arXiv Computer Vision October 26, 2026 By Jingmin Zhu, Yanyan Zou, Xiuyu Li, James Bailey, Dinh Phung, Tao Yu, Yunyun Hou, Jun Liu, Jun Zhou, Sirui Wang, Nan Duan, Jiachen Liu, Qiuhong Ke

Tempo-Zero: Recursive Self-Play in Executable Worlds for Video Reasoning

Read the original on arXiv Computer Vision →

Tempo‑Zero is a self‑improvement framework for video reasoning that uses recursive self‑play in executable environments. It closes a loop of environment execution, self‑play verification, and environment evolution, allowing a single policy to act as both questioner and solver and to receive execution‑grounded rewards. The approach trains only on simple programs, without external video data or supervised fine‑tuning, and achieves superior performance on twelve real‑world video benchmarks, especially on temporal grounding tasks.

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 Computer Vision.

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