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Computer Science

arXiv preprints from January 1, 2026 through September 10, 2026 — 02:32:26 EST

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Posted in cs.HC · 2026-01-16 · Yao Lyu, Jessica Shen, Alina Faisal, John M. Carroll

"I'm Constantly Getting Comments Like, 'Oh, You're Blind. You're Like the Only Woman That I Stand a Chance With.'": A Study of Blind TikTokers' Intersectional Experiences of Gender and Sexuality

Social media platforms are important venues for identity expression, and the Human-Computer Interaction community has been paying growing attention to how marginalized groups express their identities on these platforms. Joining the emerging literature on intersectional experiences, we study blind TikTokers ("BlindTokers") who are also...

💬 0 commentsarXiv:2601.10957v1PDF
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Posted in cs.HC · 2026-01-16 · Yao Lyu, Tawanna Dillahunt, Jiaying Liu, John M. Carroll

"My Brother Is a School Principal, Earns About $80,000 Per Year... But When the Kids See Me, 'Wow, Uncle, You Have 1500 Followers on TikTok!'": A Study of Blind TikTokers' Alternative Professional Development Experiences

One's profession is an essential part of modern life. Traditionally, professional development has been criticized for excluding people with disabilities. People with visual impairments, for example, face disproportionately low employment rates, highlighting persistent gaps in professional opportunities. Recently, there has been...

💬 0 commentsarXiv:2601.10956v1PDF
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Posted in cs.LG · 2026-01-16 · Jinshi Liu, Lei He, Pan Liu

CoVar: Confidence-Variance-Guided Pseudo-Label Selection for Semi-Supervised Learning

Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance. We propose CoVar, a confidence--variance framework that assesses pseudo-label reliability by jointly modeling Maximum Confidence (MC) and...

💬 0 commentsarXiv:2601.11670v3PDF
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Posted in cs.CR · 2026-01-16 · Kaiyu Zhou, Yongsen Zheng, Yicheng He, Meng Xue, Xueluan Gong, Yuji Wang, Xuanye Zhang, Kwok-Yan Lam

Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agents

The agent--tool interaction loop is a critical attack surface for modern Large Language Model (LLM) agents. Existing denial-of-service (DoS) attacks typically function at the user-prompt or retrieval-augmented generation (RAG) context layer and are inherently single-turn in nature. This limitation restricts cost amplification and...

💬 0 commentsarXiv:2601.10955v2PDF
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Posted in cs.AR · 2026-01-16 · Junming Zhang, Qinyan Zhang, Huajun Sun, Feiyang Gao, Sheng Hu, Rui Nie, Xiangshui Miao

SwiftKV: An Edge-Oriented Attention Algorithm and Multi-Head Accelerator for Fast, Efficient LLM Decoding

Edge acceleration for large language models is crucial for their widespread application; however, achieving fast attention inference and efficient decoding on resource-constrained edge accelerators remains challenging. This paper presents SwiftKV Attention, a per-token pipelined, low-latency single-pass attention inference algorithm,...

💬 0 commentsarXiv:2601.10953v1PDF
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Posted in cs.AI · 2026-01-16 · Wei Ai, Yilong Tan, Yuntao Shou, Tao Meng, Haowen Chen, Zhixiong He, Keqin Li

The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection

In recent years, the rapid evolution of large vision-language models (LVLMs) has driven a paradigm shift in multimodal fake news detection (MFND), transforming it from traditional feature-engineering approaches to unified, end-to-end multimodal reasoning frameworks. Early methods primarily relied on shallow fusion techniques to...

💬 0 commentsarXiv:2601.15316v1PDF
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Posted in cs.CL · 2026-01-16 · Shijie Jiang, Zefan Zhang, Kehua Zhu, Tian Bai, Ruihong Zhao

Multi-Stage Patient Role-Playing Framework for Realistic Clinical Interactions

The simulation of realistic clinical interactions plays a pivotal role in advancing clinical Large Language Models (LLMs) and supporting medical diagnostic education. Existing approaches and benchmarks rely on generic or LLM-generated dialogue data, which limits the authenticity and diversity of doctor-patient interactions. In this...

💬 0 commentsarXiv:2601.10951v1PDF
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Posted in cs.CV · 2026-01-16 · Meidan Ding, Jipeng Zhang, Wenxuan Wang, Haiqin Zhong, Xiaoling Luo, Wenting Chen, Linlin Shen

MMedExpert-R1: Strengthening Multimodal Medical Reasoning via Domain-Specific Adaptation and Clinical Guideline Reinforcement

Medical Vision-Language Models (MedVLMs) excel at perception tasks but struggle with complex clinical reasoning required in real-world scenarios. While reinforcement learning (RL) has been explored to enhance reasoning capabilities, existing approaches face critical mismatches: the scarcity of deep reasoning data, cold-start limits...

