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3,629 papers
BLIP is presented, a bolt-on framework for efficiently inferring a small-sized verifiable provenance for any LLM-powered data processing task, with any LLM, and eight strategies, each guaranteed to find a minimal verifiable provenance are introduced.
This work introduces AnchorQE, a training-free method that separately encodes the original query and its expansion before interpolating them, and shows that AnchorQE improves retrieval effectiveness by up to 12.89% when compared to widely-used expansion-only or text-level concatenation baselines across TREC-DL, LoTTE,...
This work proposes SMART, an MLLM-guided temporal alignment framework for joint sign recognition and spotting that incorporates CSFormer, a CSLR-guided spotting module that injects recognition-derived gloss evidence into a boundary-aware spotting network.
The conceptual evolution of T3SE prediction is reviewed, persistent limitations and sources of bias are highlighted, and open questions that must be addressed are outlined to enable robust, interpretable and ecologically inclusive prediction of T3SEs, pointing towards the need for centralized, user-friendly platforms t...
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ProViP is proposed, a training-free progressive visual token pruning framework that removes redundant visual tokens based on the embedding similarity of input tokens before reasoning of the LLM backbone, and then prunes tokens during reasoning via head-aware pruning.
The Jacobian lens (J-lens) has been proposed as a way to read verbalizable representations from language models. However, its principle and meaning lack a detailed and theoretical discussion. We provide a mathematical view of this interpretation and of its assumed causal structure. Besides treating the J-lens as a heur...
A comparative overview of dual experimental and computational advancements is provided and how the integration of generative diffusion models could facilitate the real-time simulation of conditional, multi-state structural ensembles across the redox proteome is highlighted.
A continual test-time adaptation framework that updates only a small subset of model parameters that consistently outperforms existing TTA and OVSS adaptation baselines across multiple datasets and corruptions, while maintaining stable performance over extended and cross-corruption continual streams without introducing...
The findings indicate a dissociation between performance quality and short-term recall in LLM-supported study, which aligns with cognitive-psychology evidence that elaboration improves comprehension while retrieval practice consolidates retention.
PhysMLLMs is a training-stage prior injection architecture that injects physics-inspired spatial continuity priors into Video MLLMs, demonstrating that the injected spatial prior improves video consistency without compromising image-level grounding or general multimodal capability.
Apodex 1.1 reaches the leading performance band despite using a substantially smaller model than many frontier systems, and the 35B-parameter Apodex 1.1 Mini further retains strong working capability in a locally deployable form.
MGQL is presented, the first mechanized, small-step operational semantics for a substantial read-only fragment of GQL that is grounded in the ISO/IEC 39075 standard, and it is proved that the type system is sound, ensuring an end-to-end guarantee of well-formed queries yielding results that conform to their declared sc...
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