Author

Eli Schwartz

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#artificial intelligence Mar 2026

Look Where It Matters: High-Resolution Crops Retrieval for Efficient VLMs

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

#natural language process... Preprint Sep 2026

Two-Token Features and Small-Large Ensembles for VLM Hallucination Detection

This work presents a system for the SHROOM-Visions 2026 shared task on character-level VLM hallucination detection that uses synthetic hallucination data generated by the large model as a source of ensemble diversity, and uses validation to select feature layer, training data and OCR grounding.

Eli Schwartz · 0 citations