💬 0 commentsarXiv:2601.10949v2PDF
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Posted in cs.CV · 2026-01-16 · K Lokesh, Abhirama Subramanyam Penamakuri, Uday Agarwal, Apoorva Challa, Shreya K Gowda, Somesh Gupta, Anand Mishra

PatientVLM Meets DocVLM: Pre-Consultation Dialogue Between Vision-Language Models for Efficient Diagnosis

Traditionally, AI research in medical diagnosis has largely centered on image analysis. While this has led to notable advancements, the absence of patient-reported symptoms continues to hinder diagnostic accuracy. To address this, we propose a Pre-Consultation Dialogue Framework (PCDF) that mimics real-world diagnostic procedures,...

💬 0 commentsarXiv:2601.10945v1PDF
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Posted in cs.IR · 2026-01-16 · Xinyi Zhang, Yutong Li, Peijie Sun, Letian Sha, Zhongxuan Han

PRISM: Personalized Recommendation via Information Synergy Module

Multimodal sequential recommendation (MSR) leverages diverse item modalities to improve recommendation accuracy, while achieving effective and adaptive fusion remains challenging. Existing MSR models often overlook synergistic information that emerges only through modality combinations. Moreover, they typically assume a fixed...

💬 0 commentsarXiv:2601.10944v1PDF
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Posted in cs.LG · 2026-01-16 · Wenwen Liao, Hang Ruan, Jianbo Yu, Xiaofeng Yang, Qingchao Jiang, Xuefeng Yan

IPEC: Test-Time Incremental Prototype Enhancement Classifier for Few-Shot Learning

Metric-based few-shot approaches have gained significant popularity due to their relatively straightforward implementation, high interpret ability, and computational efficiency. However, stemming from the batch-independence assumption during testing, which prevents the model from leveraging valuable knowledge accumulated from previous...

💬 0 commentsarXiv:2601.11669v1PDF
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Posted in cs.SE · 2026-01-16 · Zitong Zhou, Matteo Paltenghi, Miryung Kim, Michael Pradel

Change And Cover: Last-Mile, Pull Request-Based Regression Test Augmentation

Software is in constant evolution, with developers frequently submitting pull requests (PRs) to introduce new features or fix bugs. Testing PRs is critical to maintaining software quality. Yet, even in projects with extensive test suites, some PR-modified lines remain untested, leaving a "last-mile" regression test gap. Existing test...

💬 0 commentsarXiv:2601.10942v1PDF
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Posted in cs.LG · 2026-01-16 · Xiaojie Xia, Huigang Zhang, Chaoliang Zhong, Jun Sun, Yusuke Oishi

Distill-then-Replace: Efficient Task-Specific Hybrid Attention Model Construction

Transformer architectures deliver state-of-the-art accuracy via dense full-attention, but their quadratic time and memory complexity with respect to sequence length limits practical deployment. Linear attention mechanisms offer linear or near-linear scaling yet often incur performance degradation. Hybrid models that integrate full and...

💬 0 commentsarXiv:2601.11667v2PDF
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Posted in cs.LG · 2026-01-16 · Aakriti Lnu, Zhe Li, Dandan Liang, Chao Huang, Rui Li, Haibo Yang

HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Training

Split learning (SL) enables collaborative training of large language models (LLMs) between resource-constrained edge devices and compute-rich servers by partitioning model computation across the network boundary. However, existing SL systems predominantly rely on first-order (FO) optimization, which requires clients to store...

💬 0 commentsarXiv:2601.10940v4PDF
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Posted in cs.IR · 2026-01-16 · Piyush Maheshwari, Sheshera Mysore, Hamed Zamani

Can Instructed Retrieval Models Really Support Exploration?

Exploratory searches are characterized by under-specified goals and evolving query intents. In such scenarios, retrieval models that can capture user-specified nuances in query intent and adapt results accordingly are desirable -- instruction-following retrieval models promise such a capability. In this work, we evaluate instructed...

💬 0 commentsarXiv:2601.10936v1PDF
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Posted in cs.CR · 2026-01-16 · Grazia D'Onghia, Diana Gratiela Berbecaru, Antonio Lioy

Shaping a Quantum-Resistant Future: Strategies for Post-Quantum PKI

As the quantum computing era approaches, securing classical cryptographic protocols becomes imperative. Public key cryptography is widely used for signature and key exchange but it is the type of cryptography more threatened by quantum computing. Its application typically requires support via a public-key certificate, which is a...

