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e54c352176262afa6682c6bf90af879b251ac88330bca5fada65a9332d85ce44 | 2026-01-07T00:00:00-05:00 | Learning and Optimizing the Efficacy of Spatio-Temporal Task Allocation under Temporal and Resource Constraints | arXiv:2601.02505v1 Announce Type: new Abstract: Complex multi-robot missions often require heterogeneous teams to jointly optimize task allocation, scheduling, and path planning to improve team performance under strict constraints. We formalize these complexities into a new class of problems, dubbed Spatio-Temporal Eff... | https://arxiv.org/abs/2601.02505 | Academic Papers | svg |
66c17a431d9ffc62251a952d31c9eafdd95e942fc8e34eb46ddb66edd3d2017a | 2026-01-07T00:00:00-05:00 | hdlib 2.0: Extending Machine Learning Capabilities of Vector-Symbolic Architectures | arXiv:2601.02509v1 Announce Type: new Abstract: Following the initial publication of hdlib, a Python library for designing Vector-Symbolic Architectures (VSA), we introduce a major extension that significantly enhances its machine learning capabilities. VSA, also known as Hyperdimensional Computing, is a computing para... | https://arxiv.org/abs/2601.02509 | Academic Papers | svg |
0cbe4ddb092bcdaef816629c3956aa8248a1e2c89311e982162397eea50e471a | 2026-01-07T00:00:00-05:00 | LLM-Enhanced Reinforcement Learning for Time Series Anomaly Detection | arXiv:2601.02511v1 Announce Type: new Abstract: Detecting anomalies in time series data is crucial for finance, healthcare, sensor networks, and industrial monitoring applications. However, time series anomaly detection often suffers from sparse labels, complex temporal patterns, and costly expert annotation. We propos... | https://arxiv.org/abs/2601.02511 | Academic Papers | svg |
42c042faf85de67b113b1e035891f13dda7ce44147c22f7b31a6b2ef71ddb4b3 | 2026-01-07T00:00:00-05:00 | Green LLM Techniques in Action: How Effective Are Existing Techniques for Improving the Energy Efficiency of LLM-Based Applications in Industry? | arXiv:2601.02512v1 Announce Type: new Abstract: The rapid adoption of large language models (LLMs) has raised concerns about their substantial energy consumption, especially when deployed at industry scale. While several techniques have been proposed to address this, limited empirical evidence exists regarding the effe... | https://arxiv.org/abs/2601.02512 | Academic Papers | svg |
eaa5c3aa8a505f19164fd99c3aab38e04170129d6576356ead527b9a0b09ac93 | 2026-01-07T00:00:00-05:00 | On well-posed energy/entropy stable boundary conditions for the rotating shallow water equations | arXiv:2601.02513v1 Announce Type: new Abstract: We derive and analyze well-posed, energy- and entropy-stable boundary conditions (BCs) for the two-dimensional linear and nonlinear rotating shallow water equations (RSWE) in vector invariant form. The focus of the study is on subcritical flows, which are commonly observe... | https://arxiv.org/abs/2601.02513 | Academic Papers | svg |
cbfa711f8f821d3d7b1a2ffc6a5d9bf9434ee2b7db5bc74a74fa508f2ec2f749 | 2026-01-07T00:00:00-05:00 | Textual Explanations and Their Evaluations for Reinforcement Learning Policy | arXiv:2601.02514v1 Announce Type: new Abstract: Understanding a Reinforcement Learning (RL) policy is crucial for ensuring that autonomous agents behave according to human expectations. This goal can be achieved using Explainable Reinforcement Learning (XRL) techniques. Although textual explanations are easily understo... | https://arxiv.org/abs/2601.02514 | Academic Papers | svg |
b87d38ed7493f0f1ee3d39c28c595900eb6960c3920da6bcdca037c9b24e6b06 | 2026-01-07T00:00:00-05:00 | CT Scans As Video: Efficient Intracranial Hemorrhage Detection Using Multi-Object Tracking | arXiv:2601.02521v1 Announce Type: new Abstract: Automated analysis of volumetric medical imaging on edge devices is severely constrained by the high memory and computational demands of 3D Convolutional Neural Networks (CNNs). This paper develops a lightweight computer vision framework that reconciles the efficiency of ... | https://arxiv.org/abs/2601.02521 | Academic Papers | svg |
9f991a34c677f7d957b101ad38bffd76a2560021eaacdb0a915ddfb9653ef2f0 | 2026-01-07T00:00:00-05:00 | On the Effectiveness of Proposed Techniques to Reduce Energy Consumption in RAG Systems: A Controlled Experiment | arXiv:2601.02522v1 Announce Type: new Abstract: The rising energy demands of machine learning (ML), e.g., implemented in popular variants like retrieval-augmented generation (RAG) systems, have raised significant concerns about their environmental sustainability. While previous research has proposed green tactics for M... | https://arxiv.org/abs/2601.02522 | Academic Papers | svg |
a5e87e1f7de6a46d30a21b0e039071f2759b75fa4ece07cc7c76296b14b8e8fa | 2026-01-07T00:00:00-05:00 | Modellierung und Simulation der Dynamik von Fussg\"angerstr\"omen | arXiv:2601.02526v1 Announce Type: new Abstract: This work presents a microscopic model to describe pedestrian flows based on the social force theory. The aim of this study is twofold: (1) developing a realistic model that can be used as a tool for designing pedestrian-friendly infrastructure, and (2) verifying a social... | https://arxiv.org/abs/2601.02526 | Academic Papers | svg |
3768a44e30bd126965c344735e5bd3c205f92559cb701aac482e1fded5f1a48d | 2026-01-07T00:00:00-05:00 | Multi-scale Graph Autoregressive Modeling: Molecular Property Prediction via Next Token Prediction | arXiv:2601.02530v1 Announce Type: new Abstract: We present Connection-Aware Motif Sequencing (CamS), a graph-to-sequence representation that enables decoder-only Transformers to learn molecular graphs via standard next-token prediction (NTP). For molecular property prediction, SMILES-based NTP scales well but lacks exp... | https://arxiv.org/abs/2601.02530 | Academic Papers | svg |
b7c82c0c993978cdf1104bbb0762d5498de119153fea72424a8123f0f205b3da | 2026-01-07T00:00:00-05:00 | Losses that Cook: Topological Optimal Transport for Structured Recipe Generation | arXiv:2601.02531v1 Announce Type: new Abstract: Cooking recipes are complex procedures that require not only a fluent and factual text, but also accurate timing, temperature, and procedural coherence, as well as the correct composition of ingredients. Standard training procedures are primarily based on cross-entropy an... | https://arxiv.org/abs/2601.02531 | Academic Papers | svg |
