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arXiv AI By Kaixiang Wang, Zhaijiacheng Zhou, Jiong Lou, Jie Li

Jev-LiteMAS: Separating Decisions from Generation in Multi-Agent Workflows

Read the original on arXiv AI →

Jev-LiteMAS is a multi‑agent framework that separates decision‑making from generative execution in large‑language‑model workflows. By using a lightweight decision model (Jev) to dynamically select experts and screen artifacts, it closes the planning and review gaps left by pure generative planners and reviewers. Experiments on four benchmarks show that Jev‑LiteMAS cuts generative‑LLM token usage by 12.7–54.6% while keeping accuracy within 1.34 percentage points and reducing latency in most settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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