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large language models

2,172 papers

#large language models Open access Sep 2026

LLMs can assist with proposal selection at large user facilities

Abstract We explore how large language models (LLMs) can enhance the proposal selection process at large user facilities, offering a scalable, consistent, and cost-effective alternative to traditional human review. Proposal selection depends on assessing the relative strength among submitted proposals; however, traditi...

Lijie Ding, Janell Thomson, Jon Taylor et al. · 0 citations
#large language models Open access Sep 2026

LEME: open large language models for ophthalmology with advanced reasoning and clinical validation

The rising prevalence of eye diseases poses a growing public health burden. Large language models (LLMs) offer a promising path to reduce documentation workload and support clinical decision-making. However, few have been tailored for ophthalmology, and most evaluations focus mainly on knowledge-based QA without clinic...

Hyunjae Kim, X. C. Ai, Sahana Srinivasan et al. · 1 citation
#large language models Dataset Open access Sep 2026

Malva

Malva Malva is an artifact for studying tool-mediated interaction failures in agentic GUI software. It contains a defect library built from 40 open-source applications, a task suite for reproducing representative failures, and MalvaGuard, a static checker that detects missing tool-interaction guards before agent execut...

Anonymous Author(s) · 0 citations
#large language models Open access Sep 2026

MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair

Abstract Large Language Models (LLMs) have shown high capabilities in several software development-related tasks such as program repair, documentation, code refactoring, debugging, and testing. However, training these models requires massive amount of data and significant computational resources. Adapters are specializ...

Meghdad Dehghan, Jie JW Wu, Fatemeh H. Fard et al. · 1 citation
#large language models Open access Sep 2026

Language and cultural prompts influence large language models' responses to culturally anchored mental health attitudes

Objective This study experimentally tested whether language and cultural prompts would influence large language models' (LLMs) assessments and recommendations when presented with the same mental health description. Methods We conducted two experiments employing a 2 (language prompting: Chinese vs. English) x 2 (cultura...

Mian-Qian Jia, Flora Yat Nga Lam, Xuan-Qin Wang · 0 citations
#large language models Open access Sep 2026

Universal Fractal Natural Language Decision Map: Real-Time Edge Triage Across Heterogeneous Domains

Abstract: Modern automated computing systems increasingly deploy Large Language Models (LLMs) to resolve runtime operational triage, incurring prohibitive latency (>100–500 ms), severe memory allocation (>4–8 GB VRAM), and high thermodynamic dissipation. Extending the foundational theory of Mandelbrot Fractal Neural Sy...

Volkan Dağlı, Zerrin Dağlı, Daghan Dagli · 2 citations
#large language models Open access Sep 2026

Colon-Bench: An Agentic Workflow for Scalable Dense Lesion Annotation in Full-Procedure Colonoscopy Videos

Early screening via colonoscopy is critical for colon cancer prevention, yet developing robust AI systems for this domain is hindered by the lack of densely annotated, long-sequence video datasets. Existing datasets predominantly focus on single-class polyp detection and lack the rich spatial, temporal, and linguistic...

Abdullah Hamdi, Changchun Yang, Xin Gao · 0 citations
#artificial intelligence Open access Sep 2026

The Plausibility Trap: Using Probabilistic Engines for Deterministic Tasks

The ubiquity of Large Language Models (LLMs) is driving a paradigm shift where user convenience supersedes computational efficiency. This article defines the "Plausibility Trap": a phenomenon where individuals with access to Artificial Intelligence (AI) models deploy expensive probabilistic engines for simple determini...

Iván Carrera, Daniel Maldonado-Ruiz · 0 citations

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Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

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