6bd245467858fd18ed44e6dab8b550c17891ff2dd8ffcb3982d965e8a757d9f6 | 2026-01-07T00:00:00-05:00 | A $O^*((2 + \epsilon)^k)$ Time Algorithm for Cograph Deletion Using Unavoidable Subgraphs in Large Prime Graphs | arXiv:2601.02532v1 Announce Type: new Abstract: We study the parameterized complexity of the Cograph Deletion problem, which asks whether one can delete at most $k$ edges from a graph to make it $P_4$-free. This is a well-known graph modification problem with applications in computation biology and social network analy... | https://arxiv.org/abs/2601.02532 | Academic Papers | svg |
1f0a0c2fec549ffe5c179cba6affef387cc4679dd3ae59f6b4bff56701cb0044 | 2026-01-07T00:00:00-05:00 | ModeX: Evaluator-Free Best-of-N Selection for Open-Ended Generation | arXiv:2601.02535v1 Announce Type: new Abstract: Selecting a single high-quality output from multiple stochastic generations remains a fundamental challenge for large language models (LLMs), particularly in open-ended tasks where no canonical answer exists. While Best-of-N and self-consistency methods show that aggregat... | https://arxiv.org/abs/2601.02535 | Academic Papers | svg |
37c5e2a176b29ac39b25ae4a831eb17e9249550af7d60358149f66b2170235f4 | 2026-01-07T00:00:00-05:00 | MovieRecapsQA: A Multimodal Open-Ended Video Question-Answering Benchmark | arXiv:2601.02536v1 Announce Type: new Abstract: Understanding real-world videos such as movies requires integrating visual and dialogue cues to answer complex questions. Yet existing VideoQA benchmarks struggle to capture this multimodal reasoning and are largely not open-ended, given the difficulty of evaluating free-... | https://arxiv.org/abs/2601.02536 | Academic Papers | svg |
bd3f44edb43834a09404f705a2c13cdfd1a2ca821f05fb8146228d2cd86e8a54 | 2026-01-07T00:00:00-05:00 | Optimal Oblivious Load-Balancing for Sparse Traffic in Large-Scale Satellite Networks | arXiv:2601.02537v1 Announce Type: new Abstract: Oblivious load-balancing in networks involves routing traffic from sources to destinations using predetermined routes independent of the traffic, so that the maximum load on any link in the network is minimized. We investigate oblivious load-balancing schemes for a $N\tim... | https://arxiv.org/abs/2601.02537 | Academic Papers | svg |
c1f89cbf0dd6ea457ebaf79f6954832de83da280a18ee352f9e9cb44ff345c5f | 2026-01-07T00:00:00-05:00 | GPU-Accelerated Energy-Conserving Methods for the Hyperbolized Serre-Green-Naghdi Equations in 2D | arXiv:2601.02540v1 Announce Type: new Abstract: We develop energy-conserving numerical methods for a two-dimensional hyperbolic approximation of the Serre-Green-Naghdi equations with variable bathymetry for both periodic and reflecting boundary conditions. The hyperbolic formulation avoids the costly inversion of an el... | https://arxiv.org/abs/2601.02540 | Academic Papers | svg |
f347a7fb57ce3860fab4ed65fcf87fc0647f63f9ac1f99b64867adf8792a16bf | 2026-01-07T00:00:00-05:00 | Normalized Conditional Mutual Information Surrogate Loss for Deep Neural Classifiers | arXiv:2601.02543v1 Announce Type: new Abstract: In this paper, we propose a novel information theoretic surrogate loss; normalized conditional mutual information (NCMI); as a drop in alternative to the de facto cross-entropy (CE) for training deep neural network (DNN) based classifiers. We first observe that the model'... | https://arxiv.org/abs/2601.02543 | Academic Papers | svg |
180d8ef43eeef670b957fa37799540a8bcf5cf66ff545d4e1b697667f9bd6e9b | 2026-01-07T00:00:00-05:00 | SimpleMem: Efficient Lifelong Memory for LLM Agents | arXiv:2601.02553v1 Announce Type: new Abstract: To support reliable long-term interaction in complex environments, LLM agents require memory systems that efficiently manage historical experiences. Existing approaches either retain full interaction histories via passive context extension, leading to substantial redundan... | https://arxiv.org/abs/2601.02553 | Academic Papers | svg |
795e3080191cd92071d5d1ec3cc5456dc0091c1c40c40bcb276acef7cd9739e6 | 2026-01-07T00:00:00-05:00 | AMC26: VSSEA robust position control | arXiv:2601.02557v1 Announce Type: new Abstract: This paper presents robust position control strategies for the novel VSSEA. By employing a constructed state-space model, two control schemes are developed in a unified framework: a state-feedback controller and a sliding mode controller, both integrated with a second-ord... | https://arxiv.org/abs/2601.02557 | Academic Papers | svg |
efa0859d8f537f6f4146a195ad33ae2b721a57137094d426c4ad98b82f5842dc | 2026-01-07T00:00:00-05:00 | PerspectiveCoach: Exploring LLMs for Developer Reflection | arXiv:2601.02559v1 Announce Type: new Abstract: Despite growing awareness of ethical challenges in software development, practitioners still lack structured tools that help them critically engage with the lived experiences of marginalized users. This paper presents PerspectiveCoach, a large language model (LLM)-powered... | https://arxiv.org/abs/2601.02559 | Academic Papers | svg |
04bdf04243cfa7775581bd9bc31bd51740e35e694bc03bf86d4fa65ce710e8e7 | 2026-01-07T00:00:00-05:00 | AMC26: High-performance DOb for robust position control | arXiv:2601.02560v1 Announce Type: new Abstract: This paper presents a new HPDOb that significantly improves disturbance estimation accuracy and robustness in motion control systems, surpassing the capabilities of conventional DObs. The proposed observer is analysed and synthesised in the discrete-time domain, providing... | https://arxiv.org/abs/2601.02560 | Academic Papers | svg |
5f3128c3e82c45c34f13b68d16e1c8a691e0579f6557bea24ea2df773dc297f2 | 2026-01-07T00:00:00-05:00 | A Schr\"odinger-Based Dispersive Regularization Approach for Numerical Simulation of One-Dimensional Shallow Water Equations | arXiv:2601.02561v1 Announce Type: new Abstract: We propose a novel dispersive regularization framework for the numerical simulation of the one-dimensional shallow water equations (SWE). The classical hyperbolic system is regularized by a third-order dispersive term in the momentum equation, which renders the system equ... | https://arxiv.org/abs/2601.02561 | Academic Papers | svg |
