This study investigates the adoption of open-access, locally deployable causal large language models (LLMs) for travel mode choice prediction and introduces LiTransMC, the first fine-tuned causal LLM developed for this task. We systematically benchmark eleven open-access LLMs (1-12B parameters) across three stated and revealed preference datasets, testing 396 configurations and generating over 79,000 mode choice decisions. Beyond predictive accuracy, we evaluate models generated reasoning using BERTopic for topic modelling and a novel Explanation Strength Index, providing the first structured analysis of how LLMs articulate decision factors in alignment with behavioural theory. LiTransMC, fine-tuned using parameter efficient and loss masking strategy, achieved a weighted F1 score of 0.6845 and a Jensen-Shannon Divergence of 0.000245, surpassing both untuned local models and larger proprietary systems, including GPT-4o with advanced persona inference and embedding-based loading, while also outperforming classical mode choice methods such as discrete choice models and machine learning classifiers for the same dataset. This dual improvement, i.e., high instant-level accuracy and near-perfect distributional calibration, demonstrates the feasibility of creating specialist, locally deployable LLMs that integrate prediction and interpretability. Through combining structured behavioural prediction with natural language reasoning, this work unlocks the potential for conversational, multi-task transport models capable of supporting agent-based simulations, policy testing, and behavioural insight generation. These findings establish a pathway for transforming general purpose LLMs into specialized and explainable tools for transportation research and policy formulation, while maintaining privacy, reducing cost, and broadening access through local deployment.
CHECKWHY: Causal Fact Verification via Argument Structure
Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院）山东省计算机科学中心）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
Causal Reasoning Favors Encoders: On The Limits of Decoder-Only Models
SIRE, IIT Delhi（IIT德里SIRE）；Robert Bosch GmbH, India（印度罗伯特博世集团）；IIIT Hyderabad（海得拉巴IIIT）；IISER Kolkata（科钦IISER）；Microsoft and the University of York（微软和约克大学）
Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia University（哥伦比亚大学）；RWTH Aachen（亚琛工业大学）；University of Michigan（密歇根大学）；ETH Zürich（苏黎世联邦理工学院）；Max Planck Institute（马克斯·普朗克研究所）
Patches of Nonlinearity: Instruction Vectors in Large Language Models
Ubiquitous Knowledge Processing Lab (UKP Lab)（通用知识处理实验室）；Department of Computer Science, Technical University of Darmstadt and National Research Center for Applied Cybersecurity ATHENE, Germany（计算机科学系，达姆施塔特技术大学和应用网络安全国家研究中心ATHENE，德国）
A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy
Linear Separability of Activation Representations after Supervised Fine-Tuning on Incorrect Responses: A Study of Synthetic Dishonesty in Large Language Models
Algoverse AI Research；Medical Sciences Education Research Center, Mashhad University of Medical Sciences（马什哈德大学医学科学教育研究中心）；Student Research Committee, Department of Health Information Technology and Management, Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences（谢赫·贝赫什提大学医学科学学院学生研究委员会，健康信息科技与管理系，医学信息学）
WhisperRT -- Turning Whisper into a Causal Streaming Model
Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering, Technion–Israel Institute of Technology（以色列理工学院安德鲁与厄娜·维特比电气与计算机工程学院）；Language Technologies Institute, School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院语言技术研究所）
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
Idiap Research Institute（Idiap研究所）；Universidad Nacional de San Luis (UNSL)（圣路易斯国立大学）；Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Universidad Autónoma Metropolitana Unidad Cuajimalpa（墨西哥都会自治大学夸伊马尔帕分校）；Brno University of Technology（布尔诺理工大学）
