语言大模型 / LLM - arXivDaily 专题

2601.14268 2026-01-22 cs.CY cs.AI cs.CL 88%

Developmental trajectories of decision making and affective dynamics in large language models

大语言模型决策机制与情感动态的发展轨迹

Zhihao Wang, Yiyang Liu, Ting Wang, Zhiyuan Liu

专题命中领域大模型：large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

AI总结研究通过对比不同大语言模型与人类在赌博任务中的表现，揭示了模型决策和情感动态的发展轨迹及其对AI伦理的影响。

2601.06111 2026-01-22 cs.AI cs.CY 85%

LLM Powered Social Digital Twins: A Framework for Simulating Population Behavioral Response to Policy Interventions

由大型语言模型驱动的社会数字孪生：一种用于模拟人口对政策干预反应的框架

Fatima Koaik, Aayush Gupta, Farahan Raza Sheikh

机构 * PwC（普华永道）

专题命中领域大模型：LLM(title,abstract)；large language model(abstract)；language model(abstract)；分类 cs.AI

AI总结本文提出了一种由大型语言模型驱动的社会数字孪生框架，用于模拟人群对政策干预的反应，通过校准层实现对真实数据的验证，并在疫情期间展示了20.7%的预测误差改进。

Comments 13 pages, 1 figure, 4 tables

2505.15091 2026-01-22 cs.IR cs.AI 85%

ThinkRec: Thinking-based recommendation via LLM

ThinkRec: 基于思考的推荐方法

Qihang Yu, Kairui Fu, Zheqi Lv, Shengyu Zhang, Xinhui Wu, Chen Lin, Feng Wei, Bo Zheng, Fei Wu

机构 * Zhejiang University（浙江大学）； Shanghai AI Laboratory（上海人工智能实验室）

专题命中领域大模型：LLM(title,abstract)；large language model(abstract)；language model(abstract)；分类 cs.AI

AI总结 ThinkRec通过引入思考激活机制和实例级专家融合机制，提升推荐系统的准确性和可解释性。

Comments Published on WWW'26: In Proceedings of the ACM Web Conference 2026

2601.14936 2026-01-22 cs.SE 85%

LLM-Based Repair of C++ Implicit Data Loss Compiler Warnings: An Industrial Case Study

基于大型语言模型的C++隐式数据丢失编译警告修复：一个工业案例研究

Chansong You, Hyun Deok Choi, Jingun Hong

专题命中领域大模型：LLM(title,abstract)；large language model(abstract)；language model(abstract)

AI总结本文提出利用LLM修复C++隐式数据丢失警告的方法，通过工业案例验证其有效性，减少人工干预并提升代码质量。

2601.15062 2026-01-22 cs.SI 82%

Turning Citation Networks Inside Out: Studying Science Using Content-Based Knowledge Graphs from LLM-Derived Taxonomies

将引用网络翻转：利用LLM衍生的基于内容的知识图谱研究科学

Seorin Kim, Vincent Holst, Vincent Ginis

专题命中领域大模型：LLM(title)；large language model(abstract)；language model(abstract)

AI总结本文提出了一种基于内容的知识图谱方法，通过LLM衍生的分类学分析科学领域，揭示方法论、数据和问题的演变架构。

Comments 19 pages, 10 figures

2601.14560 2026-01-22 cs.CL 81%

Rewarding How Models Think Pedagogically: Integrating Pedagogical Reasoning and Thinking Rewards for LLMs in Education

奖励模型如何思考：在教育中整合教学推理和思考奖励

Unggi Lee, Jiyeong Bae, Jaehyeon Park, Haeun Park, Taejun Park, Younghoon Jeon, Sungmin Cho, Junbo Koh, Yeil Jeong, Gyeonggeon Lee

机构 * Chosun University（昌原大学）； Korea University（韩国大学）； Seoul National University（首尔国立大学）； Korea Institute for Curriculum and Evaluation（韩国课程与评价研究院）； Upstage ； Indiana University Bloomington（印第安纳大学布卢明顿分校）； Nanyang Technological University（南洋理工大学）

