Layer-wise Positional Bias in Short-Context Language Modeling
Tehran Institute for Advanced Studies, Khatam University, Iran（德黑兰高级研究院，卡坦大学，伊朗）；School of Electrical and Computer Engineering, University of Tehran, Iran（德黑兰理工大学电气与计算机工程学院，伊朗）
Transformer language models systematically prefer tokens at specific input positions regardless of semantic relevance---a phenomenon known as positional bias. Prior work characterizes this bias in model behavior through performance drops in long-context tasks or in model architecture through attention-based analyses. However, it remains unmeasured how input positions actually drive predictions layer by layer. We introduce a layer conductance framework within a sliding-window design, applied to short-context next-word prediction to isolate model-internal behavior from task and context-window pressure. The resulting layer-wise positional importance profiles are stable across diverse texts and lexical scrambling, confirming they reflect model-internal structure. Characterizing how these profiles evolve across depth, we find recency bias increases monotonically while primacy bias is subtle and diminishes. We also find that this positional bias is not uniform across word types: function words exhibit higher recency bias while content words show higher primacy bias.
HSE University（俄罗斯国立高等经济大学）；Lomonosov Moscow State University（莫斯科国立大学）；Harbin Institute of Technology（哈尔滨工业大学）；ISP RAS（俄罗斯科学院系统编程研究所）；RANEPA（俄罗斯总统国民经济与公共管理学院）；ITMO University（圣彼得堡国立信息技术机械与光学大学）；Skoltech（斯科尔科沃科学技术研究院）
A Systematic Evaluation of Positional Bias in Multi-Video Summarization with MLLMs
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Jilin University（知识驱动人机智能工程研究中心）；International Center of Future Science, Jilin University（未来科学国际中心）
Mitigating Coordinate Prediction Bias from Positional Encoding Failures
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；University of California, Los Angeles（加州大学洛杉矶分校）；The University of Queensland（昆士兰大学）；Shanghai University of Finance and Economics（上海财经大学）
PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training
Microsoft Corporation（微软公司）；School of Computer Science, Peking University（北京大学计算机学院）；National Key Laboratory for Multimedia Information Processing, Peking University（北京大学多媒体信息处理国家重点实验室）
Are LLMs Positionally Consistent Ordinal Classifiers? A Systematic Evaluation
Cornell University（康奈尔大学）；University of Washington（华盛顿大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of California, Davis（加州大学戴维斯分校）
2D-TPE: Two-Dimensional Positional Encoding Enhances Table Understanding for Large Language Models
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Ant Group（蚂蚁集团）；Kunming University of Science and Technology（昆明理工大学）
Bias in the Ear of the Listener: Assessing Sensitivity in Audio Language Models Across Linguistic, Demographic, and Positional Variations
Department of Computer Science and Information Engineering, National Taiwan University, Taiwan（国家台湾大学计算机科学与信息工程系）；Institute of Information Science, Academia Sinica, Taiwan（台湾学术院信息科学研究所）；AI Research Center (AINTU), National Taiwan University, Taiwan（国家台湾大学人工智能研究中心（AINTU））
Listening to the Wise Few: Query-Key Alignment Unlocks Latent Correct Answers in Large Language Models
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；AI Foundation and Algorithm Lab（人工智能基金会与算法实验室）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Artificial Intelligence Research Institute (AIRI)（人工智能研究院）；CNRS（法国国家科学研究中心）；Université Paris Cité（巴黎西岱大学）
Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval
Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）；Instituto Superior Técnico and INESC-ID, University of Lisbon（里斯本大学高级技术学院与INESC-ID研究所）；NOVA LINCS, NOVA School of Science and Technology（NOVA科技大学NOVA LINCS研究所）
