AuDisAgent reformulates multimodal controversy detection as a dynamic audience dissemination process using screening, panel discussion, and arbitration agents, plus comment bootstrapping, and reports outperforming prior static methods on a public dataset.
Magis: Llm-based multi-agent frame- work for github issue resolution.arXiv:2403.17927
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A purpose-built, staged LLM agent correctly sets up and executes software analysis tools on 33 of 35 benchmark tasks, outperforming general-purpose agent baselines by at least 17 percentage points.
Multi-agent LLM systems can be steered via prompt design from mere aggregates to higher-order collectives with identity-linked differentiation and goal-directed complementarity, as measured by partial information decomposition of time-delayed mutual information.
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From Static Analysis to Audience Dissemination: A Training-Free Multimodal Controversy Detection Multi-Agent Framework
AuDisAgent reformulates multimodal controversy detection as a dynamic audience dissemination process using screening, panel discussion, and arbitration agents, plus comment bootstrapping, and reports outperforming prior static methods on a public dataset.
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Evaluating LLM Agents on Automated Software Analysis Tasks
A purpose-built, staged LLM agent correctly sets up and executes software analysis tools on 33 of 35 benchmark tasks, outperforming general-purpose agent baselines by at least 17 percentage points.
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Emergent Coordination in Multi-Agent Language Models
Multi-agent LLM systems can be steered via prompt design from mere aggregates to higher-order collectives with identity-linked differentiation and goal-directed complementarity, as measured by partial information decomposition of time-delayed mutual information.