专题命中领域大模型：LLM(abstract)；large language model(abstract)；language model(abstract)；prompting(abstract)

AI总结本文提出PedagogicalRL-Thinking框架，通过教学推理提示和思考奖励方法，提升LLM在教育场景中的教学推理能力和结构化决策能力。

2504.16918 2026-01-22 cs.CL cs.AI 81%

OptimAI: Optimization from Natural Language Using LLM-Powered AI Agents

OptimAI：利用LLM驱动的AI代理进行自然语言优化

Raghav Thind, Youran Sun, Ling Liang, Haizhao Yang

机构 * Department of Computer Science University of Maryland at College Park（计算机科学系大学马里兰大学学院公园分校）； Department of Mathematics University of Maryland at College Park（数学系大学马里兰大学学院公园分校）； Department of Mathematics Department of Computer Science University of Maryland at College Park（数学系计算机科学系大学马里兰大学学院公园分校）

专题命中领域大模型：LLM(title,abstract)；分类 cs.CL、cs.AI

AI总结 OptimAI通过LLM驱动的AI代理解决自然语言描述的优化问题，实现88.1%的准确率，显著降低错误率。

2601.14827 2026-01-22 cs.AI 79%

Measuring and Aligning Abstraction in Vision-Language Models with Medical Taxonomies

利用医学分类法测量和对齐视觉-语言模型中的抽象能力

Ben Schaper, Maxime Di Folco, Bernhard Kainz, Julia A. Schnabel, Cosmin I. Bercea

机构 * 1 School of Computation, Information ； Technology, Technical University of Munich, Germany 2 Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany 3 LTCI, Télécom Paris, Institut Polytechnique de Paris, France 4 Munich Center for Machine Learning (MCML) 5 School of Biomedical Engineering ； Imaging Sciences, King's College London, UK 6 Department of Artificial Intelligence in Biomedical Imaging, FAU Erlangen-Nuremberg, Germany 7 Department of Computing, Imperial College London, UK

专题命中领域大模型：language model(title,abstract)；分类 cs.AI

AI总结本文提出通过医学分类法量化和缓解视觉-语言模型中的抽象错误，引入灾难性抽象错误概念，并通过风险约束阈值和分类法感知微调减少严重错误至2%以下。

2511.07301 2026-01-22 cs.CV cs.AI 79%

Beyond Boundaries: Leveraging Vision Foundation Models for Source-Free Object Detection

超越边界：利用视觉基础模型进行无源目标检测

Huizai Yao, Sicheng Zhao, Pengteng Li, Yi Cui, Shuo Lu, Weiyu Guo, Yunfan Lu, Yijie Xu, Hui Xiong

专题命中领域大模型：foundation model(title,abstract)；分类 cs.AI

AI总结本文提出一种利用视觉基础模型提升无源目标检测性能的新框架，通过增强特征对齐和标签质量，实现跨领域迁移和辨别性提升。

Comments Accepted to AAAI 2026. Extended version with full Appendix

2601.15153 2026-01-22 cs.AI 77%

How to Build AI Agents by Augmenting LLMs with Codified Human Expert Domain Knowledge? A Software Engineering Framework

如何通过将编码化的人类专家领域知识与大语言模型结合来构建AI代理？一种软件工程框架

Choro Ulan uulu, Mikhail Kulyabin, Iris Fuhrmann, Jan Joosten, Nuno Miguel Martins Pacheco, Filippos Petridis, Rebecca Johnson, Jan Bosch, Helena Holmström Olsson

机构 * Department of Computer Science and Engineering, Chalmers University of Technology（计算机科学与工程系，查尔姆斯理工大学）； Department of Mathematics and Computer Science, Eindhoven University of Technology（数学与计算机科学系，埃因霍温理工大学）； Department of Computer Science and Media Technology, Malmö University（计算机科学与媒体技术系，马尔默大学）

专题命中领域大模型：LLM(abstract)；large language model(abstract)；language model(abstract)；分类 cs.AI

AI总结本文提出一种软件工程框架，通过增强大语言模型与编码化专家知识，构建能自主生成可视化内容的AI代理，实现非专家在专业领域内达到专家水平的成果。

2601.11534 2026-01-22 cs.HC cs.AI 77%

Modular AI-Powered Interviewer with Dynamic Question Generation and Expertise Profiling

