LEMMA: Learning from Errors for MatheMatical Advancement in LLMs (2025.findings-acl)

Challenge:	Existing active learning approaches for textual data are limited due to the complexity of language.
Approach:	They propose an approach where guided outputs of a language generation model can be enhanced through an active learning process.
Outcome:	The proposed approach achieves performance increases of 3% and 5% on TREC-6 and SST-2 datasets compared with NGDG, which does not optimize for a reward function.

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Zhuoshi Pan, Yu Li, Honglin Lin, Qizhi Pei, Zinan Tang, Wei Wu, Chenlin Ming, H. Vicky Zhao, Conghui He, Lijun Wu

From Objectives to Questions: A Planning-based Framework for Educational Mathematical Question Generation (2025.acl-long)

Challenge:	Existing approaches focus on improving the quality of correct training data, neglecting the value contained in error data, thereby hindering the model’s reflective ability.
Approach:	They propose to improve LLM's reasoning ability by learning from error data and a grounded mistake augmentation method to collect representative errors.
Outcome:	The proposed model achieves significant performance improvements over other strong models with less than 90k data.

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Cheng Cheng, Zhenya Huang, GuanHao Zhao, Yuxiang Guo, Xin Lin, Jinze Wu, Xin Li, Shijin Wang

Text Generation

Challenge:	Traditional generation methods focus primarily on textual quality, but they fail to meet complex, multifaceted educational requirements.
Approach:	They propose a method for automatic generating high-quality mathematical problems that align with educational objectives using a dataset of 16k mathematical questions with multi-dimensional educational objectives.
Outcome:	The proposed method improves generating high-quality mathematical questions that meet multi-dimensional educational objectives.

PCQPR: Proactive Conversational Question Planning with Reflection (2024.emnlp-main)

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Shasha Guo, Lizi Liao, Jing Zhang, Cuiping Li, Hong Chen

Natural Language Interfaces

Challenge:	Current CQG methods focus on immediate context without strategic consideration of the specified conversational outcome.
Approach:	They propose a method that uses a planning algorithm inspired by Monte Carlo Tree Search to generate contextually relevant questions.
Outcome:	The proposed approach surpasses existing methods in e-learning and customer service fields . it generates contextually appropriate questions strategically devised to reach a specified outcome .

Direct Value Optimization: Improving Chain-of-Thought Reasoning in LLMs with Refined Values (2025.emnlp-main)

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Hongbo Zhang, Han Cui, Guangsheng Bao, Linyi Yang, Jun Wang, Yue Zhang

MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search (2025.findings-emnlp)

Challenge:	Existing offline preference optimization methods rely on preference labels to optimize large language models.
Approach:	They propose an offline method for enhancing large language models in reasoning tasks that utilizes value signals at individual reasoning steps.
Outcome:	The proposed framework outperforms offline preference optimization techniques by 4% to 6% on math reasoning, commonsense reasoning, and coding tasks.

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Yunhai Hu, Yilun Zhao, Chen Zhao, Arman Cohan

CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning (2025.findings-emnlp)

Challenge:	MCTS-RAG combines structured reasoning with adaptive retrieval . compared to conventional MCTLs, MCTR-RAg relies on internal model knowledge without external facts .
Approach:	a new approach integrates retrieval-augmented generation and Monte Carlo Tree Search to enhance reasoning capabilities of small language models.
Outcome:	MCTS-RAG integrates retrieval-augmented generation and Monte Carlo Tree Search to improve reasoning paths.

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Jianfeng Pan, Senyou Deng, Shaomang Huang

MMA: Cross-Domain Knowledge Integration via Mixture of Multi-Domain Agents (2025.findings-emnlp)

Challenge:	OpenAI-o1 enables ‘slow thinking’ because it is closer to the human thought process .
Approach:	They propose a new framework that integrates the Monte Carlo Tree Search algorithm and a dynamic mechanism for integrating new key information, termed ‘associative memory’.
Outcome:	The proposed framework improves performance on open-source multi-hop reasoning datasets and more than 15% gain on proprietary CRB dataset.

