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small language model

3,317 papers

#artificial intelligence Preprint Oct 2026

SFT-as-Context Mitigates Forgetting in Supervised Fine-Tuning

Supervised fine-tuning (SFT) equips large language models (LLMs) with specialized capabilities, but often comes at the cost of forgetting the general capabilities of their parent models (i.e., the pretrained models before fine-tuning). This trade-off is especially limiting for queries that require both specialized and...

Ke-Nan Tang, An-Dong Hua, Cheng-Xuan Qian et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models Outperform Prompted Frontier Models

Corrective feedback is among the best-evidenced drivers of second-language acquisition, yet corrections delivered during lessons rarely accumulate into an actionable view of grammar mastery. Prompted frontier models can provide such a view from learner--tutor lesson transcripts, but they are costly at scale. We close t...

Marjan Celikik, A. Ramallo, Javier Morales · 0 citations
#artificial intelligence Preprint Oct 2026

Looking Inside LLMs: Small-World Connectivity as a Signature of Reasoning Performance

Understanding large language model (LLM) reasoning requires looking beyond behavioral performance to examine how reasoning ability is reflected in internal organization. Inspired by neuroscience findings linking higher intelligence to stronger small-world organization in functional brain networks, we investigate small-...

Zheng Huang, Sansheng Cao, En-Pei Zhang et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Structure Tax: How Structured Output affects LLMs Performance

Deploying large language models in production often requires constraining outputs to structured formats such as JSON or XML, and prior work treats the resulting accuracy loss as an inherent `structure tax'. We re-examine this claim by evaluating a battery of models, datasets and schemas, measuring task accuracy, confid...

Vineet Kumar, Kanishka, Bhuvanesh Mandora · 0 citations
#artificial intelligence Preprint Oct 2026

Internalizer: Portable Context-to-Parameter Mapping for Very Large Language Models

Hypernetworks that map a context directly to a LoRA adapter let a large language model carry that context in its weights, but prior work has demonstrated them only on base models of up to 14 billion parameters. We present the Internalizer, a state-of-the-art, portable Context-to-Parameter Mapping hypernetwork that gene...

Peter Devine, Nick Ryan, Benjamin Sirb et al. · 0 citations
#artificial intelligence Preprint Oct 2026

From Chain-of-Thought to Loops: Non-Autoregressive Latent Reasoning via Looped Transformers

Chain-of-thought (CoT) reasoning often improves language-model performance by giving models additional computation before answering. However, explicit CoT expresses this computation as a sequence of autoregressively generated tokens. Latent reasoning replaces these tokens with compact continuous states, but most autore...

Gerard Grau García, Arnau Padrés Masdemont, Niccolò Grillo et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Harness Compilation: Which Decisions Should a Small Vision-Language Model Keep?

Small vision-language models may be able to read external evidence yet struggle to obtain it. We introduce Harness Compilation (HC), an offline procedure that adapts the division of work between a frozen small VLM and its external harness. A large teacher uses student execution traces to revise reusable content and con...

Min-Hao Fan, Yin Liu, Jiayu Zhao et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Learning How to Search for Plans with Exponentially Less Space

Heuristic search for a plan can store exponentially many states, even when its heuristic is almost perfect. We instead learn search control, one specification per domain, written as an indexical policy: a generalized policy with registers that hold objects and modes that sequence its rules. We add the choose rule, whic...

Dominik Drexler, Simon Ståhlberg, M. Fritzsche et al. · 0 citations
#small language model Review Open access Oct 2026

Doctors experiences of interruptions in UK secondary care outpatient clinics: a cross-sectional survey of prevalence, consequences and management strategies

Objectives To examine the prevalence of interruptions during UK secondary care outpatient consultations, their association with clinician mental workload, and the strategies clinicians use to manage them. Design Nationwide cross-sectional online survey. Setting Secondary care outpatient clinics, United Kingdom. Partici...

S. Boustani, C. Staras, A. Rich et al. · 0 citations
#small language model Review Open access Oct 2026

Integrating Regenerative Medicine and Stem Cell Therapy in Hearing Restoration: A Systematic Review

Hearing loss can adversely affect communication and language development and is associated with cognitive decline.Current treatments, including hearing aids and cochlear implants, can improve auditory function but do not regenerate damaged sensory cells.Stem-cell-based approaches have therefore attracted increasing int...

Isa D Hussain, Haidar Tanveer, Mohammad Asif Hussain et al. · 0 citations
#small language model Open access Oct 2026

Making goals visible: A feasibility study of co-designed visual goal sheets to support parent implementation in early autism intervention

Background Translating individualized intervention goals into everyday family routines can be challenging. This mixed-methods feasibility study examined the use/adoption and parent-reported acceptability/social validity of visual goal sheets embedded within routine Early Start Denver Model (ESDM) care. Methods Ten fami...

Nathalie Lavenne-Collot, Camille Mallégol, Clarisse Bourreau 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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