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arXiv AI By Hongyu Shi, Sen Zhao, Zuyu Zhang, Lifeng Shen, Ding Zou, Xinyu He, Xu Zhang, Qinghua Zhang

Recompose and Refine Latent Reasoning Flows for Vision-Language-Action Models

Read the original on arXiv AI →

The paper introduces Reasoning and Flow Memory (FLOWMEM), a vision‑language‑action model that reuses successful latent reasoning steps instead of discarding them after each policy query. FLOWMEM dynamically retrieves and recomposes compatible latent fragments to form a reasoning route that aligns with the temporal structure of prior successful computations, then refines this route with current visual and proprioceptive data before generating actions. Experiments on RoboMME and LIBERO‑Plus show that FLOWMEM improves success rates by 1.7 and 4.1 percentage points over memory‑free policies, demonstrating the benefit of reusing latent computation for closed‑loop control.

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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