Qwen Councils

Computer Science

arXiv preprints from January 1, 2026 through September 9, 2026 — 01:45:49 EST

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Posted in cs.LG · 2026-01-19 · Gyuyeon Na, Minjung Park, Soyoun Kim, Jungbin Shin, Sangmi Chai

Knowledge-Integrated Representation Learning for Crypto Anomaly Detection under Extreme Label Scarcity; Relational Domain-Logic Integration with Retrieval-Grounded Context and Path-Level Explanations

Detecting anomalous trajectories in decentralized crypto networks is fundamentally challenged by extreme label scarcity and the adaptive evasion strategies of illicit actors. While Graph Neural Networks (GNNs) effectively capture local structural patterns, they struggle to internalize multi hop, logic driven motifs such as fund...

💬 0 commentsarXiv:2601.12839v1PDF
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Posted in cs.CL · 2026-01-19 · Baek Seong-Eun, Lee Jung-Mok, Kim Sung-Bin, Tae-Hyun Oh

A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search

Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) offers a resource-efficient way to personalize or specialize. However, LoRA is highly sensitive to hyperparameter choices, and exhaustive hyperparameter search is computationally expensive. To address this, we propose a Bayesian Optimization (BO) framework that...

💬 0 commentsarXiv:2602.11171v2PDF
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Posted in cs.GT · 2026-01-19 · Kui-Wang Choi, Minming Li

Temporal Fair Division of Indivisible Goods with Scheduling

We study temporal fair division, where agents receive goods over multiple rounds and cumulative fairness is required. We investigate Temporal Envy-Freeness Up to One Good (TEF1) and Up to Any Good (TEFX), its approximation $α$-TEFX, and Temporal Maximin Share (TMMS). Motivated by known impossibilities in standard settings, we consider...

💬 0 commentsarXiv:2601.12835v3PDF
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Posted in cs.DC · 2026-01-19 · Om Mishra, Jayesh Patil, Sathwik Narkedimilli, G Srikantha Sharma, Ananda S, Manjunath K Vanahalli

From Design to Deorbit: A Solar-Electric Autonomous Module for Multi-Debris Remediation

The escalating accumulation of orbital debris threatens the sustainability of space operations, necessitating active removal solutions that overcome the limitations of current fuel-dependent methods. To address this, this study introduces a novel remediation architecture that integrates a mechanical clamping system for secure capture...

💬 0 commentsarXiv:2601.12830v1PDF
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Posted in cs.IR · 2026-01-19 · Masoud Mansoury, Jin Huang, Mykola Pechenizkiy, Herke van Hoof, Maarten de Rijke

The Unfairness of Multifactorial Bias in Recommendation

Popularity bias and positivity bias are two prominent sources of bias in recommender systems. Both arise from input data, propagate through recommendation models, and lead to unfair or suboptimal outcomes. Popularity bias occurs when a small subset of items receives most interactions, while positivity bias stems from the...

💬 0 commentsarXiv:2601.12828v1PDF
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Posted in cs.CV · 2026-01-19 · Teerapong Panboonyuen

Seeing Isn't Always Believing: Analysis of Grad-CAM Faithfulness and Localization Reliability in Lung Cancer CT Classification

Explainable Artificial Intelligence (XAI) techniques, such as Gradient-weighted Class Activation Mapping (Grad-CAM), have become indispensable for visualizing the reasoning process of deep neural networks in medical image analysis. Despite their popularity, the faithfulness and reliability of these heatmap-based explanations remain...

💬 0 commentsarXiv:2601.12826v1PDF
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Posted in cs.CV · 2026-01-19 · Belal Shaheen, Minh-Hieu Nguyen, Bach-Thuan Bui, Shubham, Tim Wu, Michael Fairley, Matthew David Zane, Michael Wu, James Tompkin

TreeDGS: Aerial Gaussian Splatting for Distant DBH Measurement

Aerial remote sensing efficiently surveys large areas, but accurate direct object-level measurement remains difficult in complex natural scenes. Advancements in 3D computer vision, particularly radiance field representations such as NeRF and 3D Gaussian splatting, can improve reconstruction fidelity from posed imagery. Nevertheless,...

💬 0 commentsarXiv:2601.12823v3PDF
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Posted in cs.AI · 2026-01-19 · Wenqi Zhang, Yulin Shen, Changyue Jiang, Jiarun Dai, Geng Hong, Xudong Pan

MirrorGuard: Toward Secure Computer-Use Agents via Simulation-to-Real Reasoning Correction

Large foundation models are integrated into Computer Use Agents (CUAs), enabling autonomous interaction with operating systems through graphical user interfaces (GUIs) to perform complex tasks. This autonomy introduces serious security risks: malicious instructions or visual prompt injections can trigger unsafe reasoning and cause...

