MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have advanced natural language processing, demonstrating exceptional reasoning, tool usage, and memory capabilities. |
| Approach: | They propose a competition-based benchmark framework specifically designed to assess LLMs within multi-agent environments. |
| Outcome: | The proposed framework enhances the LLMs’ abilities in navigating complex social and cognitive dimensions by over threefold between the strongest and weakest LLM models. |
Similar Papers
LLMsPark: A Benchmark for Evaluating Large Language Models in Strategic Gaming Contexts (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) are increasingly important for their intelligence evaluation. |
| Approach: | They propose a game theory-based evaluation platform that measures LLMs’ decision-making strategies and social behaviors in classic game-theoretic settings. |
| Outcome: | The proposed system cross-evaluates 15 leading LLMs using leaderboard rankings and scoring mechanisms. |
LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments (2024.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for evaluating large language models use static datasets, leading to data leakage or overlooking the complexities of multi-agent interactions. |
| Approach: | They propose a framework that evaluates the diverse capabilities of LLM agents in multi-agent dynamic environments. |
| Outcome: | The proposed framework assesses the diverse capabilities of LLM agents in multi-agent dynamic environments. |
Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)
Copied to clipboard
| Challenge: | Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems . |
| Approach: | This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems . it focuses on three core components: model distillation, dynamic routing, memory- and compute efficient serving . |
| Outcome: | This tutorial introduces state-of-the-art techniques for building efficient and efficient multi-agent LLM systems . it covers coordination and communication among agents, crucial for collective performance . |
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)
Copied to clipboard
Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Large language models are increasingly employed to empower autonomous agents to simulate human behavior. |
| Approach: | They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts. |
| Outcome: | The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning. |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
Copied to clipboard
Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| 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. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)
Copied to clipboard
| 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 . |
MultiAgentBench : Evaluating the Collaboration and Competition of LLM agents (2025.acl-long)
Copied to clipboard
Kunlun Zhu, Hongyi Du, Zhaochen Hong, Xiaocheng Yang, Shuyi Guo, Zhe Wang, Zhenhailong Wang, Cheng Qian, Robert Tang, Heng Ji, Jiaxuan You
| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents, yet existing benchmarks focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition. |
| Approach: | They propose a benchmark to evaluate LLM-based multi-agent systems across diverse, interactive scenarios. |
| Outcome: | The proposed framework measures task completion and quality of collaboration and competition using novel, milestone-based key performance indicators. |
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation (2025.coling-main)
Copied to clipboard
| 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. |
CToolEval: A Chinese Benchmark for LLM-Powered Agent Evaluation in Real-World API Interactions (2024.findings-acl)
Copied to clipboard
| Challenge: | a benchmark is designed to evaluate the capabilities of large language models (LLMs) as agents in decision making and operational tasks. |
| Approach: | They propose a benchmark to evaluate LLMs in the context of Chinese societal applications . they propose he benchmark will evaluate tool invocation ability of LLM and task completion ability . |
| Outcome: | The proposed benchmark features 398 APIs across 27 widely-used Apps across 14 domains. |
Language Model Council: Democratically Benchmarking Foundation Models on Highly Subjective Tasks (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing evaluations of Large Language Models (LLMs) rely on a single large model to score outputs from other LLMs, but this is prone to intra-model bias and many tasks may be too subjective for a one model to judge fairly. |
| Approach: | They propose a language model council where a group of LLMs collaborate to create tests, respond to them, and evaluate each other’s responses to produce a ranking in a democratic fashion. |
| Outcome: | The proposed model produces rankings that are more separable and robust than any individual LLM judge. |