06ff8dbf83a5c22b706b0d1132e856aa88baca04e4f22642b316028633f1d9e2 | 2026-01-07T00:00:00-05:00 | CutisAI: Deep Learning Framework for Automated Dermatology and Cancer Screening | arXiv:2601.02562v1 Announce Type: new Abstract: The rapid growth of dermatological imaging and mobile diagnostic tools calls for systems that not only demonstrate empirical performance but also provide strong theoretical guarantees. Deep learning models have shown high predictive accuracy; however, they are often criti... | https://arxiv.org/abs/2601.02562 | Academic Papers | svg |
13e61b22dc546cf869986bc67a2841e87e52a77433a8223ac2666ef8958625d2 | 2026-01-07T00:00:00-05:00 | Compressed code: the hidden effects of quantization and distillation on programming tokens | arXiv:2601.02563v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated exceptional code generation capabilities, yet their token-level mechanisms remain underexplored, particularly in compressed models. Through systematic analysis of programming language token representations, we characterize ho... | https://arxiv.org/abs/2601.02563 | Academic Papers | svg |
9aca2eff7d42c4b23149ab90383e01586a47fb7826014d9af9faa8e742d5b6fa | 2026-01-07T00:00:00-05:00 | Shallow- and Deep-fake Image Manipulation Localization Using Vision Mamba and Guided Graph Neural Network | arXiv:2601.02566v1 Announce Type: new Abstract: Image manipulation localization is a critical research task, given that forged images may have a significant societal impact of various aspects. Such image manipulations can be produced using traditional image editing tools (known as "shallowfakes") or advanced artificial... | https://arxiv.org/abs/2601.02566 | Academic Papers | svg |
9e143143d5a3c03b15ccd0edafe9b71355adc8394aac49fdf27f537334a2e4b1 | 2026-01-07T00:00:00-05:00 | LoRA-Drop: Temporal LoRA Decoding for Efficient LLM Inference | arXiv:2601.02569v1 Announce Type: new Abstract: Autoregressive large language models (LLMs) are bottlenecked by sequential decoding, where each new token typically requires executing all transformer layers. Existing dynamic-depth and layer-skipping methods reduce this cost, but often rely on auxiliary routing mechanism... | https://arxiv.org/abs/2601.02569 | Academic Papers | svg |
1f1fa8fbf1e52cf8c815763c99891f08c96b0173e31250eed86c2502b0ae6add | 2026-01-07T00:00:00-05:00 | O-DSS: An Open Dynamic Spectrum Sharing Framework for Cellular-Radar Coexistence in Mid-band Frequencies | arXiv:2601.02571v1 Announce Type: new Abstract: The growing demand for mid-band spectrum necessitates efficient Dynamic Spectrum Sharing (DSS) to ensure coexistence between cellular networks and incumbent radar systems. Existing Spectrum Access System (SAS) frameworks rely on fixed Environmental Sensing Capability (ESC... | https://arxiv.org/abs/2601.02571 | Academic Papers | svg |
1364d8b3e00ddb0efeefbce95022e7581fc975f8ad22b567c67b5b4377f043a0 | 2026-01-07T00:00:00-05:00 | LendNova: Towards Automated Credit Risk Assessment with Language Models | arXiv:2601.02573v1 Announce Type: new Abstract: Credit risk assessment is essential in the financial sector, but has traditionally depended on costly feature-based models that often fail to utilize all available information in raw credit records. This paper introduces LendNova, the first practical automated end-to-end ... | https://arxiv.org/abs/2601.02573 | Academic Papers | svg |
c0b7b7f23956469317233130a81f468829124ce177e05051457d32a438a320d6 | 2026-01-07T00:00:00-05:00 | Fact-Checking with Large Language Models via Probabilistic Certainty and Consistency | arXiv:2601.02574v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in applications requiring factual accuracy, yet their outputs often contain hallucinated responses. While fact-checking can mitigate these errors, existing methods typically retrieve external evidence indiscriminately, ov... | https://arxiv.org/abs/2601.02574 | Academic Papers | svg |
03995e27ca74021e544cfb3d7663054fbada9269197c1522004d2a29dee6fa6f | 2026-01-07T00:00:00-05:00 | Orchestral AI: A Framework for Agent Orchestration | arXiv:2601.02577v1 Announce Type: new Abstract: The rapid proliferation of LLM agent frameworks has forced developers to choose between vendor lock-in through provider-specific SDKs and complex multi-package ecosystems that obscure control flow and hinder reproducibility. Integrating tool calling across multiple LLM pr... | https://arxiv.org/abs/2601.02577 | Academic Papers | svg |
470db73b7ccdd1c58be428942977a5bc8337a9d8f76db6654b521b33c8d893dc | 2026-01-07T00:00:00-05:00 | DataParasite Enables Scalable and Repurposable Online Data Curation | arXiv:2601.02578v1 Announce Type: new Abstract: Many questions in computational social science rely on datasets assembled from heterogeneous online sources, a process that is often labor-intensive, costly, and difficult to reproduce. Recent advances in large language models enable agentic search and structured extracti... | https://arxiv.org/abs/2601.02578 | Academic Papers | svg |
9693b51656f88f462d8404bfe85af8eb43d1eb62a4cf61c81a01562ff699a3f7 | 2026-01-07T00:00:00-05:00 | Reconstructing Item Characteristic Curves using Fine-Tuned Large Language Models | arXiv:2601.02580v1 Announce Type: new Abstract: Traditional methods for determining assessment item parameters, such as difficulty and discrimination, rely heavily on expensive field testing to collect student performance data for Item Response Theory (IRT) calibration. This study introduces a novel approach that impli... | https://arxiv.org/abs/2601.02580 | Academic Papers | svg |
d007805b8c05e9ae7560c471130d36936a3857bb34653de5426e36d3e4bbe636 | 2026-01-07T00:00:00-05:00 | Threat Detection in Social Media Networks Using Machine Learning Based Network Analysis | arXiv:2601.02581v1 Announce Type: new Abstract: The accelerated development of social media websites has posed intricate security issues in cyberspace, where these sites have increasingly become victims of criminal activities including attempts to intrude into them, abnormal traffic patterns, and organized attacks. The... | https://arxiv.org/abs/2601.02581 | Academic Papers | svg |
