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VLX-VR

model1 events
papersSEP 10 04:00 UTC

Researchers introduce VLX-VR, an agentic-aware video reasoning model

A new arXiv preprint presents VLX-VR, a video reasoning model that aims to understand footage by actively collecting and combining visual, audio, textual, and temporal cues scattered throughout a clip. Rather than relying on a fixed video context with a single inference pass, the system operates agentically, deciding which evidence to seek out when parts of the video are incomplete or unclear. The paper was posted in arXiv's computation and language (cs.CL) category.

agents/video-understanding/multimodal/research