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This paper presents a function-centric framework using Transcoders to trace computational pathways in vision-language models, demonstrating stronger attribution of visual grounding and the ability to predict hallucinations via graph-based features.
This paper introduces ForMaT, a parallel corpus of 3,956 PDFs across 15 language pairs designed for visually-grounded multilingual translation, preserving layout metadata to benchmark layout-aware MT systems.
MementoGUI introduces a plug-in agentic memory framework for GUI agents that uses learned controllers for selective memory management and retrieval, improving performance on long-horizon tasks with compressed visual and textual representations.
This paper presents a case study on visually-guided movie subtitle translation for low-resource Indic languages, demonstrating that selective visual grounding improves translation quality while addressing temporal misalignment challenges.
The paper introduces SeePhys Pro, a benchmark to diagnose modality transfer issues in multimodal RL for physics reasoning, revealing that models struggle with representation-invariant reasoning and often rely on residual textual cues rather than visual evidence.
HyperEyes is a parallel multimodal search agent that uses dual-grained reinforcement learning to optimize inference efficiency, achieving higher accuracy with significantly fewer tool-call rounds compared to existing agents.