模块化的人工智能面试官：动态问题生成与专业能力分析

Aisvarya Adeseye, Jouni Isoaho, Seppo Virtanen, Mohammad Tahir

机构 * Department of Computing, University of Turku（计算系，图尔库大学）

专题命中领域大模型：LLM(abstract)；large language model(abstract)；language model(abstract)；分类 cs.AI

AI总结本文提出一种模块化的人工智能面试官，通过动态生成情境合适且专业对齐的问题，提升定性研究的灵活性和参与度。

Comments Accepted and Waiting to be published in conference AIR-RES'25 ( http://www.american-cse.org/air-res2025 )

2601.14265 2026-01-22 cs.CY cs.AI cs.CL 73%

From Textbook to Talkbot: A Case Study of a Greek-Language RAG-Based Chatbot in Higher Education

从教材到谈bot：面向高等教育的希腊语基于检索增强生成的聊天机器人案例研究

Maria Eleni Koutsiaki, Marina Delianidi, Chaido Mizeli, Konstantinos Diamantaras, Iraklis Grigoropoulos, Nikolaos Koutlianos

专题命中领域大模型：large language model(abstract)；language model(abstract)；分类 cs.CL、cs.AI

AI总结本研究开发了一个基于RAG框架的希腊语AI聊天机器人，用于高等教育中的教学支持，旨在提升教育实践和AI技术在语言教育中的应用。

Comments 11 pages, 5 figures, 6th Barcelona Conference on Education (BCE2025)

2601.14235 2026-01-22 astro-ph.IM astro-ph.CO cs.AI cs.LG stat.ML 73%

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

人工智能/机器学习在Rubin LSST暗能量科学合作中的机遇

LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz, Matthew R. Becker, Biswajit Biswas, Rahul Biswas, Boris Bolliet, Adam S. Bolton, Clecio R. Bom, Raphaël Bonnet-Guerrini, Alexandre Boucaud, Jean-Eric Campagne, Chihway Chang, Aleksandra Ćiprijanović, Johann Cohen-Tanugi, Michael W. Coughlin, John Franklin Crenshaw, Juan C. Cuevas-Tello, Juan de Vicente, Seth W. Digel, Steven Dillmann, Mariano Javier de León Dominguez Romero, Alex Drlica-Wagner, Sydney Erickson, Alexander T. Gagliano, Christos Georgiou, Aritra Ghosh, Matthew Grayling, Kirill A. Grishin, Alan Heavens, Lindsay R. House, Mustapha Ishak, Wassim Kabalan, Arun Kannawadi, François Lanusse, C. Danielle Leonard, Pierre-François Léget, Michelle Lochner, Yao-Yuan Mao, Peter Melchior, Grant Merz, Martin Millon, Anais Möller, Gautham Narayan, Yuuki Omori, Hiranya Peiris, Laurence Perreault-Levasseur, Andrés A. Plazas Malagón, Nesar Ramachandra, Benjamin Remy, Cécile Roucelle, Jaime Ruiz-Zapatero, Stefan Schuldt, Ignacio Sevilla-Noarbe, Ved G. Shah, Tjitske Starkenburg, Stephen Thorp, Laura Toribio San Cipriano, Tilman Tröster, Roberto Trotta, Padma Venkatraman, Amanda Wasserman, Tim White, Justine Zeghal, Tianqing Zhang, Yuanyuan Zhang

