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Computer Science

arXiv preprints from January 1, 2026 through September 25, 2026 — 05:46:52 EST

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Posted in cs.AI · 2026-01-08 · Sofiene Lassoued, Laxmikant Shrikant Bahetic, Nathalie Weiß-Borkowskib, Stefan Lierc, Andreas Schwunga

Flexible Manufacturing Systems Intralogistics: Dynamic Optimization of AGVs and Tool Sharing Using Coloured-Timed Petri Nets and Actor-Critic RL with Actions Masking

Flexible Manufacturing Systems (FMS) are pivotal in optimizing production processes in today's rapidly evolving manufacturing landscape. This paper advances the traditional job shop scheduling problem by incorporating additional complexities through the simultaneous integration of automated guided vehicles (AGVs) and tool-sharing...

💬 0 commentsarXiv:2601.04887v1PDF
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Posted in cs.SE · 2026-01-08 · Jingzhi Gong, Giovanni Pinna, Yixin Bian, Jie M. Zhang

Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests

Pull request (PR) descriptions generated by AI coding agents are the primary channel for communicating code changes to human reviewers. However, the alignment between these messages and the actual changes remains unexplored, raising concerns about the trustworthiness of AI agents. To fill this gap, we analyzed 23,247 agentic PRs...

💬 0 commentsarXiv:2601.04886v2PDF
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Posted in cs.CL · 2026-01-08 · Ao Sun, Xiaoyu Wang, Zhe Tan, Yu Li, Jiachen Zhu, Yuheng Jia, Shu Su

CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters

As Large Language Models (LLMs) serve a global audience, alignment must transition from enforcing universal consensus to respecting cultural pluralism. We demonstrate that dense models, when forced to fit conflicting value distributions, suffer from \textbf{Mean Collapse}, converging to a generic average that fails to represent...

💬 0 commentsarXiv:2601.04885v3PDF
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Posted in cs.AI · 2026-01-08 · Issa Hanou, Eric Kemmeren, Devin Wild Thomas, Mathijs de Weerdt

Precomputing Multi-Agent Path Replanning Using Temporal Flexibility

Executing a multi-agent plan can be challenging when an agent is delayed, because this typically creates conflicts with other agents. So, we need to quickly find a new safe plan. Replanning only the delayed agent often does not yield an efficient plan, and sometimes cannot even yield a feasible one. On the other hand, replanning other...

💬 0 commentsarXiv:2601.04884v3PDF
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Posted in cs.NI · 2026-01-08 · Mattia Figaro, Francesco Rossato, Marco Giordani, Alessandro Traspadini, Takayuki Shimizu, Chinmay Mahabal, Sanjeewa Herath, Chunghan Lee, Dogan Kutay Pekcan, Michele Zorzi

5G NR Non-Terrestrial Networks: From Early Results to the Road Ahead

This paper overviews the 3GPP 5G NR-NTN standard, detailing the evolution from Rel. 18 to 19 and innovations for Rel. 20. Using realistic ns-3 simulations validated against 3GPP calibration data, we evaluate various satellite network configurations. The results highlight the potential of NTNs to extend wireless connectivity to remote...

💬 0 commentsarXiv:2601.04882v2PDF
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Posted in cs.RO · 2026-01-08 · Kiyoung Choi, Juwon Jeong, Sehoon Oh

Zero Wrench Control via Wrench Disturbance Observer for Learning-free Peg-in-hole Assembly

This paper proposes a Dynamic Wrench Disturbance Observer (DW-DOB) designed to achieve highly sensitive zero-wrench control in contact-rich manipulation. By embedding task-space inertia into the observer nominal model, DW-DOB cleanly separates intrinsic dynamic reactions from true external wrenches. This preserves sensitivity to small...

💬 0 commentsarXiv:2601.04881v1PDF
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Posted in cs.CL · 2026-01-08 · Mingyue Cheng, Daoyu Wang, Qi Liu, Shuo Yu, Xiaoyu Tao, Yuqian Wang, Chengzhong Chu, Yu Duan, Mingkang Long, Enhong Chen

Mind2Report: A Cognitive Deep Research Agent for Expert-Level Commercial Report Synthesis

Synthesizing informative commercial reports from massive and noisy web sources is critical for high-stakes business decisions. Although current deep research agents achieve notable progress, their reports still remain limited in terms of quality, reliability, and coverage. In this work, we propose Mind2Report, a cognitive deep...

💬 0 commentsarXiv:2601.04879v1PDF
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Posted in cs.AI · 2026-01-08 · Isabella A. Stewart, Markus J. Buehler

Higher-Order Knowledge Representations for Agentic Scientific Reasoning

Scientific inquiry requires systems-level reasoning that integrates heterogeneous experimental data, cross-domain knowledge, and mechanistic evidence into coherent explanations. While Large Language Models (LLMs) offer inferential capabilities, they often depend on retrieval-augmented contexts that lack structural depth. Traditional...

