Slot2Text: Object-Centric Visual Tokenization for Efficient and Spatially Traceable Surgical MLLMs
Multimodal large language models (MLLM) for surgical scene understanding typically inject hundreds of dense visual tokens into a language model, leading to costly inference and limited spatial traceability for generated answers. We present Slot2Text, a dual-mode surgical MLLM that replaces dense representations of visual input with a compact set of regions encoded as slot latents. Instead of relying on contrastive alignment of the visual encoder with language, Slot2Text groups self-supervised vi
Record details
Published: 2 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment
Retrieved: 4 August 2026
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ethics.ai (2 August 2026), “Slot2Text: Object-Centric Visual Tokenization for Efficient and Spatially Traceable Surgical MLLMs,” evidence record 16153, https://ethics.ai/record/16153 (originally published by arXiv cs.LG).
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