L-MAD framework systematically evaluates multi-agent debate structures in legal reasoning
Researchers introduced the Legal Multi-Agent Debate (L-MAD) framework to evaluate different debate structures and aggregation methods for Legal Textual Entailment. Assigning distinct expert personas to multiple agents improved upon strong single-agent baselines by up to 8%. The study also revealed a trade-off between agent population size and performance.
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AI debate platform with RAG, moderator, and judge
A platform where two AI agents debate opposing positions using real data via RAG. A separate moderator agent tries to derail the leading debater, and a judge agent fact-checks and scores the debate. Designed for exploring AI argumentation and fact-checking dynamics.
ProofCouncil: An LLM Agent for Solving Open Mathematical Problems
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GATS framework eliminates LLM calls during agent planning inference
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AgentMaker: a new Python framework for building LLM agents and multi-agent systems
AgentMaker is a general-purpose Python framework for building LLM agents and multi-agent systems, featuring tools, memory, RAG, context engineering, guardrails, human-in-the-loop, and observability. It is released under MIT license on GitHub and PyPI.