f3caddb48dfcc44848ac019e2e8f72e170957e98cb4ae5719ebf50860c3f3313 | 2026-01-07T00:00:00-05:00 | AI Social Responsibility as Reachability: Execution-Level Semantics for the Social Responsibility Stack | arXiv:2601.02585v1 Announce Type: new Abstract: Artificial intelligence systems are increasingly embedded as persistent, closed-loop components within cyber-physical, social, and institutional processes. Rather than producing isolated outputs, such systems operate continuously under feedback, adaptation, and scale, res... | https://arxiv.org/abs/2601.02585 | Academic Papers | svg |
84a8612d6b6def23231567d0429481ad1779a74bc6bd60dae12004df335fef19 | 2026-01-07T00:00:00-05:00 | Understanding Human Perception of Music Plagiarism Through a Computational Approach | arXiv:2601.02586v1 Announce Type: new Abstract: There is a wide variety of music similarity detection algorithms, while discussions about music plagiarism in the real world are often based on audience perceptions. Therefore, we aim to conduct a study to examine the key criteria of human perception of music plagiarism, ... | https://arxiv.org/abs/2601.02586 | Academic Papers | svg |
10d6f6c8aca3fa68c8ec75343095211f7e070b2d594a3df4a1734848602479ec | 2026-01-07T00:00:00-05:00 | FlowPlan-G2P: A Structured Generation Framework for Transforming Scientific Papers into Patent Descriptions | arXiv:2601.02589v1 Announce Type: new Abstract: Over 3.5 million patents are filed annually, with drafting patent descriptions requiring deep technical and legal expertise. Transforming scientific papers into patent descriptions is particularly challenging due to their differing rhetorical styles and stringent legal re... | https://arxiv.org/abs/2601.02589 | Academic Papers | svg |
5f335a0994beb7699370696255bf025d43153baba2f9bf3705d3ab5da1ef80d8 | 2026-01-07T00:00:00-05:00 | A Music Information Retrieval Approach to Classify Sub-Genres in Role Playing Games | arXiv:2601.02591v1 Announce Type: new Abstract: Video game music (VGM) is often studied under the same lens as film music, which largely focuses on its theoretical functionality with relation to the identified genres of the media. However, till date, we are unaware of any systematic approach that analyzes the quantifia... | https://arxiv.org/abs/2601.02591 | Academic Papers | svg |
6c3c2f950506b8398e06be05f1d86008a4db52626c175c3b1303bda2a547ae1d | 2026-01-07T00:00:00-05:00 | Volumetric locking-free Mixed Virtual Element Methods for Contact Problems | arXiv:2601.02595v1 Announce Type: new Abstract: We consider the approximation of the 2D frictionless contact problem in elasticity using the Virtual Element Methods (VEMs). To overcome the volumetric locking phenomenon in the nearly incompressible case, we adopt a mixed displacement/pressure ($u/p$) variational formula... | https://arxiv.org/abs/2601.02595 | Academic Papers | svg |
b2b498e727cfe89fd5ea28d4719968c46e1aee9ecdf4d2def66c3932772bc5d3 | 2026-01-07T00:00:00-05:00 | Coordinated Multi-Domain Deception: A Stackelberg Game Approach | arXiv:2601.02596v1 Announce Type: new Abstract: This paper explores coordinated deception strategies by synchronizing defenses across coupled cyber and physical systems to mislead attackers and strengthen defense mechanisms. We introduce a Stackelberg game framework to model the strategic interaction between defenders ... | https://arxiv.org/abs/2601.02596 | Academic Papers | svg |
bb63962487b0fe25267df414815ac364613f90e017b844725efe812c628abc07 | 2026-01-07T00:00:00-05:00 | LongDA: Benchmarking LLM Agents for Long-Document Data Analysis | arXiv:2601.02598v1 Announce Type: new Abstract: We introduce LongDA, a data analysis benchmark for evaluating LLM-based agents under documentation-intensive analytical workflows. In contrast to existing benchmarks that assume well-specified schemas and inputs, LongDA targets real-world settings in which navigating long... | https://arxiv.org/abs/2601.02598 | Academic Papers | svg |
dd69563d1e5f685c80cdfc479927af5da66f631813c34358417ecb759266edbb | 2026-01-07T00:00:00-05:00 | State of the Quantum Software Engineering Ecosystem | arXiv:2601.02601v1 Announce Type: new Abstract: We study the current state of the Quantum Software Engineering (QSE) ecosystem, focusing on the achievements, activities, and engagements from academia and industry, with a special focus on successful entrepreneurial endeavors in this arena. Our research methodology is a ... | https://arxiv.org/abs/2601.02601 | Academic Papers | svg |
ff10c6085ec9c9ae66c725d6866b5e95ff2ca04c135a4b52d1167d6090585e66 | 2026-01-07T00:00:00-05:00 | SWaRL: Safeguard Code Watermarking via Reinforcement Learning | arXiv:2601.02602v1 Announce Type: new Abstract: We present SWaRL, a robust and fidelity-preserving watermarking framework designed to protect the intellectual property of code LLM owners by embedding unique and verifiable signatures in the generated output. Existing approaches rely on manually crafted transformation ru... | https://arxiv.org/abs/2601.02602 | Academic Papers | svg |
9e65035b5eb67d4b4d7fab484cb8621ad8d42aaad3aeadab3488a4c8f758f115 | 2026-01-07T00:00:00-05:00 | Scalable Construction of a Lung Cancer Knowledge Base: Profiling Semantic Reasoning in LLMs | arXiv:2601.02604v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into biomedical research offers new opportunities for domainspecific reasoning and knowledge representation. However, their performance depends heavily on the semantic quality of training data. In oncology, where precision a... | https://arxiv.org/abs/2601.02604 | Academic Papers | svg |
9f57debffa8c6df8056ee46b220f1719e0636bf84f7dbd918f9194e036cea5ec | 2026-01-07T00:00:00-05:00 | Weights on finite fields and failures of the MacWilliams identities | arXiv:2601.02608v1 Announce Type: new Abstract: In the 1960s, MacWilliams proved that the Hamming weight enumerator of a linear code over a finite field completely determines, and is determined by, the Hamming weight enumerator of its dual code. In particular, if two linear codes have the same Hamming weight enumerator... | https://arxiv.org/abs/2601.02608 | Academic Papers | svg |