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Kehang Jia, Juntao Li, Xiaobo Liang, Yisheng Xiao, Yixuan Yang, Min Zhang

Challenge:	achieving synergistic improvements between generalization and domain specialization remains a challenge in pre-training and post-training.
Approach:	They propose a test-time cross-domain knowledge integration method that integrates general-purpose and domain-specific models to enhance their performance on complex, domainspecific tasks.
Outcome:	The proposed method combines the outputs of general-purpose and domain-specific models to improve their performance on complex, domainspecific tasks.

QDTSynth: Quality-Driven Formal Theorem Synthesis for Enhancing Proving Performance of LLMs (2025.acl-long)

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Lei Wang, Ruobing Zuo, Gaolei He, Jianlin Wang, Zhengfeng Yang

PRISM-MCTS: Learning from Reasoning Trajectories with Metacognitive Reflection (2026.findings-acl)

Challenge:	Existing formal languages such as Lean, Coq and Metamath are proving to be useful in formal theorem proving . however, there is a scarcity of high-quality supervised fine-tuning data for formal proofs .
Approach:	They propose a Quality-Driven Theorem S**ynthesis method in Lean4 . they propose diversity screening and the self-assessment method to select theoremas that exhibit diversity and high quality from the initial synthetic statements.
Outcome:	The proposed method significantly improves performance of open-source LLMs in theorem proving tasks.

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Siyuan Cheng, Bozhong Tian, Yanchao Hao, Zheng Wei

Q-PRM: Adaptive Query Rewriting for Retrieval-Augmented Generation via Step-level Process Supervision (2025.findings-emnlp)

Challenge:	Existing reasoning models are limited by inefficiency and computational redundancy . PRISM-MCTS integrates a process reward model with a dynamic shared memory .
Approach:	They propose a reasoning framework that integrates a process reward model with a dynamic shared memory.
Outcome:	PRISM-MCTS integrates a process reward model with a dynamic shared memory . it halves trajectory requirements on GPQA while surpassing MCTS-RAG and Search-o1 .

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Xiaopeng Ye, Chen Xu, Chaoliang Zhang, Zhaocheng Du, Jun Xu, Gang Wang, Zhenhua Dong

Paraphrasing

Challenge:	Existing approaches to rewriting queries often lack supervision signals for intermediate steps . existing approaches rely on outcome-supervised training or heuristic rules to guide the rewrite process .
Approach:	They propose a query rewriting framework that generates process-level supervision signals for intermediate steps.
Outcome:	a new query rewriting framework outperforms existing approaches on open-domain QA benchmarks.

Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter (2025.findings-emnlp)

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Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu

RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models (2025.acl-long)

Challenge:	Existing supervised fine-tuning (SFT) fails to address these issues, as it trains models on single gold-standard responses without modeling nuanced strategy trade-offs.
Approach:	They propose a two-stage framework that optimizes strategy selection preferences at each dialogue turn.
Outcome:	The proposed framework improves strategy selection preferences at each dialogue turn.

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Hieu Tran, Zonghai Yao, Zhichao Yang, Junda Wang, Yifan Zhang, Shuo Han, null Feiyun Ouyang, Hong Yu

Med-VRAgent: A Framework for Medical Visual Reasoning-Enhanced Agents (2025.emnlp-main)

Challenge:	Existing work aims to improve reasoning accuracy and factual integrity across large language models for knowledge-intensive tasks such as medical and commonsense reasoning.
Approach:	They propose a versatile extension to the mutual reasoning framework (rStar) that enhances reasoning accuracy and factual integrity across large language models.
Outcome:	The proposed extension to the mutual reasoning framework improves reasoning accuracy and factual integrity across large language models for complex, knowledge-intensive tasks.

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Guangfu Guo, Xiaoqian Lu, Yue Feng

Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction (2026.acl-long)

Challenge:	Visual Language Models (VLMs) have shown strong performance in tasks like radiology report generation but struggle with hallucinations, vague descriptions, Inconsistent logic and poor localization.
Approach:	They propose a framework for medical visual reasoning based on Visual Guidance and Self-Reward paradigms and Monte Carlo Tree Search to improve the model's visual reasoning capabilities.
Outcome:	The proposed framework outperforms existing models on multiple medical VQA benchmarks.

