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.
Researchers released ZGCM-1, a 7-billion-parameter dense foundation model trained from scratch with a focus on data, system, and algorithmic efficiency. The work argues that smaller models should not try to memorize the open web, but instead be optimized for targeted capabilities such as mathematical reasoning and agentic search. It is presented as a fully open release.
A new arXiv paper introduces CITECHOICE, a causal audit framework for studying citation allocation in multi-turn agentic search, where an answer engine cites only some of the retrieved sources that support the same claim. The study is built from 129 everyday queries and examines how the way documents are presented redistributes credit among sources.
A new arXiv paper introduces LongAgent, an agentic search method that uses patient history to predict future medical outcomes from longitudinal data. The authors note that such datasets are heterogeneous, with many variables collected across different sources and time points. The approach aims to extract representations from this messy, multi-source data that improve downstream outcome prediction.