597fc592591def4d11414a1d1e3a858cd80938d6e41c09441d0e10a352f3967e | 2026-01-07T00:00:00-05:00 | Chronicals: A High-Performance Framework for LLM Fine-Tuning with 3.51x Speedup over Unsloth | arXiv:2601.02609v1 Announce Type: new Abstract: Large language model fine-tuning is bottlenecked by memory: a 7B parameter model requires 84GB--14GB for weights, 14GB for gradients, and 56GB for FP32 optimizer states--exceeding even A100-40GB capacity. We present Chronicals, an open-source training framework achieving ... | https://arxiv.org/abs/2601.02609 | Academic Papers | svg |
21cfe4598108f8440d1f6745ecc0f9a5ebd1bb362477df1324f991de5653962e | 2026-01-07T00:00:00-05:00 | Sparsity-Aware Streaming SNN Accelerator with Output-Channel Dataflow for Automatic Modulation Classification | arXiv:2601.02613v1 Announce Type: new Abstract: The rapid advancement of wireless communication technologies, including 5G, emerging 6G networks, and the large-scale deployment of the Internet of Things (IoT), has intensified the need for efficient spectrum utilization. Automatic modulation classification (AMC) plays a... | https://arxiv.org/abs/2601.02613 | Academic Papers | svg |
ec3ccbe8c7588093bd41104539411998914b7de19b0c571d135cd95e28add81a | 2026-01-07T00:00:00-05:00 | LAsset: An LLM-assisted Security Asset Identification Framework for System-on-Chip (SoC) Verification | arXiv:2601.02624v1 Announce Type: new Abstract: The growing complexity of modern system-on-chip (SoC) and IP designs is making security assurance difficult day by day. One of the fundamental steps in the pre-silicon security verification of a hardware design is the identification of security assets, as it substantially... | https://arxiv.org/abs/2601.02624 | Academic Papers | svg |
9d6f254135887cd45447cd461b4d46007155469592aa0a0f7cab0666b268db85 | 2026-01-07T00:00:00-05:00 | Improved Evidence Extraction for Document Inconsistency Detection with LLMs | arXiv:2601.02627v1 Announce Type: new Abstract: Large language models (LLMs) are becoming useful in many domains due to their impressive abilities that arise from large training datasets and large model sizes. However, research on LLM-based approaches to document inconsistency detection is relatively limited. There are... | https://arxiv.org/abs/2601.02627 | Academic Papers | svg |
fc3d52eaada7bdaeaf9ca88a919c9ff55e394a00d148d782bc7b4dbe3ae81ac7 | 2026-01-07T00:00:00-05:00 | Listen to the Unexpected: Self-Supervised Surprise Detection for Efficient Viewport Prediction | arXiv:2601.02629v1 Announce Type: new Abstract: Adaptive streaming of 360-degree video relies on viewport prediction to allocate bandwidth efficiently. Current approaches predominantly use visual saliency or historical gaze patterns, neglecting the role of spatial audio in guiding user attention. This paper presents a ... | https://arxiv.org/abs/2601.02629 | Academic Papers | svg |
0fa71878d118a0369cc1b1308c29a9cb423ffea77b0b18210c7d5c5c513f217f | 2026-01-07T00:00:00-05:00 | Copyright Laundering Through the AI Ouroboros: Adapting the 'Fruit of the Poisonous Tree' Doctrine to Recursive AI Training | arXiv:2601.02631v1 Announce Type: new Abstract: Copyright enforcement rests on an evidentiary bargain: a plaintiff must show both the defendant's access to the work and substantial similarity in the challenged output. That bargain comes under strain when AI systems are trained through multi-generational pipelines with ... | https://arxiv.org/abs/2601.02631 | Academic Papers | svg |
348751aa19782ec5a2f78f9c1d88c28455bf07fcd21f129d993b3b6d98bc4fba | 2026-01-07T00:00:00-05:00 | TAAF: A Trace Abstraction and Analysis Framework Synergizing Knowledge Graphs and LLMs | arXiv:2601.02632v1 Announce Type: new Abstract: Execution traces are a critical source of information for understanding, debugging, and optimizing complex software systems. However, traces from OS kernels or large-scale applications like Chrome or MySQL are massive and difficult to analyze. Existing tools rely on prede... | https://arxiv.org/abs/2601.02632 | Academic Papers | svg |
c06f6331100b470f01b8fb08fe4b2ba6a54049f21b10094397bb15b1d41c0b28 | 2026-01-07T00:00:00-05:00 | Fluid Agency in AI Systems: A Case for Functional Equivalence in Copyright, Patent, and Tort | arXiv:2601.02633v1 Announce Type: new Abstract: Modern Artificial Intelligence (AI) systems lack human-like consciousness or culpability, yet they exhibit fluid agency: behavior that is (i) stochastic (probabilistic and path-dependent), (ii) dynamic (co-evolving with user interaction), and (iii) adaptive (able to reori... | https://arxiv.org/abs/2601.02633 | Academic Papers | svg |
a20879951faac9820462a53e34e1be188a8514957d8a1137c8ddb03bdb7a6b0e | 2026-01-07T00:00:00-05:00 | Credit Assignment via Neural Manifold Noise Correlation | arXiv:2601.02636v1 Announce Type: new Abstract: Credit assignment--how changes in individual neurons and synapses affect a network's output--is central to learning in brains and machines. Noise correlation, which estimates gradients by correlating perturbations of activity with changes in output, provides a biologicall... | https://arxiv.org/abs/2601.02636 | Academic Papers | svg |
36a94d5cf318b3b0d7f5798c5d249ff37a19c19987acd47eaccc81a66f5251c4 | 2026-01-07T00:00:00-05:00 | An Empirical Study of On-Device Translation for Real-Time Live-Stream Chat on Mobile Devices | arXiv:2601.02641v1 Announce Type: new Abstract: Despite its efficiency, there has been little research on the practical aspects required for real-world deployment of on-device AI models, such as the device's CPU utilization and thermal conditions. In this paper, through extensive experiments, we investigate two key iss... | https://arxiv.org/abs/2601.02641 | Academic Papers | svg |
c6ce73f987c24dd2584c2cf5850abf8879c112381b8aa93c79170654b4f231a1 | 2026-01-07T00:00:00-05:00 | AWARE-US: Benchmark for Preference-Aware Resolution in Tool-Calling Agents | arXiv:2601.02643v1 Announce Type: new Abstract: Tool-calling conversational agents querying structured databases often face two linked failures: underspecification (missing constraints needed to run a precise query) and infeasibility (the fully specified query returns an empty set because no item satisfies all constrai... | https://arxiv.org/abs/2601.02643 | Academic Papers | svg |