💬 0 commentsarXiv:2601.12822v1PDF
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Posted in cs.CV · 2026-01-19 · Wei Chen, Liang Wu, Shuyi Lu, Yuanyuan Sun, Wenkai Bi, Zilong Yuan, Yaoyao He, Feng Wang, Junchi Ma, Shuyong Liu, Zhaoping Cheng, Xiaoyan Hu, Jianfeng Qiu

A Generalist Foundation Model for Total-body PET/CT Enables Diagnostic Reporting and System-wide Metabolic Profiling

Total-body PET/CT enables system-wide molecular imaging, but heterogeneous anatomical and metabolic signals, approximately 2 m axial coverage, and structured radiology semantics challenge existing medical AI models that assume single-modality inputs, localized fields of view, and coarse image-text alignment. We introduce SDF-HOLO...

💬 0 commentsarXiv:2601.12820v1PDF
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Posted in cs.IT · 2026-01-19 · Sebastian Bitzer, Alberto Ravagnani, Violetta Weger

Weighted-Hamming Metric: Bounds and Codes

The weighted-Hamming metric generalizes the Hamming metric by assigning different weights to blocks of coordinates. It is well-suited for applications such as coding over independent parallel channels, each of which has a different level of importance or noise. From a coding-theoretic perspective, the actual error-correction...

💬 0 commentsarXiv:2601.12998v1PDF
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Posted in cs.LG · 2026-01-19 · Laha Ale, Hu Luo, Mingsheng Cao, Shichao Li, Huanlai Xing, Haifeng Sun

Lightweight Edge Learning via Dataset Pruning

Edge learning facilitates ubiquitous intelligence by enabling model training and adaptation directly on data-generating devices, thereby mitigating privacy risks and communication latency. However, the high computational and energy overhead of on-device training hinders its deployment on battery-powered mobile systems with strict...

💬 0 commentsarXiv:2602.00047v1PDF
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Posted in cs.MA · 2026-01-19 · Shiyuan Li, Yixin Liu, Yu Zheng, Mei Li, Quoc Viet Hung Nguyen, Shirui Pan

OFA-MAS: One-for-All Multi-Agent System Topology Design based on Mixture-of-Experts Graph Generative Models

Multi-Agent Systems (MAS) offer a powerful paradigm for solving complex problems, yet their performance is critically dependent on the design of their underlying collaboration topology. As MAS become increasingly deployed in web services (e.g., search engines), designing adaptive topologies for diverse cross-domain user queries...

💬 0 commentsarXiv:2601.12996v1PDF
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Posted in cs.CL · 2026-01-19 · Runxuan Liu, Xianhao Ou, Xinyan Ma, Jiyuan Wang, Jiafeng Liang, Jiaqi Li, Tao He, Zheng Chu, Rongchuan Mu, Zekun Wang, Baoxin Wang, Dayong Wu, Ming Liu, Shijin Wang, Guoping Hu, Bing Qin

Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models

Long Chain-of-Thought (LCoT), achieved by Reinforcement Learning with Verifiable Rewards (RLVR), has proven effective in enhancing the reasoning capabilities of Large Language Models (LLMs). However, reasoning in current LLMs is primarily generated as plain text, where performing semantic evaluation on such unstructured data creates a...

💬 0 commentsarXiv:2601.12995v1PDF
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Posted in cs.CV · 2026-01-19 · Shiming Wang, Holger Caesar, Liangliang Nan, Julian F. P. Kooij

AsyncBEV: Cross-modal Flow Alignment in Asynchronous 3D Object Detection

In autonomous driving, multi-modal perception tasks like 3D object detection typically rely on well-synchronized sensors, both at training and inference. However, despite the use of hardware- or software-based synchronization algorithms, perfect synchrony is rarely guaranteed: Sensors may operate at different frequencies, and...

💬 0 commentsarXiv:2601.12994v1PDF
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Posted in cs.RO · 2026-01-19 · Hao Luo, Ye Wang, Wanpeng Zhang, Sipeng Zheng, Ziheng Xi, Chaoyi Xu, Haiweng Xu, Haoqi Yuan, Chi Zhang, Yiqing Wang, Yicheng Feng, Zongqing Lu

Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization

We introduce Being-H0.5, a foundational Vision-Language-Action (VLA) model designed for robust cross-embodiment generalization across diverse robotic platforms. While existing VLAs often struggle with morphological heterogeneity and data scarcity, we propose a human-centric learning paradigm that treats human interaction traces as a...

💬 0 commentsarXiv:2601.12993v1PDF
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Posted in cs.HC · 2026-01-19 · Haoyu Tian, Yingchaojie Feng, Zhen Wen, Haoxuan Li, Minfeng Zhu, Wei Chen

RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systems

The advent of Retrieval-Augmented Generation (RAG) has significantly enhanced the ability of Large Language Models (LLMs) to produce factually accurate and up-to-date responses. However, the performance of a RAG system is not determined by a single component but emerges from a complex interplay of modular choices, such as embedding...