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Tao Wu, Jingyuan Chen, Wang Lin, Jian Zhan, Mengze Li, Fangzhou Jin, Min Zhang, Kun Kuang, Fei Wu

Natural Language Interfaces

Challenge:	Current distractor generation methods produce shared distractors for all students, ignoring individual variations in reasoning, which limits their diagnostic effectiveness.
Approach:	They propose a method which tailors distractors to each student’s specific cognitive flaws, inferred from their past question-answering (QA) history.
Outcome:	The proposed framework outperforms existing methods in generating plausible distractors and adapts to group-level settings.

AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation (2025.findings-emnlp)

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Wenfeng Feng, Chuzhan Hao, Yuewei Zhang, Guochao Jiang, Jingyi Song

Boosting LLM’s Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning (2025.acl-long)

Challenge:	Experimental results show the effectiveness of AirRAG on complex question-answering datasets.
Approach:	They propose a new thinking pattern that integrates autonomous strategic planning with efficient reasoning actions.
Outcome:	The proposed approach significantly activates intrinsic reasoning capabilities and expands the solution space of specific tasks via Monte Carlo Tree Search.

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Xiang Zhuang, Bin Wu, Jiyu Cui, Kehua Feng, Xiaotong Li, Huabin Xing, Keyan Ding, Qiang Zhang, Huajun Chen

Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models (2025.acl-long)

Challenge:	Molecular structure elucidation involves deducing a molecule’s structure from various types of spectral data, which is crucial in chemical experimental analysis.
Approach:	They propose a Knowledge-enhanced reasoning framework for Molecular Structure Elucidation that leverages Monte Carlo Tree Search for test-time scaling as a plugin to extend the LLMs’ coverage of the chemical structure space.
Outcome:	The proposed framework significantly improves on both GPT-4o-mini and GPT4o, and a specialized molecule-spectrum scorer improves performance.

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Huifeng Yin, Yu Zhao, Minghao Wu, Xuanfan Ni, Bo Zeng, Huaiyu.wh Huaiyu.wh, Tianqi Shi, Liangying Shao, Chenyang Lyu, Longyue Wang, Weihua Luo, Kaifu Zhang

WebSynthesis: World Model-Guided Monte Carlo Tree Search for Efficient WebAgent Trajectory Synthesis (2026.acl-long)

Challenge:	Recent efforts to distill large reasoning models into smaller lightweight models have shown competitive performances.
Approach:	They propose to distill long Chain-of-Thought data to improve SFT and RL methods by constructing data from scratch using Monte Carlo Tree Search.
Outcome:	The proposed method significantly improves reasoning performance on various benchmarks such as math (GSM8K, MATH, AIME).

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Yifei Gao, Junhong Ye, Yifan Yang, Jiaqi Wang, Yi Zhang, Zhang Ruichen, Jitao Sang

Green & Sustainable NLP

Challenge:	Recent advances in large language models have enabled increasingly capable web agents . however, training such agents at scale still relies on high-quality interaction trajectories that are difficult to obtain at scale.
Approach:	They propose a framework for scalable trajectory synthesis that simulates state transitions without network dependencies and integrates Monte Carlo Tree Search to enable reversible exploration over the simulated state space.
Outcome:	Experiments on WebArena, WebVoyager, and Mind2Web-Online show that agents trained exclusively on synthesized trajectories outperform those trained on real-world data.

A Dual-Mind Framework for Strategic and Expressive Negotiation Agent (2025.acl-long)

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Yutong Liu, Lida Shi, Rui Song, Hao Xu

Challenge:	Existing approaches to negotiation dialogue focus on only one aspect, ignoring the synergistic effect of their combined synergies.
Approach:	They propose a dual-mind negotiation agent framework that integrates an intuitive and a deliberative module for slow, expression optimization.
Outcome:	The proposed framework achieves state-of-the-art on negotiation datasets showing that it improves negotiation ability.