a381c3146d79a3b7ce64416b8cb8d10e80d0e90fdebe9c49baced8ada993b779 | 2026-01-07T00:00:00-05:00 | Making Infeasible Tasks Feasible: Planning to Reconfigure Disconnected 3D Environments with Movable Objects | arXiv:2601.02645v1 Announce Type: new Abstract: Several planners have been developed to compute dynamically feasible, collision-free robot paths from an initial to a goal configuration. A key assumption in these works is that the goal region is reachable; an assumption that often fails in practice when environments are... | https://arxiv.org/abs/2601.02645 | Academic Papers | svg |
9801d8f28ee143027be0c246609c5ca11b3efcba6d268836b40c09a2b64b812a | 2026-01-07T00:00:00-05:00 | DreamLoop: Controllable Cinemagraph Generation from a Single Photograph | arXiv:2601.02646v1 Announce Type: new Abstract: Cinemagraphs, which combine static photographs with selective, looping motion, offer unique artistic appeal. Generating them from a single photograph in a controllable manner is particularly challenging. Existing image-animation techniques are restricted to simple, low-fr... | https://arxiv.org/abs/2601.02646 | Academic Papers | svg |
d8a7d3192a8734fa70ccfa4f4ac9ffa34d4bd95146ff0e34fa57c2a9e91ad571 | 2026-01-07T00:00:00-05:00 | Prioritized Replay for RL Post-training | arXiv:2601.02648v1 Announce Type: new Abstract: We introduce a problem-level prioritization framework for RL post-training of large language models. Building on insights from prioritized replay in deep RL, as well as prior observations that rollouts with intermediate success rates tend to produce stronger learning sign... | https://arxiv.org/abs/2601.02648 | Academic Papers | svg |
0f661669ee318dab2aa6a11bc161feebd7ef17adb8e760468aa7b8d25f7f34a3 | 2026-01-07T00:00:00-05:00 | Effective Online 3D Bin Packing with Lookahead Parcels Using Monte Carlo Tree Search | arXiv:2601.02649v1 Announce Type: new Abstract: Online 3D Bin Packing (3D-BP) with robotic arms is crucial for reducing transportation and labor costs in modern logistics. While Deep Reinforcement Learning (DRL) has shown strong performance, it often fails to adapt to real-world short-term distribution shifts, which ar... | https://arxiv.org/abs/2601.02649 | Academic Papers | svg |
0132788a5d0eba9392d2b9856d124259547d1650f28288cf316206cab717becd | 2026-01-07T00:00:00-05:00 | A Derivative-Free Saddle-search Algorithm With Linear Convergence Rate | arXiv:2601.02650v1 Announce Type: new Abstract: We propose a derivative-free saddle-search algorithm designed to locate transition states using only function evaluations. The algorithm employs a nested architecture consisting of an inner eigenvector search and an outer saddle-point search. Through rigorous numerical an... | https://arxiv.org/abs/2601.02650 | Academic Papers | svg |
f9bcb4f091a02c227a397b72f9e41bb70d2c943b61107d3b5708b5b3e6b4f33c | 2026-01-07T00:00:00-05:00 | Driving Accessibility: Shifting the Narrative & Design of Automated Vehicle Systems for Persons With Disabilities Through a Collaborative Scoring System | arXiv:2601.02651v1 Announce Type: new Abstract: Automated vehicles present unique opportunities and challenges, with progress and adoption limited, in part, by policy and regulatory barriers. Underrepresented groups, including individuals with mobility impairments, sensory disabilities, and cognitive conditions, who ma... | https://arxiv.org/abs/2601.02651 | Academic Papers | svg |
343939670a1c20ecdf9e801526c57f30ab0a1b8f56408b593e8e26d64449e6b9 | 2026-01-07T00:00:00-05:00 | Backwards Data-Flow Analysis using Prophecy Variable in the BuildIt System | arXiv:2601.02653v1 Announce Type: new Abstract: Many program transformations and optimizations require information about the future behavior of the program. A standard way to obtain this information is to build an intermediate program representation, then use a backwards program analysis to propagate relevant informati... | https://arxiv.org/abs/2601.02653 | Academic Papers | svg |
7f531c6d513e57c260bf1da2d82193b1052ad8f50965310b774cd44cc5a7ed67 | 2026-01-07T00:00:00-05:00 | Empirical Comparison of Encoder-Based Language Models and Feature-Based Supervised Machine Learning Approaches to Automated Scoring of Long Essays | arXiv:2601.02659v1 Announce Type: new Abstract: Long context may impose challenges for encoder-only language models in text processing, specifically for automated scoring of essays. This study trained several commonly used encoder-based language models for automated scoring of long essays. The performance of these trai... | https://arxiv.org/abs/2601.02659 | Academic Papers | svg |
08fb531ad604744c3b3c32068daf03e01f524c42d31cfd0d1e8853d7303e0c83 | 2026-01-07T00:00:00-05:00 | When Prompting Meets Spiking: Graph Sparse Prompting via Spiking Graph Prompt Learning | arXiv:2601.02662v1 Announce Type: new Abstract: Graph Prompt Feature (GPF) learning has been widely used in adapting pre-trained GNN model on the downstream task. GPFs first introduce some prompt atoms and then learns the optimal prompt vector for each graph node using the linear combination of prompt atoms. However, e... | https://arxiv.org/abs/2601.02662 | Academic Papers | svg |
a32934c5c652dc07ab3c98cf310156082fe2c224457f9bc1e23a46b7d6843c5b | 2026-01-07T00:00:00-05:00 | When Do Tools and Planning Help LLMs Think? A Cost- and Latency-Aware Benchmark | arXiv:2601.02663v1 Announce Type: new Abstract: Modern large language models (LLMs) increasingly rely on inference-time planning and external tools to improve reasoning. We benchmark this behavior on two real-world settings: event-centric question answering over graph-structured knowledge (Event-QA) and persuasive resp... | https://arxiv.org/abs/2601.02663 | Academic Papers | svg |