💬 0 commentsarXiv:2601.12991v2PDF
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Posted in cs.DC · 2026-01-19 · Yitian Wang, Yebo Feng, Yingjiu Li, Jiahua Xu

Enshrined Proposer Builder Separation in the presence of Maximal Extractable Value

In blockchain systems operating under the Proof-of-Stake (PoS) consensus mechanism, fairness in transaction processing is essential to preserving decentralization and maintaining user trust. However, with the emergence of Maximal Extractable Value (MEV), concerns about economic centralization and content manipulation have intensified....

💬 0 commentsarXiv:2601.12989v1PDF
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Posted in cs.LG · 2026-01-19 · Zijian Wang, Tiancheng Huang, Hanqi Li, Da Ma, Lu Chen, Kai Yu

PaperGuide: Making Small Language-Model Paper-Reading Agents More Efficient

The accelerating growth of the scientific literature makes it increasingly difficult for researchers to track new advances through manual reading alone. Recent progress in large language models (LLMs) has therefore spurred interest in autonomous agents that can read scientific papers and extract task-relevant information. However,...

💬 0 commentsarXiv:2601.12988v1PDF
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Posted in cs.CR · 2026-01-19 · Zhenhua Xu, Xiaoning Tian, Wenjun Zeng, Wenpeng Xing, Tianliang Lu, Gaolei Li, Chaochao Chen, Meng Han

KinGuard: Hierarchical Kinship-Aware Fingerprinting to Defend Against Large Language Model Stealing

Protecting the intellectual property of large language models requires robust ownership verification. Conventional backdoor fingerprinting, however, is flawed by a stealth-robustness paradox: to be robust, these methods force models to memorize fixed responses to high-perplexity triggers, but this targeted overfitting creates...

💬 0 commentsarXiv:2601.12986v2PDF
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Posted in cs.LG · 2026-01-19 · Sarthak Sattigeri

Extending Beacon to Hindi: Cultural Adaptation Drives Cross-Lingual Sycophancy

Sycophancy, the tendency of language models to prioritize agreement with user preferences over principled reasoning, has been identified as a persistent alignment failure in English-language evaluations. However, it remains unclear whether such diagnostics generalize across languages and cultural contexts. We extend the Beacon...

💬 0 commentsarXiv:2602.00046v1PDF
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Posted in cs.IR · 2026-01-19 · Melanie A. Kilian, David Elsweiler

Rules, Resources, and Restrictions: A Taxonomy of Task-Based Information Request Intents

Understanding and classifying query intents can improve retrieval effectiveness by helping align search results with the motivations behind user queries. However, existing intent taxonomies are typically derived from system log data and capture mostly isolated information needs, while the broader task context often remains...

💬 0 commentsarXiv:2601.12985v1PDF
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Posted in cs.CL · 2026-01-19 · Jesus-German Ortiz-Barajas, Jonathan Tonglet, Vivek Gupta, Iryna Gurevych

ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation

Multimodal large language models (MLLMs) are increasingly used to automate chart generation from data tables, improving analysis and reporting efficiency while introducing new misuse risks. We present ChartAttack, a framework for evaluating how MLLMs can generate misleading charts at scale by injecting misleaders into chart designs to...

💬 0 commentsarXiv:2601.12983v3PDF
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Posted in cs.CV · 2026-01-19 · Sulaiman Khan, Md. Rafiul Biswas, Zubair Shah

Early Prediction of Type 2 Diabetes Using Multimodal data and Tabular Transformers

This study introduces a novel approach for early Type 2 Diabetes Mellitus (T2DM) risk prediction using a tabular transformer (TabTrans) architecture to analyze longitudinal patient data. By processing patients` longitudinal health records and bone-related tabular data, our model captures complex, long-range dependencies in disease...

💬 0 commentsarXiv:2601.12981v1PDF
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Posted in cs.GT · 2026-01-19 · Masatsugu Yoshizawa, Yuta Kawamoto, Daisuke Takeshita

Rules Create Unequal Rewards: Elite Tennis Players Allocate Resources Efficiently

In many competitive settings, from education to politics, rules do not reward effort evenly, and thresholds (e.g., grade cutoffs or electoral majorities) make some moments disproportionately important. Success thus depends on efficiently allocating limited resources. However, empirical demonstration has been difficult because effort...

💬 0 commentsarXiv:2601.15327v1PDF
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Posted in cs.NI · 2026-01-19 · Hongbo Wang, Xin Li, Yinghui He, Jingzhi Hu, Mingming Xu, Zhe Chen, Fu Xiao, Jun Luo

Path to Diversity: A Primer on ISAC-izing Commodity Wi-Fi for Practical Deployments

Integrated Sensing and Communication (ISAC) has emerged as a key paradigm in next-generation wireless networks. While the ubiquity and low cost of commodity Wi-Fi make it an ideal platform for wide-scale sensing, it is the continuous evolution of Wi-Fi standards-towards higher frequency bands, wider bandwidths, and larger antenna...

💬 0 commentsarXiv:2601.12980v2PDF