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small language model

2,885 papers

#small language model Review Open access Sep 2026

Systematic review and meta-analysis of the effects of combined physical and cognitive training on depression symptoms in adults across clinical populations

Objectives To compare the effect of combined physical training (PT) and cognitive training (CT) with single-modality interventions, on depression symptoms in adults across clinical populations. Methods PubMed, Scopus, Web of Science, SPORTDiscus, and APA PsycINFO were systematically searched for relevant English-langua...

Piesie A. G. Asuako, Ghazal Naser, Luis K H Holzhauer et al. · 0 citations
#generative ai Review Open access Sep 2026

ChatGPT and Generative AI in Physical Education: Applications, Benefits, Limitations, and Risks for Teachers, Students, Coaches, and Professionals

The article proposes the Human-Supervised Generative AI Framework for Physical Education and Sport, which classifies tasks by consequence and requires source grounding, data minimization, professional review, disclosure, and outcome monitoring.

Kalyd Pierry Ferreira Gonçalves · 0 citations
#small language model Dataset Open access Sep 2026

NepEduBench Dataset

NepEduBench, an initial benchmark foropen-source Small Language Models (SLMs) covering English, Nepali, and Romanized Nepali. Built from Nepal’s official Grade 6–10 Social Studies textbooks, it combines a parallel core of 318 lessons per language (the same lessons in all three languages), language-specific instruction...

Inisha Pradhan, Chutiporn Anutariya, SEIN MINN et al. · 0 citations
#large language models Open access Sep 2026

Does a Model Forget Differently When the Data Is Its Own? RL's Retention Advantage and Model Collapse Are Claims About the Same Loop, and No Study Has Measured Both

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Two literatures make claims about what happens when a language model trains on data that resembles its own distribution. One asks whether reinforcement learning forgets a model's prior capabilities less than supervised fine-tuning does, and answers yes: on-policy R...

Pranay M. Mahendrakar · 0 citations
#large language models Open access Sep 2026

Does a Model Forget Differently When the Data Is Its Own? RL's Retention Advantage and Model Collapse Are Claims About the Same Loop, and No Study Has Measured Both

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Two literatures make claims about what happens when a language model trains on data that resembles its own distribution. One asks whether reinforcement learning forgets a model's prior capabilities less than supervised fine-tuning does, and answers yes: on-policy R...

Pranay M. Mahendrakar · 0 citations
#large language models Open access Sep 2026

PrefixAgent: An LLM-Powered Design Framework for Efficient Prefix Adder Optimization

Prefix adders are fundamental arithmetic circuits, but their design space grows exponentially with bit-width, posing significant optimization challenges. Previous works face limitations in performance, generalization, and scalability. To address these challenges, we propose PrefixAgent , a large language model (LLM)-po...

Dongsheng Zuo, Jiadong Zhu, Yang Luo et al. · 0 citations
#large language models Open access Sep 2026

An evidence-weighted knowledge framework with path-aware reasoning for personalized lifestyle management in chronic disease

Background Healthy lifestyle management is essential for chronic disease prevention and long-term care. However, evidence linking lifestyle behaviors, biological mechanisms, and health outcomes is dispersed across diverse biomedical sources. This fragmentation makes it difficult for health professionals to efficiently...

Hong-Yu Kang, Xiao-Wei Xu, Ning Xiao et al. · 0 citations
#small language model Preprint Sep 2026

Pretraining and adapting a language model on a dependency-free stack: GPT-2 124M from random weights, reproduced against llm.c, and a clinical adapter for Qwen3-0.6B

Almost every language model in service was trained by one family of software. That concentration makes a question hard to settle: how much of what is known about training a language model describes language models, and how much describes that software? Settling it needs a second implementation able to carry a model thr...

Thang Tran, Lan Dang · 0 citations
#small language model Preprint Sep 2026

Seeing Is Not Measuring: Tool-Augmented Metric Spatial Reasoning for Vision-Language Models

A modular, predictor agnostic, tool-augmented framework that equips a small VLM with geometric tools: 3D object detection, metric depth estimation, and deterministic solvers for distance, size and bearing, matching a scripted pipeline on three of four tasks.

Kai Glantz, Clemens Grange · 0 citations
#natural language process... Preprint Sep 2026

CodeGraph: Open-Taxonomy Knowledge Graph for Source Code with Wikidata Grounding

Public software repositories, like GitHub and Software Heritage Archive, store billions of files, yet extracting their implicit engineering knowledge ---i.e., the algorithms they implement, the paradigms they follow, the patterns they instantiate, and the application domains they serve--- remains challenging, as curren...

Federico Pennino, Andrea Gurioli, Stefano Zacchiroli et al. · 0 citations
#computer vision Preprint Sep 2026

Small yet Assistive: Spatially-Aware Post-Training for Low Vision

Results show that BLV-focused post-training improves both accessibility-specific spatial grounding and general visual-text reasoning, and is substantially outperforms the baseline across various benchmarks.

Rishabh C. Choudhary, S. Raj, Umesh Goyal et al. · 0 citations

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