arXiv AI
Jul 1
Investigating Multi-Agent Deliberation in Law
arXiv:2606. 30906v1 Announce Type: new Abstract: Artificial Intelligence is increasingly applied to the field of law, and has the potential to increase access to justice.
L-MAD: A Systematic Evaluation of Multi-Agent Debate Structures in Legal Reasoning
arXiv:2607. 09099v1 Announce Type: new Abstract: While multi-agent debate (MAD) frameworks have shown significant potential in general reasoning, their effectiveness in highly structured, knowledge-heavy legal domains remains under-explored.
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arXiv AI
Aug 5
Where Reasoning Diverges: Localized Multi-Agent Debate for Multi-Hop Question Answering
arXiv:2608. 01463v2 Announce Type: replace Abstract: Multi-agent debate commonly exchanges complete rationales even when disagreements concern only a few intermediate claims.
arXiv AI
Jul 31
Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges
arXiv:2607. 26212v1 Announce Type: cross Abstract: Multi-Agent Debate (MAD) is a promising paradigm for improving the accuracy and robustness of Large Language Model (LLM)-based agentic systems.
arXiv AI
Jun 16
DynaDebate: Breaking Homogeneity in Multi-Agent Debate with Dynamic Path Generation
arXiv:2601. 05746v2 Announce Type: replace Abstract: Recent years have witnessed the rapid development of Large Language Model-based Multi-Agent Systems (MAS), which excel at collaborative decision-making and complex problem-solving.
arXiv AI
Jun 30
Mixture of Debaters: Learn to Debate at Architectural Level in Multi-Agent Reasoning
arXiv:2606. 29425v1 Announce Type: new Abstract: Existing multi-agent debate frameworks suffer from two critical limitations: they rely on static architectures where agent roles and coordination patterns are fixed at design time, and they require instantiating multiple model copies, incurring substantial computational overhead.
arXiv AI
Jun 4
Consensus is Strategically Insufficient: Reasoning-Trace Disagreement as a Knowledge-Representation Signal
arXiv:2606. 04223v1 Announce Type: new Abstract: Multi-agent systems are commonly designed to reduce disagreement through voting, consensus protocols, debate, or fault-tolerant aggregation.
arXiv AI
Jun 8
CAF-Gen: A Multi-Agent System for Enriching Argumentation Structures
arXiv:2606. 06646v1 Announce Type: cross Abstract: Formalizing complex reasoning from natural text is one of the central challenges in computational linguistics.
arXiv AI
Aug 5
AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questions
arXiv:2608. 03283v1 Announce Type: new Abstract: Identifying promising scientific ideas remains an important challenge in research practice.
arXiv AI
Aug 3
M3MAD-Bench: Multi-Dimensional Evaluation of Multi-Agent Debate Across Domains and Modalities
arXiv:2601. 02854v2 Announce Type: replace Abstract: As an agent-level reasoning and coordination paradigm, Multi-Agent Debate (MAD) orchestrates multiple agents through structured debate to improve answer quality and support complex reasoning.
Hugging Face Trending Papers
Aug 4
AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questions
Identifying promising scientific ideas remains an important challenge in research practice. Researchers commonly rely on small-group discussions or one-to-one interactions with a single large language model, yet these approaches often expose them to only a limited range of perspectives and directions.
arXiv AI
Jun 2
Demystifying Multi-Agent Debate: The Role of Confidence and Diversity
arXiv:2601. 19921v2 Announce Type: replace-cross Abstract: Multi-agent debate (MAD) is widely used to improve large language model (LLM) performance through test-time scaling, yet recent work shows that vanilla MAD often underperforms simple majority vote despite higher computational cost.
arXiv AI
Jun 10
The Confident Liar: Diagnosing Multi-Agent Debate with Log-Probabilities and LLM-as-Judge
arXiv:2606. 10296v1 Announce Type: cross Abstract: Multi-agent debate systems are typically evaluated only on whether the final answer is correct, overlooking the quality of the intermediate reasoning that debate is designed to produce.