💬 0 commentsarXiv:2601.11104v1PDF
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Posted in cs.HC · 2026-01-16 · Minju Park, Seunghyun Lee, Juhwan Ma, Dongwook Yoon

AI Twin: Enhancing ESL Speaking Practice through AI Self-Clones of a Better Me

Advances in AI have enabled ESL learners to practice speaking through conversational systems. However, most tools rely on explicit correction, which can interrupt the conversation and undermine confidence. Grounded in second language acquisition and motivational psychology, we present AI Twin, a system that rephrases learner...

💬 0 commentsarXiv:2601.11103v2PDF
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Posted in cs.CV · 2026-01-16 · Shangbo Yuan, Jie Xu, Ping Hu, Xiaofeng Zhu, Na Zhao

Graph Smoothing for Enhanced Local Geometry Learning in Point Cloud Analysis

Graph-based methods have proven to be effective in capturing relationships among points for 3D point cloud analysis. However, these methods often suffer from suboptimal graph structures, particularly due to sparse connections at boundary points and noisy connections in junction areas. To address these challenges, we propose a novel...

💬 0 commentsarXiv:2601.11102v1PDF
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Posted in cs.AI · 2026-01-16 · Zhezheng Hao, Hong Wang, Jian Luo, Jianqing Zhang, Yuyan Zhou, Qiang Lin, Can Wang, Hande Dong, Jiawei Chen

ReCreate: Reasoning and Creating Domain Agents Driven by Experience

Large Language Model agents are reshaping the industrial landscape. However, most practical agents remain human-designed because tasks differ widely, making them labor-intensive to build. This situation poses a central question: can we automatically create and adapt domain agents in the wild? While several recent approaches have...

💬 0 commentsarXiv:2601.11100v2PDF
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Posted in cs.CR · 2026-01-16 · Zhuoyi Shang, Jiasen Li, Pengzhen Chen, Yanwei Liu, Xiaoyan Gu, Weiping Wang

Attesting Model Lineage by Consisted Knowledge Evolution with Fine-Tuning Trajectory

The fine-tuning technique in deep learning gives rise to an emerging lineage relationship among models. This lineage provides a promising perspective for addressing security concerns such as unauthorized model redistribution and false claim of model provenance, which are particularly pressing in \textcolor{blue}{open-weight model}...

💬 0 commentsarXiv:2601.11683v1PDF
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Posted in cs.CV · 2026-01-16 · Shuai Tan, Biao Gong, Ke Ma, Yutong Feng, Qiyuan Zhang, Yan Wang, Yujun Shen, Hengshuang Zhao

CoDance: An Unbind-Rebind Paradigm for Robust Multi-Subject Animation

Character image animation is gaining significant importance across various domains, driven by the demand for robust and flexible multi-subject rendering. While existing methods excel in single-person animation, they struggle to handle arbitrary subject counts, diverse character types, and spatial misalignment between the reference...

💬 0 commentsarXiv:2601.11096v1PDF
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Posted in cs.CR · 2026-01-16 · Grazia D'Onghia, Antonio Lioy

Towards Quantum-Resistant Trusted Computing: Architectures for Post-Quantum Integrity Verification Techniques

Trust is the core building block of secure systems, and it is enforced through methods to ensure that a specific system is properly configured and works as expected. In this context, a Root of Trust (RoT) establishes a trusted environment, where both data and code are authenticated via a digital signature based on asymmetric...

💬 0 commentsarXiv:2601.11095v1PDF
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Posted in cs.CL · 2026-01-16 · Ashish Raj Shekhar, Shiven Agarwal, Priyanuj Bordoloi, Yash Shah, Tejas Anvekar, Vivek Gupta

Integrity Shield A System for Ethical AI Use & Authorship Transparency in Assessments

Large Language Models (LLMs) can now solve entire exams directly from uploaded PDF assessments, raising urgent concerns about academic integrity and the reliability of grades and credentials. Existing watermarking techniques either operate at the token level or assume control over the model's decoding process, making them ineffective...

💬 0 commentsarXiv:2601.11093v1PDF
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Posted in cs.AI · 2026-01-16 · Suhan Guo, Jiahong Deng, Furao Shen

MiCA: A Mobility-Informed Causal Adapter for Lightweight Epidemic Forecasting

Accurate forecasting of infectious disease dynamics is critical for public health planning and intervention. Human mobility plays a central role in shaping the spatial spread of epidemics, but mobility data are noisy, indirect, and difficult to integrate reliably with disease records. Meanwhile, epidemic case time series are typically...

💬 0 commentsarXiv:2601.11089v2PDF