Papers with MASs
Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design (2026.findings-acl)
Copied to clipboard
| Challenge: | Prior studies have focused on designing customized MAS for specific tasks . a critical research question remains: do LLM agent groups exhibit a form of "general intelligence" |
| Approach: | They find a Collective Intelligence factor in human groups that captures their general capability. |
| Outcome: | The proposed model predicts the ACI factor based on the features of LLM agent groups and can improve generalization abilities. |
Seeing the Whole Elephant: A Benchmark for Failure Attribution in LLM-based Multi-Agent Systems (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks rely on partially observable traces that capture only agent outputs . lack of full execution traces obscures many failure causes, authors argue . |
| Approach: | They propose a benchmark that allows attribution under full execution observability . they find full traces improve attribution accuracy by up to 76.5% over a partial-observation counterpart . |
| Outcome: | The proposed benchmark improves attribution accuracy by up to 76.5% over a partial-observation counterpart. |
LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have enabled Multi-Agent Systems (MASs) where agents interact through natural language to solve complex tasks or simulate multi-party dialogues. |
| Approach: | They propose a linguistically-grounded game-theoretic paradigm for multi-agent dialogue generation that uses a training-free equilibrium approximation algorithm to model dialogue over communicative intents and strategies. |
| Outcome: | The proposed framework improves agents’ communication efficiency by helping them convey their intended meaning more effectively through language. |