Finding the right video often requires distinguishing similar scenes in which different events occur. Joint matching improves retrieval, but processing rich video representations for each query is costly. VEDJE compresses features within sampled frames while keeping their representations separate in a reusable cache. F...
S. Wagner, Gabriele Serussi, D. Ben-Ami et al.· 0 citations
Streaming video-language models must decide not only what to answer, but whether the evidence needed for the current question has arrived. Existing systems learn that decision as a separate trigger; we ask whether an unmodified model already computes it. We show that frozen VideoLLMs carry a linearly readable evidence-...
This work presents AwaRes, a spatial-on-demand framework that resolves this accuracy-efficiency trade-off by operating on a low-resolution global view and using tool-calling to retrieve only high-resolution segments needed for a given query.
Nimrod Shabtay, M. Kimhi, Artem Spector et al.· arXiv.org· 2 citations
It is shown that diffusion remasking can promote recovery from harmful intermediate generations, provide evidence that this behavior is tied to the sampling mechanism, and demonstrate that switching from AR to diffusion sampling improves jailbreak robustness, including under fixed model weights.
Eliron Rahimi, Elad Hirshel, Rom Himelstein et al.· arXiv.org· 2 citations
The results show that reliable chest X-ray retrieval requires training objectives that model not only which findings are mentioned, but also how they are clinically asserted, as well as a label-aware contrastive fine-tuning objective for clinical retrieval.
Tom Erez, M. Kimhi, Chaim Baskin et al.· arXiv.org· 0 citations
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