Searching Videos as Trees: Self-Correcting Agents for Grounded Long Video QA

TL;DR - VideoTreeSearch enables agents to answer questions about long videos by navigating an adaptive temporal tree with explicit backtracking. This self-correcting search substantially improves evidence localization and answer accuracy.

• Builds non-uniform temporal trees from scene boundaries to represent semantically coherent video segments.
• Supports zoom_in, zoom_out, shift, and answer operations, allowing recovery from premature or incorrect search decisions.
• Trains with synthesized trajectories containing deliberate detours, followed by supervised fine-tuning and reinforcement learning.
• Outperforms prior agentic methods by 12.5 mIoU on CG-Bench and 7.4 T-F1 on Haystack-Ego4D, while transferring to general long-video QA.