💬 0 commentsarXiv:2601.04878v1PDF
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Posted in cs.SD · 2026-01-08 · Kaiwen Luo, Liang Lin, Yibo Zhang, Moayad Aloqaily, Jialiang Tao, Dexian Wang, Zhenhong Zhou, Junwei Zhang, Kun Wang, Li Sun, Qingsong Wen

ChronosAudio: A Comprehensive Long-Audio Benchmark for Evaluating Audio-Large Language Models

Although Audio Large Language Models (ALLMs) have witnessed substantial advancements, their long audio understanding capabilities remain unexplored. A plethora of benchmarks have been proposed for general audio tasks, they predominantly focus on short-form clips, leaving without a consensus on evaluating ALLMs over extended durations....

💬 0 commentsarXiv:2601.04876v2PDF
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Posted in cs.CL · 2026-01-08 · Xuanguang Pan, Chongyang Tao, Jiayuan Bai, Jianling Gao, Zhengwei Tao, Xiansheng Zhou, Gavin Cheung, Shuai Ma

EvolSQL: Structure-Aware Evolution for Scalable Text-to-SQL Data Synthesis

Training effective Text-to-SQL models remains challenging due to the scarcity of high-quality, diverse, and structurally complex datasets. Existing methods either rely on limited human-annotated corpora, or synthesize datasets directly by simply prompting LLMs without explicit control over SQL structures, often resulting in limited...

💬 0 commentsarXiv:2601.04875v1PDF
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Posted in cs.LG · 2026-01-08 · Elisa Roldan, Kirstie Andrews, Stephen M. Richardson, Reyhaneh Fatahian, Glen Cooper, Rasool Erfani, Tasneem Sabir, Neil D. Reeves

FibreCastML: An Open Web Platform for Predicting Electrospun Nanofibre Diameter Distributions

Electrospinning is a scalable technique for producing fibrous scaffolds with tunable micro- and nanoscale architectures for applications in tissue engineering, drug delivery, and wound care. While machine learning (ML) has been used to support electrospinning process optimisation, most existing approaches predict only mean fibre...

💬 0 commentsarXiv:2601.04873v2PDF
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Posted in cs.DB · 2026-01-08 · Meghyn Bienvenu, Diego Figueira, Pierre Lafourcade

Responsibility Measures for Conjunctive Queries with Negation

We contribute to the recent line of work on responsibility measures that quantify the contributions of database facts to obtaining a query result. In contrast to existing work which has almost exclusively focused on monotone queries, here we explore how to define responsibility measures for unions of conjunctive queries with negated...

💬 0 commentsarXiv:2601.04868v3PDF
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Posted in cs.AI · 2026-01-08 · Haihua Luo, Xuming Ran, Zhengji Li, Huiyan Xue, Tingting Jiang, Jiangrong Shen, Tommi Kärkkäinen, Qi Xu, Fengyu Cong

Key-Value Pair-Free Continual Learner via Task-Specific Prompt-Prototype

Continual learning aims to enable models to acquire new knowledge while retaining previously learned information. Prompt-based methods have shown remarkable performance in this domain; however, they typically rely on key-value pairing, which can introduce inter-task interference and hinder scalability. To overcome these limitations,...

💬 0 commentsarXiv:2601.04864v2PDF
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Posted in cs.IT · 2026-01-08 · Ailing Zheng, Qingqing Wu, Ziyuan Zheng, Qiaoyan Peng, Yanze Zhu, Honghao Wang, Wen Chen, Guoying Zhang

Wireless Communication with Cross-Linked Rotatable Antenna Array: Architecture Design and Rotation Optimization

Rotatable antenna (RA) technology can harness additional spatial degrees of freedom by enabling the dynamic three-dimensional orientation control of each antenna. Unfortunately, the hardware cost and control complexity of traditional RA systems is proportional to the number of RAs. To address the issue, we consider a cross-linked (CL)...

💬 0 commentsarXiv:2601.04862v1PDF
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Posted in cs.AI · 2026-01-08 · Jingbo Wang, Sendong Zhao, Jiatong Liu, Haochun Wang, Wanting Li, Bing Qin, Ting Liu

Orchestrating Intelligence: Confidence-Aware Routing for Efficient Multi-Agent Collaboration across Multi-Scale Models

While multi-agent systems (MAS) have demonstrated superior performance over single-agent approaches in complex reasoning tasks, they often suffer from significant computational inefficiencies. Existing frameworks typically deploy large language models (LLMs) uniformly across all agent roles, failing to account for the varying...

💬 0 commentsarXiv:2601.04861v2PDF
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Posted in cs.CV · 2026-01-08 · Ayush Pande, Mayank Vatsa

DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation

Interactive 3D segmentation of a reconstructed scene should not require a representation-specific optimization loop. We observe that the recipe for lifting 2D foundation-model masks into 3D, namely prompting a few views, refining the resulting masks with rendered depth, and fusing the multi-view evidence into a voxel grid, is shared...

