SHIFT-LLM, a training-free post-pruning correction framework that inserts a Linear Residual Adapter at each pruning site, consistently recovers accuracy lost to depth pruning across most configurations, achieving gains up to +15.7 points on Llama-3.1-8B-Instruct.
Ali Bahri, Hang Li, Hongliang Li et al.· 0 citations
PURPOSE
Developmental language disorder (DLD) is linked to long-term academic and social difficulties. Despite these lasting impacts, state-level prevalence estimates are limited. This study estimated prevalence among early-elementary students in West Virginia who may be at risk for DLD and examined variation by grade,...
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.
Yi-Ming Lin, Sepanta Zeighami, Aditya G. Parameswaran· Proceedings of the VLDB Endo...· 0 citations
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,...
Sixia Sun, Mihai Surdeanu· 1 citation
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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.
Eunjee Choi, J. Sung, Seongwhan Cho et al.· 0 citations
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...
Iva Rosić, Ivan Nikolić· Open Biology· 0 citations
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.
Chao-Fang Ma, Lin Jiang, Carol Jingyi Li et al.· 1 citation
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...
Shi-Qi Yan, Kai-Xuan Ding, Chao-Hong Tan et al.· 0 citations
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.
T. Rass, Dana Reichmann, Gábor Erdős· FEBS Letters· 0 citations
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...
Fen Luo, Sen Li, Zhanqiang Huo· Journal of King Saud Univers...· 0 citations
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.
V. Tsiligkiris· International Journal of Edu...· 0 citations
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.
Siyao Yan, Bo Han, Ji-Sheng Dang et al.· 0 citations
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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