Enhancing Retrieval-Augmented Generation via Evidence Tree Search (2025.acl-long)

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Hao Sun, Hengyi Cai, Yuchen Li, Xuanbo Fan, Xiaochi Wei, Shuaiqiang Wang, Yan Zhang, Dawei Yin

LLM Inductive Reasoning Through Multi-Agent Enhanced Monte Carlo Tree Search (2026.findings-acl)

Challenge:	Evidence retrieval is used to enhance Large Language Models (LLMs) but in real-world applications, it often returns lengthy documents with redundant or irrelevant content, confusing downstream readers.
Approach:	They propose a framework that reformulates evidence retrieval as a dynamic tree expansion process.
Outcome:	The proposed framework outperforms existing methods on five datasets.

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Xiang Li, Yucheng Zhou, Xiangzhi Wei, Zesheng Shi, Haiyuan Wan, Gong Yifan, Fangming Liu, Jing Li

FastMCTS: A Simple Sampling Strategy for Data Synthesis (2025.acl-long)

Challenge:	Existing methods for enhancing inductive reasoning of large language models often lack explicit optimization guidance and effective error correction.
Approach:	They propose a plug-and-play test-time framework that integrates multi-agent coordination with Monte Carlo Tree Search to improve inductive reasoning.
Outcome:	The proposed framework outperforms existing methods on four benchmarks and shows consistent improvements on QWQ-32B and Deepseek-V3 .

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Peiji Li, Kai Lv, Yunfan Shao, Yichuan Ma, Linyang Li, Xiaoqing Zheng, Xipeng Qiu, Qipeng Guo

Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience (2025.findings-acl)

Challenge:	Existing methods for generating multi-step reasoning data rely on rejection sampling, which generates trajectories independently and suffers from inefficiency and imbalanced sampling across problems of varying difficulty levels.
Approach:	They propose a data synthesis strategy inspired by Monte Carlo Tree Search . it offers step-level evaluation signals and promotes balanced sampling .
Outcome:	Experiments show that FastMCTS generates 30% more correct reasoning paths than rejection sampling.

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Jiawei Gu, Ziting Xian, Yuanzhen Xie, Ye Liu, Enjie Liu, Ruichao Zhong, Mochi Gao, Yunzhi Tan, Bo Hu, Zang Li

Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions (2025.emnlp-main)

Challenge:	Large language models struggle to infer implicit relationships embedded in tabular formats . authors introduce a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning (ICL).
Approach:	They propose a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning to simulate human-like knowledge transfer.
Outcome:	Experiments on Text-to-SQL and TableQA show CoRE significantly improves performance . it achieves gains of 3.44% and 4.24%, with up to 17.2% on challenging tasks .

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David Acuna, Ximing Lu, Jaehun Jung, Hyunwoo Kim, Amlan Kar, Sanja Fidler, Yejin Choi

Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks (2025.acl-long)

Challenge:	Recent research in vision-language models has centered around the possibility of equipping them with implicit long-form chain-of-thought reasoning via distillation and reinforcement learning.
Approach:	They propose a Monte Carlo Tree Search-inspired algorithm that injects subquestion–subanswer pairs into the model’s output stream to elicit hidden knowledge and induce long reasoning traces.
Outcome:	The proposed method yields a 2% improvement on MMMU-PRO, including a significant 9% gain in Liberal Arts.

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Xingxuan Li, Weiwen Xu, Ruochen Zhao, Fangkai Jiao, Shafiq Joty, Lidong Bing

AGENTVIGIL: Automatic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents (2025.findings-emnlp)

Challenge:	Existing approaches to problem-solving for large language models fail to provide accurate reasoning and factual accuracy.
Approach:	They propose a framework that leverages fine-tuned critic models to guide reasoning and retrieval processes.
Outcome:	The proposed framework outperforms baselines on domain-knowledge-intensive tasks . it can be used to iterate retrieval and reasoning, and improve retrieval relevance .