机构 * Université Paris Cité, CNRS, CEA, Astroparticule et Cosmologie, F-75013 Paris, France ； Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA ； Leinweber Institute of Theoretical Physics, University of Michigan, Ann Arbor, MI 48109, USA ； Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA ； Cavendish Astrophysics, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK ； Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK ； SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA ； Department of Computer Science, University of Milan, Milan, Italy ； Université Paris Cité, CNRS, Astroparticule et Cosmologie, F-75013 Paris, France ； Université Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France ； Department of Astronomy ； Astrophysics, University of Chicago, Chicago, IL 60637, USA ； Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA ； NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611, USA ； Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, USA ； Universit\'e Clermont-Auvergne, CNRS, LPCA, 63000 Clermont-Ferrand, France ； Kavli Institute for Particle Astrophysics ； Cosmology, Stanford University, Stanford, CA 94305, USA ； Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, CA 94305, USA ； Engineering Faculty, Universidad Autonoma de San Luis Potosi, Zona Universitaria, San Luis Potosi, 78290, Mexico ； Stanford Artificial Intelligence Laboratory, Stanford University, Stanford, CA 94305, USA ； Kavli Institute of Cosmological Physics, University of Chicago, Chicago, IL 60637, USA ； The NSF AI Institute for Artificial Intelligence ； Center for Astrophysics Harvard \& Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA ； Department of Physics ； Kavli Institute for Astrophysics ； Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA ； Institut de Física d'Altes Energies (IFAE), The Barcelona Institute of Science ； Institute of Astronomy ； Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK ； Imperial Centre for Inference ； Cosmology (ICIC), Imperial College London, Blackett Laboratory, Prince Consort Road, London SW7 2AZ, UK ； Data Science Institute, The University of Chicago, Chicago, IL 60615, USA ； Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA ； Department of Physics, Duke University, Durham, NC 27708, USA ； Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM, F-91191 Gif-sur-Yvette, France ； School of Mathematics, Statistics ； Physics, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom ； Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA ； Astronomy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa ； Astronomy, University of Utah, Salt Lake City, UT 84112, USA ； Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544, USA ； Department of Astronomy, University of Illinois Urbana Champaign, 1002 W. Green St., Urbana, IL, 61801, USA ； Institute for Particle Physics ； Astrophysics, ETH Zürich, Wolfgang-Pauli-Strasse 27, CH-8093 Zurich, Switzerland ； Swinburne University of Technology, Hawthorn, Victoria 3122, Australia ； Ciela - Montr\'eal Institute for Astrophysical Data Analysis ； Mila - Quebec Artificial Intelligence Institute, Montréal, QC H2S 3H1, Canada ； Advanced Research Computing Centre, University College London, 90 High Holborn, London WC1V 6LJ, UK ； Finnish Centre for Astronomy with ESO (FINCA), University of Turku, FI-20014 Turku, Finland ； Department of Physics, P.O. Box 64, University of Helsinki, FI-00014 Helsinki, Finland ； Astronomy, Northwestern University, Evanston, IL, USA ； Center for Interdisciplinary Exploration ； Research in Astrophysics, Northwestern University, Evanston, IL, USA ； Scientific Data Science, International School for Advanced Study, Via Bonomea 265, I-34136 Trieste, Italy ； Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA ； PITT PACC, University of Pittsburgh, Pittsburgh, PA 15260, USA ； NSF NOIRLab, 950 N. Cherry Ave., Tucson, AZ 85719, USA

专题命中领域大模型：LLM(abstract)；foundation model(abstract)；分类 cs.AI、cs.LG

AI总结本文探讨了AI/ML在LSST暗能量科学合作中的应用机遇，强调了大规模贝叶斯推断、物理指导方法和主动学习等关键方法学优先事项，并讨论了新兴技术在重塑工作流程中的潜力。

Comments 84 pages. This is v1.0 of the DESC's white paper on AI/ML, a collaboration document that is being made public but which is not planned for submission to a journal

2601.15182 2026-01-22 cs.CL cs.IR 70%

Supporting Humans in Evaluating AI Summaries of Legal Depositions

支持人类评估AI对法律证词的摘要

Naghmeh Farzi, Laura Dietz, Dave D. Lewis

机构 * University of New Hampshire（新罕布什尔大学）

专题命中领域大模型：large language model(abstract)；language model(abstract)；分类 cs.CL

AI总结本研究提出了一种基于 nugget 的方法，帮助法律专业人士评估和改进AI生成的法律证词摘要。

Comments To appear in 2026 ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR '26), March 22-26, 2026, Seattle, WA, USA. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3786304.3787923

2601.14641 2026-01-22 cs.HC 67%

MIND: Empowering Mental Health Clinicians with Multimodal Data Insights through a Narrative Dashboard

