A new arXiv paper introduces VideoScout, a method that lets multimodal language models actively explore long videos instead of relying on uniform frame sampling. The approach pairs agentic search with adaptive reasoning pacing to work around limited visual context windows. It targets the problem of long-video understanding, where current models still lag behind their short-video performance.
A new arXiv preprint evaluates keyframe selection techniques that can be plugged into existing multimodal large language models without modifying them. The work targets the constraint that MLLMs cannot ingest every frame of a long video due to visual-token and compute limits, and compares the main families of approaches proposed to address this. The study positions keyframe selection as a model-agnostic add-on for improving long-video understanding.
Researchers propose MarKey, a method that picks keyframes for long-video question answering using a greedy strategy driven by marginal utility rather than dense encoding or uniform sampling. The approach targets multimodal large language models, where encoding every frame is costly and even sampling can overlook brief but important moments. The paper is an arXiv preprint and appears under a cross-listing.