MAD-OPD recasts on-policy distillation teachers as a debating collective to supply better supervision, lifting agentic and code performance over single-teacher OPD across multiple model sizes.
Mixed citations
Debate or vote: Which yields better decisions in multi-agent large language models?
Mixed citation behavior. Most common role is background (40%).
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Pith papers citing it
Background
40% of classified citations
citation-role summary
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citation-polarity summary
years
2026 7verdicts
UNVERDICTED 7roles
background 5representative citing papers
WORC improves multi-agent LLM reasoning to 82.2% average accuracy by predicting and compensating for the weakest agent via targeted extra sampling rather than uniform reinforcement.
SDRL trains LLMs via self-generated multi-path debates and joint optimization of standalone plus debate-conditioned responses to boost both single-model reasoning and multi-agent debate performance.
Conformal Social Choice aggregates verbalized probabilities from LLM debates via linear opinion pooling and uses split conformal prediction to generate prediction sets that guarantee inclusion of the correct answer with probability at least 1-alpha, enabling adjustable safe act-or-escalate decisions
AgentCity introduces a Separation of Power constitutional architecture on blockchain for governing autonomous agent economies through agent legislation, automated execution, and human accountability.
On Codeforces problems, independent k-shot sampling achieves better accuracy-cost and accuracy-query tradeoffs than agentic reasoning, even with prompt caching.
EMS reduces the average number of agents invoked for majority voting by 32% via reliability-aware prioritization and early stopping on six benchmarks.
citing papers explorer
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MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate
MAD-OPD recasts on-policy distillation teachers as a debating collective to supply better supervision, lifting agentic and code performance over single-teacher OPD across multiple model sizes.
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Weak-Link Optimization for Multi-Agent Reasoning and Collaboration
WORC improves multi-agent LLM reasoning to 82.2% average accuracy by predicting and compensating for the weakest agent via targeted extra sampling rather than uniform reinforcement.
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Learning from Self-Debate: Preparing Reasoning Models for Multi-Agent Debate
SDRL trains LLMs via self-generated multi-path debates and joint optimization of standalone plus debate-conditioned responses to boost both single-model reasoning and multi-agent debate performance.
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From Debate to Decision: Conformal Social Choice for Safe Multi-Agent Deliberation
Conformal Social Choice aggregates verbalized probabilities from LLM debates via linear opinion pooling and uses split conformal prediction to generate prediction sets that guarantee inclusion of the correct answer with probability at least 1-alpha, enabling adjustable safe act-or-escalate decisions
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AgentCity: Constitutional Governance for Autonomous Agent Economies via Separation of Power
AgentCity introduces a Separation of Power constitutional architecture on blockchain for governing autonomous agent economies through agent legislation, automated execution, and human accountability.
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When Independent Sampling Outperforms Agentic Reasoning
On Codeforces problems, independent k-shot sampling achieves better accuracy-cost and accuracy-query tradeoffs than agentic reasoning, even with prompt caching.
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EMS: Multi-Agent Voting via Efficient Majority-then-Stopping
EMS reduces the average number of agents invoked for majority voting by 32% via reliability-aware prioritization and early stopping on six benchmarks.