Challenge: Current Large Language Models focus on syntax and ignore the vital semantic signals in code comments.
Approach: They propose a Multi-Agent framework for COmment-guided code Refactoring that populates original code with precise comments to provide necessary semantic guidance for subsequent refactoring.
Outcome: The proposed framework significantly improves code quality and achieves higher developer acceptance compared to baselines.

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MASA: LLM-Driven Multi-Agent Systems for Autoformalization (2025.emnlp-demos)

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Challenge: This paper presents a framework for building multi-agent systems for autoformalization driven by Large Language Models.
Approach: They propose a framework for building multi-agent systems for autoformalization driven by Large Language Models.
Outcome: The proposed framework leverages collaborative agents to convert natural language statements into formal representations.
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation (2025.coling-main)

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Challenge: Recent advances in Large Language Models have demonstrated remarkable performance across tasks.
Approach: They propose a benchmark self-evolving framework to dynamically evaluate rapidly advancing Large Language Models.
Outcome: The proposed framework extends existing benchmarks to extend models across tasks and tasks.
A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)

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Challenge: Large Language Models have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation.
Approach: They present a comprehensive synthesis of large language models and their applications . they dissect a four-module agent architecture and review representative designs .
Outcome: The proposed models address fundamental challenges in traditional recommender systems . they include limited comprehension of complex user intents, insufficient interaction capabilities .
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

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Challenge: Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents.
Approach: They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
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CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models (2025.findings-acl)

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Challenge: State-of-the-art large language models (LLMs) have demonstrated impressive code generation capabilities but struggle with real-world software engineering tasks such as revising source code to address code reviews.
Approach: They propose a benchmark to evaluate large language models' ability to bridge both technical and conversational contexts by decomposing the generation task of code refinement into three essential reasoning steps.
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CodeAgent: Autonomous Communicative Agents for Code Review (2024.emnlp-main)

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Challenge: Existing methods for code review rely on single input-output generative models and thus lack the collaborative nature of code review.
Approach: They propose a multi-agent Large Language Model (LLM) system for code review automation that incorporates a supervisory agent to ensure that all the agents’ contributions address the initial review question.
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Small LLMs Are Weak Tool Learners: A Multi-LLM Agent (2024.emnlp-main)

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Challenge: Large Language Models (LLMs) have revolutionized natural language processing with impressive capabilities, but they lack domain specificity, real-time information and face challenges in solving specialized problems.
Approach: They propose a multi-LLM approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer.
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Mitigating Cultural Bias in LLMs via Multi-Agent Cultural Debate (2026.findings-acl)

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Challenge: Existing approaches to evaluate large language models fail to address cultural bias in non-Western languages . Chinese prompting shifts bias toward East Asian perspectives rather than eliminating it, authors say .
Approach: They propose a Chinese–English bilingual benchmark and multi-agent vote frameworks that enable explicit "no bias" judgments.
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TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents (2025.emnlp-demos)

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Challenge: Existing research systems often design and use agentic workflows to perform research tasks such as ideation, scientific coding, review writing, and tree-based search.
Approach: They propose an open-source codebase, an interactive web demonstration, and a PyPI Python package to make state-of-the-art auto-research pipelines broadly accessible to every researcher and developer.
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MAC-Reasoner: A Multi-Agent Collaborative Framework for Enhancing Logical Reasoning in Large Language Models (2026.findings-acl)

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Challenge: Existing methods for logical reasoning are limited to a limited number of tasks.
Approach: They propose a multi-agent framework that constructs a Logic-Augmented Context to enhance LLMs’ reasoning by a translator agent.
Outcome: The proposed framework improves on three backbone LLMs on four challenging benchmarks and shows consistent and robust improvements over baselines.

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