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small language model

3,366 papers

#small language model Preprint Aug 2026

SHIFT-LLM: Distribution Shift Correction in Depth-Pruned LLMs

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
#small language model Open access Aug 2026

Estimated Prevalence of Developmental Language Disorder Risk in West Virginia Schools.

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,...

Megan Israelsen-Augenstein, Michelle Moore, Tracy Toman et al. · 0 citations
#small language model Open access Jul 2026

Bolt-on, Verifiable Provenance for LLM-Powered Data Processing

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 · 0 citations
#small language model Preprint Aug 2026

Query Expansion Is More Than Generation: Improving Dense Retrieval through Better Integration

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
#small language model Preprint Aug 2026

SMART: MLLM-guided Temporal Alignment for Unifying Sign Language Recognition and Spotting

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
#small language model Review Open access Aug 2026

Challenges and opportunities in type III secretion system effector prediction.

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 · 0 citations
#small language model Preprint Aug 2026

Not All Attention Heads Contribute to Critical Visual Token Selection: Head-Aware Pruning Matters More

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
#small language model Preprint Aug 2026

Short Horizons and Sparse Concepts: a Mathematical View of the Readout in the J-lens

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
#protein folding Review Open access Aug 2026

Emerging experimental and computational methods for studying redox-regulated structural transitions.

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 · 0 citations
#small language model Open access Aug 2026

CARE: conflict-aware regulation and evolution for continual test-time open-vocabulary semantic segmentation

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 · 0 citations
#small language model Open access Aug 2026

What students ask matters: LLM interaction depth, task quality, and immediate recall in higher education

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 · 0 citations
#small language model Preprint Aug 2026

PhysMLLMs: Spatial Priors for Unified Referring Segmentation and Grounded Reasoning of Images and Videos

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

From tech blogs

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MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

Microsoft Research Blog Oct 22, 2026

Flint-interactive: A language for creating interactive data visualizations

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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