MIND：通过叙述仪表板赋能心理健康临床医生的多模态数据洞察

Ruishi Zou, Shiyu Xu, Margaret E Morris, Jihan Ryu, Timothy D. Becker, Nicholas Allen, Anne Marie Albano, Randy Auerbach, Dan Adler, Varun Mishra, Lace Padilla, Dakuo Wang, Ryan Sultan, Xuhai "Orson" Xu

专题命中领域大模型：large language model(abstract)；language model(abstract)

AI总结 MIND通过叙述仪表板为心理健康临床医生提供多模态数据洞察，提升临床决策支持和数据洞察发现能力。

Comments Conditionally accepted to CHI Conference on Human Factors in Computing Systems (CHI'26)

2503.18085 2026-01-22 cs.CL cs.AI cs.LG 67%

Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach

临床文本中的时间关系抽取：一种基于跨度的图变换器方法

Rochana Chaturvedi, Peyman Baghershahi, Sourav Medya, Barbara Di Eugenio

专题命中领域大模型：language model(abstract)；分类 cs.CL、cs.AI、cs.LG

AI总结本文提出GRAPHTREX方法，通过结合基于跨度的实体关系抽取、临床预训练语言模型和异构图变换器，提升临床文本中时间关系抽取的准确率和长距离关系识别能力。

Comments Introducing a novel method for joint extraction of medical events and temporal relations from free-text, leveraging clinical LPLMs and Heterogeneous Graph Transformers, achieving a 5.5% improvement over the previous state-of-the-art and up to 8.9% on long-range relations

Journal ref Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

2601.15239 2026-01-22 stat.ML cs.LG math.ST stat.TH 57%

Multi-context principal component analysis

多情境主成分分析

Kexin Wang, Salil Bhate, João M. Pereira, Joe Kileel, Matylda Figlerowicz, Anna Seigal

机构 * Harvard University（哈佛大学）； Broad Institute of MIT and Harvard（哈佛-麻省理工Broad研究所）； University of Georgia（佐治亚大学）； University of Texas at Austin（德克萨斯大学奥斯汀分校）

专题命中领域大模型：language model(abstract)；分类 cs.LG

AI总结多情境主成分分析（MCPCA）是一种理论和算法框架，用于识别跨不同情境子集共享的变异因素，应用于基因表达和语言模型数据，揭示隐藏的变异轴。

Comments 47 pages, 8 figures. Supplementary tables are provided as downloadable file

2601.15172 2026-01-22 cs.CL 57%

Is Peer Review Really in Decline? Analyzing Review Quality across Venues and Time

同行评审真的在下降吗？跨会议和时间分析评审质量

Ilia Kuznetsov, Rohan Nayak, Alla Rozovskaya, Iryna Gurevych

机构 * Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science, Technical University of Darmstadt and National Research Center for Applied Cybersecurity ATHENE（技术大学达姆施塔特计算机科学系和国家应用网络安全研究中心ATHENE）； Department of Computer Science at Queens College, City University of New York (CUNY)（纽约城市大学皇后学院计算机科学系）

专题命中领域大模型：LLM(abstract)；分类 cs.CL

AI总结本文通过分析ICLR、NeurIPS和ACL等会议的评审质量，发现评审质量并未持续下降，提出了基于证据的比较研究框架和标准化方法。

2601.14774 2026-01-22 cs.CV 50%

Does medical specialization of VLMs enhance discriminative power?: A comprehensive investigation through feature distribution analysis

医学专业性是否增强VLMs的判别能力？：通过特征分布分析的全面研究

Keita Takeda, Tomoya Sakai

机构 * Graduate School of Integrated Science and Technology（整合科学与技术研究生院）； Nagasaki University（长崎大学）

专题命中领域大模型：language model(abstract)

AI总结本研究通过特征分布分析，探讨医学专业性对VLMs判别能力的影响，发现增强文本编码器比大量医学图像训练更关键，且非医学模型易受图像文本偏见影响。

Comments A short version paper of this research has been accepted for The IEEE International Symposium on Biomedical Imaging (ISBI) 2026

1. 领域大模型 19 篇