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Zhun Wang, Vincent Siu, Zhe Ye, Tianneng Shi, Yuzhou Nie, Xuandong Zhao, Chenguang Wang, Wenbo Guo, Dawn Song

GRAT: Guiding Retrieval-Augmented Reasoning through Process Rewards Tree Search (2025.acl-long)

Challenge:	AGENTVIGIL is a black-box optimization framework to exploit indirect prompt injection vulnerabilities . indirect prompts compromise the core of LLM agents by manipulating contextual information rather than direct user prompts.
Approach:	They propose a black-box optimization framework to exploit indirect prompt injection vulnerabilities . they use a Monte Carlo tree-based algorithm to iteratively refine inputs .
Outcome:	The proposed framework achieves 71% and 70% success rates against two public benchmarks .

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Xianshu Peng, Wei Wei

Search-in-Context: Efficient Multi-Hop QA over Long Contexts via Monte Carlo Tree Search with Dynamic KV Retrieval (2025.findings-acl)

Challenge:	Existing methods to enhance large models for multi-hop question-answering lack the ability for multipath exploration, strategic look-ahead, stepwise evaluation, and global selection.
Approach:	They propose an algorithm guided by Monte Carlo Tree Search and process rewards that assigns fine-grained rewards to each step in the search path.
Outcome:	The proposed algorithm outperforms various RAG-based methods on four multihop QA datasets and shows that it can self-train and self-update.

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Jiabei Chen, Guang Liu, Shizhu He, Kun Luo, Yao Xu, Jun Zhao, Kang Liu

From Outcome to Process: Optimizing MoE Load Balancing with MCTS (2026.findings-acl)

Challenge:	Existing approaches to multihop question answering (MHQA) over long contexts are often neglecting explicit reasoning or incurring expensive computational costs due to full-attention mechanisms over long contextuals.
Approach:	They propose a framework that integrates Monte Carlo Tree Search (MCTS) with dynamic key-value retrieval to enable iterative, context-aware reasoning.
Outcome:	The proposed framework integrates Monte Carlo Tree Search (MCTS) with dynamic key-value (KV) retrieval to enable iterative, context-aware reasoning.

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Wenjun Ke, Hengyuan Xu, Ziyu Shang, Yao He, Jiahao Wang, Zijie Xu, Peng Wang, Yuhang Lou, Jiajun Liu

Green & Sustainable NLP

Challenge:	Existing balancing strategies focus on constraining the final distribution of expert usage, but overlook the routing decisions made at each layer.
Approach:	They propose a three-stage framework that leverages process-level rewards to guide balanced expert routing.
Outcome:	Extensive experiments show that LayerMoE improves the performance of state-of-the-art LoRA-MoA baselines, yielding an average accuracy gain of 1.39%.

ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs (2026.acl-long)

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Hongxin Ding, Baixiang Huang, Yue Fang, Weibin Liao, Xinke Jiang, Jinyang Zhang, Yinghao Zhu, Zheng Li, Liantao Ma, Junfeng Zhao, Yasha Wang

Challenge:	Existing medical Large Language Models (LLMs) follow a reactive paradigm, risking diagnostic errors by answering before seeking sufficient details.
Approach:	They propose a reinforcement learning framework that transitions LLMs toward a proactive paradigm, enabling them to ask clinically valuable questions before decision-making.
Outcome:	Experiments on partial-information medical benchmarks show that ProMed outperforms state-of-the-art methods by 6.29% on average and delivers a 54.45% gain over the reactive paradigm.

BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving (2025.acl-long)

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Ran Xin, Chenguang Xi, Jie Yang, Feng Chen, Hang Wu, Xia Xiao, Yifan Sun, Shen Zheng, Ming Ding

AutoCT: Automating Interpretable Clinical Trial Prediction with LLM Agents (2025.emnlp-main)

Challenge:	Existing approaches to theorem proving in large language models rely on value functions and/or Monte Carlo Tree Search (MCTS), but the potential of simpler methods like Best-First Tree Search remains underexplored.
Approach:	They propose a scalable expert iteration framework that implements strategic data filtering at each expert iteration round, excluding problems solvable via beam search node expansion to focus on harder cases.
Outcome:	The proposed framework achieves a state-of-the-art score of 72.95 on the MiniF2F test set and challenges the perceived necessity of complex tree search methods.

