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This work introduces a unified conceptual framework that views discrete diffusion models through the construction of the underlying discrete state space, and exposes common design trade-offs across training objectives, inference algorithms, scaling behavior, systems optimization, and evaluation protocols.
Evaluating three frontier VLMs in both homogeneous and cross-model adversarial settings, it is found that even the strongest agent hallucinates 15.1% of its verifiable spatial claims and 11.5% of accusations are strictly unsupported.
CARE is proposed: a multi-stage privacy-compliant agentic reasoning framework in which a proprietary LLM provides guidance by generating structured categories and transitions without accessing sensitive patient data, while a local LLM uses these categories and transitions to support evidence acquisition and final decision-making.
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