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Multi-Agent Flow Matching with Decoupled Generative Guidance

Paper recorded by Signals 4 on 2026-09-29 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-29 on arXiv · recorded by Signals 4 on 2026-09-30

Category: cs.LG · 机器学习 · first seen 2026-09-30

Abstract

Generative modeling is widely used for producing diverse objects from complex, multimodal distributions. However, its expressivity does not, in general, come with formal guarantees that the generated objects satisfy hard constraints or requirements. In multi-agent generation, this problem becomes more challenging because a hard requirement can depend on multiple agents, while each agent may need t

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#4 most recent of 334 cs.LG papers we have recorded · ↑ newer: A Spectral Theory of Distortion in LLM Graph Reconstruction: Sharp Bou · ↓ older: Achieving an $O(1/N)$ Optimality Gap in Average-Reward Weakly-Coupled
Cite this page: Multi-Agent Flow Matching with Decoupled Generative Guidance: the #4 most recent of 334 cs.LG papers we have recorded (as of 2026-09-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/multi-agent-flow-matching-with-decoupled-generative-guidance.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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