6485bf0d3fb04d66f39c964ee03b59389d54601bf94dc070b8aaddca615aee24 | 2026-01-07T00:00:00-05:00 | Inferring Causal Graph Temporal Logic Formulas to Expedite Reinforcement Learning in Temporally Extended Tasks | arXiv:2601.02666v1 Announce Type: new Abstract: Decision-making tasks often unfold on graphs with spatial-temporal dynamics. Black-box reinforcement learning often overlooks how local changes spread through network structure, limiting sample efficiency and interpretability. We present GTL-CIRL, a closed-loop framework ... | https://arxiv.org/abs/2601.02666 | Academic Papers | svg |
a2438e9d9194d97d74761979d6f5815dccafe9009d07f33b98ce972bc03aef04 | 2026-01-07T00:00:00-05:00 | MAFS: Multi-head Attention Feature Selection for High-Dimensional Data via Deep Fusion of Filter Methods | arXiv:2601.02668v1 Announce Type: new Abstract: Feature selection is essential for high-dimensional biomedical data, enabling stronger predictive performance, reduced computational cost, and improved interpretability in precision medicine applications. Existing approaches face notable challenges. Filter methods are hig... | https://arxiv.org/abs/2601.02668 | Academic Papers | svg |
d480c72c1813508f97e93cc3d3ba13bf21e0b2228719d3b75cb1a8b8f33e82b4 | 2026-01-07T00:00:00-05:00 | Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking | arXiv:2601.02669v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in real-world fact-checking systems, yet existing evaluations focus predominantly on claim verification and overlook the broader fact-checking workflow, including claim extraction and evidence retrieval. This narrow f... | https://arxiv.org/abs/2601.02669 | Academic Papers | svg |
48138af08940cf935c80db8f60b22632587ef16ff657b33d73fbd6c79cde3675 | 2026-01-07T00:00:00-05:00 | Multi-Turn Jailbreaking of Aligned LLMs via Lexical Anchor Tree Search | arXiv:2601.02670v1 Announce Type: new Abstract: Most jailbreak methods achieve high attack success rates (ASR) but require attacker LLMs to craft adversarial queries and/or demand high query budgets. These resource limitations make jailbreaking expensive, and the queries generated by attacker LLMs often consist of non-... | https://arxiv.org/abs/2601.02670 | Academic Papers | svg |
a35aa60f405352012416f807224ccf57bde92c21c4f58806f7aeb78228ab56bb | 2026-01-07T00:00:00-05:00 | Extracting books from production language models | arXiv:2601.02671v1 Announce Type: new Abstract: Many unresolved legal questions over LLMs and copyright center on memorization: whether specific training data have been encoded in the model's weights during training, and whether those memorized data can be extracted in the model's outputs. While many believe that LLMs ... | https://arxiv.org/abs/2601.02671 | Academic Papers | svg |
f97e4e1e4e2f0b8c28110201c033fb3a3cc6217a03a4b735a87524aca4aa69aa | 2026-01-07T00:00:00-05:00 | Iterative Structured Pruning for Large Language Models with Multi-Domain Calibration | arXiv:2601.02674v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success across a wide spectrum of natural language processing tasks. However, their ever-growing scale introduces significant barriers to real-world deployment, including substantial computational overhead, memory foot... | https://arxiv.org/abs/2601.02674 | Academic Papers | svg |
7b817252f7cfb2b2d9b0521f359037d4f556b8b2b7fa08ceedd984dbcf8effe6 | 2026-01-07T00:00:00-05:00 | Uni-FinLLM: A Unified Multimodal Large Language Model with Modular Task Heads for Micro-Level Stock Prediction and Macro-Level Systemic Risk Assessment | arXiv:2601.02677v1 Announce Type: new Abstract: Financial institutions and regulators require systems that integrate heterogeneous data to assess risks from stock fluctuations to systemic vulnerabilities. Existing approaches often treat these tasks in isolation, failing to capture cross-scale dependencies. We propose U... | https://arxiv.org/abs/2601.02677 | Academic Papers | svg |
cdf13ee7d2c3a6b61f118326537e8554e055273cf83602d08bbd48a77776f081 | 2026-01-07T00:00:00-05:00 | Adversarial Contrastive Learning for LLM Quantization Attacks | arXiv:2601.02680v1 Announce Type: new Abstract: Model quantization is critical for deploying large language models (LLMs) on resource-constrained hardware, yet recent work has revealed severe security risks that benign LLMs in full precision may exhibit malicious behaviors after quantization. In this paper, we propose ... | https://arxiv.org/abs/2601.02680 | Academic Papers | svg |
444a19d47ed309a5e0f3d88da89d476111c537b32c45c3a02a07dc3e4cda0f37 | 2026-01-07T00:00:00-05:00 | Topology-Independent Robustness of the Weighted Mean under Label Poisoning Attacks in Heterogeneous Decentralized Learning | arXiv:2601.02682v1 Announce Type: new Abstract: Robustness to malicious attacks is crucial for practical decentralized signal processing and machine learning systems. A typical example of such attacks is label poisoning, meaning that some agents possess corrupted local labels and share models trained on these poisoned ... | https://arxiv.org/abs/2601.02682 | Academic Papers | svg |
54fd4e60db380481ae05805784e4fc50f14ae32457f9fb89dc8a0d1e134613a5 | 2026-01-07T00:00:00-05:00 | Learning from Prompt itself: the Hierarchical Attribution Prompt Optimization | arXiv:2601.02683v1 Announce Type: new Abstract: Optimization is fundamental across numerous disciplines, typically following an iterative process of refining an initial solution to enhance performance. This principle is equally critical in prompt engineering, where designing effective prompts for large language models ... | https://arxiv.org/abs/2601.02683 | Academic Papers | svg |
cdef903ae3593020a5835ed30d81ac059f5dd6d7cb123368c3cecc0b0de0ff19 | 2026-01-07T00:00:00-05:00 | Learning to Nudge: A Scalable Barrier Function Framework for Safe Robot Interaction in Dense Clutter | arXiv:2601.02686v1 Announce Type: new Abstract: Robots operating in everyday environments must navigate and manipulate within densely cluttered spaces, where physical contact with surrounding objects is unavoidable. Traditional safety frameworks treat contact as unsafe, restricting robots to collision avoidance and lim... | https://arxiv.org/abs/2601.02686 | Academic Papers | svg |
