Refute-or-Promote applies adversarial multi-agent review with kill gates and empirical verification to filter LLM defect candidates, killing 79-83% before disclosure and yielding 4 CVEs plus multiple accepted fixes across libraries, C++ standard, and compilers.
Rethinking mixture-of-agents: Is mixing different large language models beneficial?
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Pyramid MoA is a hierarchical Mixture-of-Agents system with a decision-theoretic router that achieves up to 42.9% compute savings while nearly matching oracle accuracy on MBPP, GSM8K, MMLU, HumanEval, and MATH.
SANet uses semantic-aware AI agents for cross-layer 6G optimization, achieving up to 14.61% performance gains with 44.37% of the FLOPs of prior methods via model partitioning and decentralized multi-objective algorithms.
SIGMA builds a signed relational graph among LLM agents and uses conflict-aware message passing plus weighted aggregation to produce more consistent predictions than prior cooperative-assumption baselines.
LLM reliability techniques are unified as communication channel operators, with a new cost-aware router achieving superior quality-cost tradeoffs on hard tasks.
MAC framework selects Pareto-optimal LLM agents and masks low cross-consistency outputs for adaptive collaboration in medical decision-making.
SMCS coordinates 15 open-source LLMs via retrieval-based prior selection and exploration-exploitation posterior enhancement, outperforming GPT-4.1 by 5.36% and GPT-o3-mini by 5.28% on eight benchmarks.
Execution feedback in refinement loops improves 1-3B code generation performance far more than complex pipeline topologies discovered via evolutionary search on HumanEval and sanitized MBPP.
citing papers explorer
-
Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery
Refute-or-Promote applies adversarial multi-agent review with kill gates and empirical verification to filter LLM defect candidates, killing 79-83% before disclosure and yielding 4 CVEs plus multiple accepted fixes across libraries, C++ standard, and compilers.
-
Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference
Pyramid MoA is a hierarchical Mixture-of-Agents system with a decision-theoretic router that achieves up to 42.9% compute savings while nearly matching oracle accuracy on MBPP, GSM8K, MMLU, HumanEval, and MATH.
-
SANet: A Semantic-aware Agentic AI Networking Framework for Cross-layer Optimization in 6G
SANet uses semantic-aware AI agents for cross-layer 6G optimization, achieving up to 14.61% performance gains with 44.37% of the FLOPs of prior methods via model partitioning and decentralized multi-objective algorithms.
-
Conflict-Resilient Multi-Agent Reasoning via Signed Graph Modeling
SIGMA builds a signed relational graph among LLM agents and uses conflict-aware message passing plus weighted aggregation to produce more consistent predictions than prior cooperative-assumption baselines.
-
A Communication-Theoretic Framework for LLM Agents: Cost-Aware Adaptive Reliability
LLM reliability techniques are unified as communication channel operators, with a new cost-aware router achieving superior quality-cost tradeoffs on hard tasks.
-
MAC: Masked Agent Collaboration Boosts Large Language Model Medical Decision-Making
MAC framework selects Pareto-optimal LLM agents and masks low cross-consistency outputs for adaptive collaboration in medical decision-making.
-
A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement
SMCS coordinates 15 open-source LLMs via retrieval-based prior selection and exploration-exploitation posterior enhancement, outperforming GPT-4.1 by 5.36% and GPT-o3-mini by 5.28% on eight benchmarks.
-
Feedback Over Form: Why Execution Feedback Matters More Than Pipeline Topology in 1-3B Code Generation
Execution feedback in refinement loops improves 1-3B code generation performance far more than complex pipeline topologies discovered via evolutionary search on HumanEval and sanitized MBPP.