A bipartite factor graph with message-passing protocol and asymmetric damping aggregates multi-LLM predictions, cutting token use by 97% and API calls by 6X while outperforming baselines on MMLU, MMLU-Pro, GPQA, and MedMCQA.
Rethinking mixture-of-agents: Is mixing different large language models beneficial?
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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 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.
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