| 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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| Challenge: | Existing methods for ensembling language models fail to address complex reasoning tasks. |
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Collaborative Beam Search: Enhancing LLM Reasoning via Collective Consensus (2025.emnlp-main)
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| Challenge: | Existing approaches to improve the reasoning capabilities of large language models (LLMs) depend on domain-specific external verifiers or self-evaluation which is brittle and prompt-sensitive. |
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Getting MoRE out of Mixture of Language Model Reasoning Experts (2023.findings-emnlp)
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| Challenge: | Existing large language models (LLMs) have poor generalizability on question types beyond those seen in the prompt. |
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Grammar Search for Multi-Agent Systems (2026.acl-long)
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Mayank Singh, Vikas Yadav, Shiva Krishna Reddy Malay, Shravan Nayak, Sai Rajeswar, Sathwik Tejaswi Madhusudhan, Eduardo Blanco
| Challenge: | Several prior approaches have relied on LLM-based free-form search over the code space. |
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CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have shown great ability in solving traditional natural language tasks and elementary reasoning tasks with appropriate prompting techniques. |
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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
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Small LLMs Are Weak Tool Learners: A Multi-LLM Agent (2024.emnlp-main)
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| Challenge: | Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems . |
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Puzzle Solving using Reasoning of Large Language Models: A Survey (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated their logical reasoning abilities across various domains. |
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Planning with Multi-Constraints via Collaborative Language Agents (2025.coling-main)
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| Challenge: | Recent advances in neural language models have sparked a new surge of intelligent agent research. |
| Approach: | They propose a method for collaborative LLM-based multi-agent systems that simplifies complex task planning with constraints by decomposing it into a hierarchy of subordinate tasks. |
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