Honest heterogeneous peers in LLM debates lower harmful revision rates (e.g., 89% to 35%), while adversarial peers raise them (to 90%), and provide defense even against same-family adversaries.
Diversity of thought elicits stronger reasoning capabilities in multi-agent debate frameworks.arXiv preprint arXiv:2410.12853
3 Pith papers cite this work. Polarity classification is still indexing.
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A systematic audit of LLM-based AI societies finds that 89.7% of 39 studies violate at least one of six PIMMUR validity principles, with reproductions showing that many claimed collective behaviors disappear when controls are tightened.
APE post-trains small language models as single-agent or multi-agent prompt enhancers that improve visual alignment on image generation and editing benchmarks without altering the downstream visual model.
citing papers explorer
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Heterogeneous LLM Debate Under Adversarial Peers: Honest Gains, Replacement Costs, and Resilience
Honest heterogeneous peers in LLM debates lower harmful revision rates (e.g., 89% to 35%), while adversarial peers raise them (to 90%), and provide defense even against same-family adversaries.
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The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies
A systematic audit of LLM-based AI societies finds that 89.7% of 39 studies violate at least one of six PIMMUR validity principles, with reproductions showing that many claimed collective behaviors disappear when controls are tightened.
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APE: Agentic Prompt Enhancer for Image Generation and Editing
APE post-trains small language models as single-agent or multi-agent prompt enhancers that improve visual alignment on image generation and editing benchmarks without altering the downstream visual model.