✍️ New blog post by Amit Kayal

One author, one reviewer

#aws #agentskills #multagent

KVComm reduces inter‑LLM traffic by sending only the top 30% of key‑value pairs, preserving performance close to the no‑communication baseline, according to recent arXiv results. https://getnews.me/kvcomm-proposes-selective-kv-sharing-for-efficient-llm-communication/ #kvcomm #llm #multagent

The blackboard multi‑agent system improves data discovery, delivering 13%‑57% higher task success and up to a 9% boost in F1 score, the authors report. Read more: https://getnews.me/llm-driven-multi-agent-blackboard-system-enhances-data-discovery/ #multagent #blackboard #datadiscovery

The Reinforced Advantage feedback (ReAd) framework improves multi‑agent coordination, achieving higher success rates on Overcooked‑AI and a RoCoBench variant while cutting LLM query rounds. Read more: https://getnews.me/efficient-llm-grounding-boosts-multi-agent-collaboration/ #llm #multagent

MAS² introduces a tri‑agent framework that recursively designs, builds, and refines its own agents. It was tested on seven benchmark suites, showing performance gains up to 19.6% over leading baselines. https://getnews.me/mas2-self-generative-self-configuring-multi-agent-system/ #mas2 #multagent #ai

SummQ, an adversarial multi‑agent system with four specialized agents, outperformed state‑of‑the‑art on three long‑document summarization benchmarks (ROUGE, BERTScore) in September 2025. Read more: https://getnews.me/summq-adversarial-multi-agent-system-improves-document-summaries/ #summq #multagent

Researchers presented an online multi-agent control framework with sub-linear regret per agent under adversarial disturbances; the work was posted June 2025 and updated September 2025. Read more: https://getnews.me/robust-online-multi-agent-control-with-adversarial-disturbances/ #multagent #control

Researchers introduced MA-SPL and MA-MPL, two policy-learning algorithms with a (1‑c/e) approximation guarantee, accepted at NeurIPS 2025; the paper appeared on 26 September 2025. https://getnews.me/new-algorithms-boost-multi-agent-coordination-beyond-submodular-limits/ #neurips2025 #multagent

Researchers released Visual Flow, an overlay for visual multi‑agent systems that curbs hallucination snowballing. It was evaluated on eight benchmark tasks with ten base VLMs. Read more: https://getnews.me/visual-multi-agent-systems-reduce-hallucination-snowballing/ #visualflow #multagent

AEGIS, a new framework, injects and tracks errors in multi‑agent system trajectories using an LLM manipulator, and releases a public dataset for training robust models. Read more: https://getnews.me/aegis-introduces-automated-error-generation-for-multi-agent-systems/ #aegis #multagent #erroranalysis

A multi‑agent AI framework evaluated on the eICU database improved mortality prediction to 59% (vs 56% for a single‑agent system) and reduced length‑of‑stay error to 4.37 days. https://getnews.me/multi-agent-ai-system-boosts-icu-decision-accuracy-and-transparency/ #ai #icucare #multagent

Multi‑Agent Consensus Alignment (MACA) improves language model consistency, boosting self‑consistency by 27.6% on the GSM8K benchmark. Read more: https://getnews.me/multi-agent-consensus-alignment-boosts-language-model-consistency/ #multagent #aialignment

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