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Fengze Liu, Haoyu Wang, Joonhyuk Cho, Dan Roth, Andrew Lo

Explainability & Interpretability in NLP

Challenge:	Clinical trials are expensive and time-consuming, and accurate trial prediction is key to advancing medical treatments.
Approach:	They propose a framework that combines reasoning capabilities of large language models with the explainability of classical machine learning to generate, evaluate, and refine tabular features without human input.
Outcome:	The proposed framework performs better than SOTA methods on clinical trial prediction tasks within a limited number of iterations.

SeDev: Structured Semantic Exploration for LLM-Driven Code Generation (2026.acl-long)

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Ronghui Yang, Jie Liu, Jiajie Zeng, Jiexin Wang, Jiuchuan Jiang, Bo An, Yi Cai, Mengchen Zhao

SQLAgent: Learning to Explore Before Generating as a Data Engineer (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have shown remarkable capabilities in automating code generation, but they suffer from insufficient exploration of the vast solution space.
Approach:	They propose a large-scale LLM-driven code generation framework that efficiently finds high-quality solutions in only a few iterations.
Outcome:	The proposed framework outperforms baselines while maintaining reasonable time and computational costs.

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Wenjia Jiang, Yiwei Wang, Boyan Han, Joey Tianyi Zhou, Chi Zhang

Uncertainty-Aware Test-Time Search for Optimization Problem Solving (2026.acl-long)

Challenge:	Existing approaches to large language models fail to generalize in complex, real-world settings due to database-specific nature of SQL reasoning.
Approach:	They propose a two-stage LLM-based framework that decouples knowledge acquisition from query generation.
Outcome:	The proposed framework significantly improves accuracy over baselines on large-scale benchmarks.

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Linlin Yu, Xujiang Zhao, Dong Li, Yanchi Liu, Wei Cheng, Zhengzhang Chen, Chen Zhao, Feng Chen, Haifeng Chen

Multi-LLM Collaborative Search for Complex Problem Solving (2026.findings-acl)

Challenge:	Language model hallucinations and limited availability of labeled datasets often result in misaligned formulations, code errors and feasibility failures.
Approach:	They propose a Monte Carlo Tree Search framework that automates optimization problems from natural language descriptions with efficiency and reliability.
Outcome:	The proposed framework achieves state-of-the-art solution accuracy and reduces token usage.

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Sen Yang, Yafu Li, Wai Lam, Yu Cheng

CodeRM-NT: Reward Model for Code RL without Unit Tests (2026.findings-acl)

Challenge:	Large language models (LLMs) often struggle with complex reasoning tasks due to the vast reasoning space inherent in the complexity and inherent ambiguities of natural languages.
Approach:	They propose a mixture-of-search-agents paradigm that integrates diverse reasoning pathways by combining independent exploration and iterative refinement among multiple LLMs.
Outcome:	The proposed approach improves performance over single-agent and multi-agend baselines in complex mathematical and commonsense reasoning tasks.

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Xiao Xia, Dan Zhang, Tianrui Sun

Reinforced Efficient Reasoning via Semantically Diverse Exploration (2026.acl-long)

Challenge:	Existing methods rely on unit tests to evaluate code correctness and provide rewards, but these methods are difficult to verify at scale.
Approach:	They propose a code reward model that leverages Monte Carlo Tree Search guided by LLMs to generate code snippets and judges execution traces to annotate code with reward signals.
Outcome:	The proposed model outperforms synthetic unit tests on multiple code generation benchmarks and improves curriculum learning.

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Ziqi Zhao, Zhaochun Ren, Jiahong Zou, Liu Yang, Zhiwei Xu, Xuri Ge, Zhumin Chen, Xinyu Ma, Daiting Shi, Shuaiqiang Wang, Dawei Yin, Xin Xin