💬 0 commentsarXiv:2601.04860v2PDF
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Posted in cs.CL · 2026-01-08 · Maxime Delmas, Lei Xu, André Freitas

A Navigational Approach for Comprehensive RAG via Traversal over Proposition Graphs

Standard RAG pipelines based on chunking excel at simple factual retrieval but fail on complex multi-hop queries due to a lack of structural connectivity. Conversely, initial strategies that interleave retrieval with reasoning often lack global corpus awareness, while Knowledge Graph (KG)-based RAG performs strongly on complex...

💬 0 commentsarXiv:2601.04859v1PDF
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Posted in cs.CY · 2026-01-08 · Zhanyu Liu, Yang Yu

Towards Public Administration Research Based on Interpretable Machine Learning

Causal relationships play a pivotal role in research within the field of public administration. Ensuring reliable causal inference requires validating the predictability of these relationships, which is a crucial precondition. However, prediction has not garnered adequate attention within the realm of quantitative research in public...

💬 0 commentsarXiv:2601.06205v1PDF
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Posted in cs.CL · 2026-01-08 · Zhiwei Liu, Paul Thompson, Jiaqi Rong, Baojie Qu, Runteng Guo, Min Peng, Qianqian Xie, Sophia Ananiadou

MisSpans: Fine-Grained False Span Identification in Cross-Domain Fake News

Online misinformation is increasingly pervasive, yet most existing benchmarks and methods evaluate veracity at the level of whole claims or paragraphs using coarse binary labels, obscuring how true and false details often co-exist within single sentences. These simplifications also limit interpretability: global explanations cannot...

💬 0 commentsarXiv:2601.04857v1PDF
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Posted in cs.LG · 2026-01-08 · Francesco Ferrini, Veronica Lachi, Antonio Longa, Bruno Lepri, Matono Akiyoshi, Andrea Passerini, Xin Liu, Manfred Jaeger

Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution

Handling missing node features is a key challenge for deploying Graph Neural Networks (GNNs) in real-world domains such as healthcare and sensor networks. Existing studies mostly address relatively benign scenarios, namely benchmark datasets with (a) high-dimensional but sparse node features and (b) incomplete data generated under...

💬 0 commentsarXiv:2601.04855v2PDF
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Posted in cs.CL · 2026-01-08 · Oshri Naparstek

Projected Autoregression: Autoregressive Language Generation in Continuous State Space

Standard autoregressive language models generate text by repeatedly selecting a discrete next token, coupling prediction with irreversible commitment at every step. We show that token selection is not the only viable autoregressive interface. \textbf{Projected Autoregression} replaces token selection with continuous prediction in...

💬 0 commentsarXiv:2601.04854v3PDF
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Posted in cs.CL · 2026-01-08 · Zhiwei Liu, Runteng Guo, Baojie Qu, Yuechen Jiang, Min Peng, Qianqian Xie, Sophia Ananiadou

RAAR: Retrieval Augmented Agentic Reasoning for Cross-Domain Misinformation Detection

Cross-domain misinformation detection is challenging, as misinformation arises across domains with substantial differences in knowledge and discourse. Existing methods often rely on single-perspective cues and struggle to generalize to challenging or underrepresented domains, while reasoning large language models (LLMs), though...

💬 0 commentsarXiv:2601.04853v1PDF
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Posted in cs.CR · 2026-01-08 · Tooba Qasim, Vasilios A. Siris, Izak Oosthuizen, Muttukrishnan Rajarajan, Sujit Biswas

Quantum Secure Biometric Authentication in Decentralised Systems

Biometric authentication has become integral to digital identity systems, particularly in smart cities where it en-ables secure access to services across governance, trans-portation, and public infrastructure. Centralised archi-tectures, though widely used, pose privacy and scalabil-ity challenges due to the aggregation of sensitive...

💬 0 commentsarXiv:2601.04852v1PDF
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Posted in cs.IT · 2026-01-08 · Yijun Zhong, Yi Shen

Stability of Constrained Optimization Models for Structured Signal Recovery

Recovering an unknown but structured signal from its measurements is a challenging problem with significant applications in fields such as imaging restoration, wireless communications, and signal processing. In this paper, we consider the inherent problem stems from the prior knowledge about the signal's structure, such as sparsity...

💬 0 commentsarXiv:2601.04849v1PDF
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Posted in cs.CV · 2026-01-08 · Tayyab Rehman, Giovanni De Gasperis, Aly Shmahell

Cascading multi-agent anomaly detection in surveillance systems via vision-language models and embedding-based classification

Intelligent anomaly detection in dynamic visual environments requires reconciling real-time performance with semantic interpretability. Conventional approaches address only fragments of this challenge. Reconstruction-based models capture low-level deviations without contextual reasoning, object detectors provide speed but limited...

💬 0 commentsarXiv:2601.06204v3PDF