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PrefLUT is introduced, a reusable and refinable user-preference modeling framework for deployable 3D LUTs, encoding ordered preferred/non-preferred image pairs into a lightweight Reusable User Profile that is reused across queries and refined using additional user preference pairs, without per-user optimization.
This work studies unpredictable near-optimal policies (UNOP), which mix uniformly on near-best actions that remain individually rational and which can be learned and the optimizer can match its best response, while the user's payoff stays higher because the mixture changes which action is targeted.
This work proposes SciFigQual-Bench, a full-text contextual benchmark that evaluates scientific image across five dimensions (clarity, layout,captionfit, contextrelevance, andmisleadingrisk), and develops SFQ-Agent to achieve auditable and refined scoring through the collection and fusion of modal evidence.
SciFigAlign is proposed, a fine-tuned multimodal scorer that grounds figure quality assessment in manuscript evidence and confirms that manuscript-grounded inputs, citing-context denoising, and ranking supervision are all critical, showing that scientific figure assessment requires learned alignment between visual cont...
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