4362eb8696924e8f79511b634249141ebc091535f89f34714686ecaa99c6e74f | 2026-01-07T00:00:00-05:00 | Multi-channel multi-speaker transformer for speech recognition | arXiv:2601.02688v1 Announce Type: new Abstract: With the development of teleconferencing and in-vehicle voice assistants, far-field multi-speaker speech recognition has become a hot research topic. Recently, a multi-channel transformer (MCT) has been proposed, which demonstrates the ability of the transformer to model ... | https://arxiv.org/abs/2601.02688 | Academic Papers | svg |
ca99f424f2fe8b3dad0c68c52528a4b2d75fdc05d4571f65d70dd96e4a1b2999 | 2026-01-07T00:00:00-05:00 | Which Deep Learner? A Systematic Evaluation of Advanced Deep Forecasting Models Accuracy and Efficiency for Network Traffic Prediction | arXiv:2601.02694v1 Announce Type: new Abstract: Network traffic prediction is essential for automating modern network management. It is a difficult time series forecasting (TSF) problem that has been addressed by Deep Learning (DL) models due to their ability to capture complex patterns. Advances in forecasting, from s... | https://arxiv.org/abs/2601.02694 | Academic Papers | svg |
0746287a74a448dafd23d3c2e9fc6a910cdb582fbea2cfeddcf20d4b54cada9f | 2026-01-07T00:00:00-05:00 | EvoRoute: Experience-Driven Self-Routing LLM Agent Systems | arXiv:2601.02695v1 Announce Type: new Abstract: Complex agentic AI systems, powered by a coordinated ensemble of Large Language Models (LLMs), tool and memory modules, have demonstrated remarkable capabilities on intricate, multi-turn tasks. However, this success is shadowed by prohibitive economic costs and severe lat... | https://arxiv.org/abs/2601.02695 | Academic Papers | svg |
b22080b223720b761431cb2193c9ca849c775ad7689797636b749aeb333cad26 | 2026-01-07T00:00:00-05:00 | Boosting Accuracy and Interpretability in Multilingual Hate Speech Detection Through Layer Freezing and Explainable AI | arXiv:2601.02697v1 Announce Type: new Abstract: Sentiment analysis focuses on identifying the emotional polarity expressed in textual data, typically categorized as positive, negative, or neutral. Hate speech detection, on the other hand, aims to recognize content that incites violence, discrimination, or hostility tow... | https://arxiv.org/abs/2601.02697 | Academic Papers | svg |
be0ea18d07fbd01734e1b8ea3bcf47b6cf8446b04b2b9227fb712c7524283894 | 2026-01-07T00:00:00-05:00 | Enterprise Identity Integration for AI-Assisted Developer Services: Architecture, Implementation, and Case Study | arXiv:2601.02698v1 Announce Type: new Abstract: AI-assisted developer services are increasingly embedded in modern IDEs, yet enterprises must ensure these tools operate within existing identity, access control, and governance requirements. The Model Context Protocol (MCP) enables AI assistants to retrieve structured in... | https://arxiv.org/abs/2601.02698 | Academic Papers | svg |
250518c204a3dbb5f091f3fa370982205cafc70b1ef8a2ff6a7dd376f7633c2c | 2026-01-07T00:00:00-05:00 | Adversarial Question Answering Robustness: A Multi-Level Error Analysis and Mitigation Study | arXiv:2601.02700v1 Announce Type: new Abstract: Question answering (QA) systems achieve impressive performance on standard benchmarks like SQuAD, but remain vulnerable to adversarial examples. This project investigates the adversarial robustness of transformer models on the AddSent adversarial dataset through systemati... | https://arxiv.org/abs/2601.02700 | Academic Papers | svg |
d6dc0413be11a3ba19ff1da9c18dde3c41a4e2adfec672951c45937258731fe0 | 2026-01-07T00:00:00-05:00 | Topology-Aware Spatio-Temporal Graph Transformer for Predicting Smart Grid Failures | arXiv:2601.02701v1 Announce Type: new Abstract: Smart grid infrastructure needs improved resilience and preventive maintenance through more accurate predictions. Current methodologies lack accurate representation of spatio-temporal-causal interdependencies and class imbalance in failure prediction tasks. This study int... | https://arxiv.org/abs/2601.02701 | Academic Papers | svg |
95d373e9679c14b041f58825fc768f245445b3ebe050713a25d6242fb6718654 | 2026-01-07T00:00:00-05:00 | Learning User Preferences Through Interaction for Long-Term Collaboration | arXiv:2601.02702v1 Announce Type: new Abstract: As conversational agents accumulate experience collaborating with users, adapting to user preferences is essential for fostering long-term relationships and improving collaboration quality over time. We introduce MultiSessionCollab, a benchmark that evaluates how well age... | https://arxiv.org/abs/2601.02702 | Academic Papers | svg |
fccdb08bae7b73c85414f963c674502e23823f893a4d8676b375cced3fdfe41a | 2026-01-07T00:00:00-05:00 | Exact Constructive Digit-by-Digit Algorithms for Integer $e$-th Root Extraction | arXiv:2601.02703v1 Announce Type: new Abstract: We present a unified constructive digit-by-digit framework for exact root extraction using only integer arithmetic. The core contribution is a complete correctness theory for the fractional square root algorithm, proving that each computed decimal digit is exact and final... | https://arxiv.org/abs/2601.02703 | Academic Papers | svg |
4c334d14eead97418e88bf7996cad41e375280d37991bcd08c393ff964393fed | 2026-01-07T00:00:00-05:00 | Analysis of Various Manipulator Configurations Based on Multi-Objective Black-Box Optimization | arXiv:2601.02704v1 Announce Type: new Abstract: Various 6-degree-of-freedom (DOF) and 7-DOF manipulators have been developed to date. Over a long history, their joint configurations and link length ratios have been determined empirically. In recent years, the development of robotic foundation models has become increasi... | https://arxiv.org/abs/2601.02704 | Academic Papers | svg |
abb93af2254f3378af7aeec05a3d7cb81a667d1d60316c9793a5bcd38255be39 | 2026-01-07T00:00:00-05:00 | Scaling Laws of Machine Learning for Optimal Power Flow | arXiv:2601.02706v1 Announce Type: new Abstract: Optimal power flow (OPF) is one of the fundamental tasks for power system operations. While machine learning (ML) approaches such as deep neural networks (DNNs) have been widely studied to enhance OPF solution speed and performance, their practical deployment faces two cr... | https://arxiv.org/abs/2601.02706 | Academic Papers | svg |
14e90ffdef69e63a504fed37427628c3405e07a2e825f35767ebd93dc2cf4f97 | 2026-01-07T00:00:00-05:00 | CREAM: Continual Retrieval on Dynamic Streaming Corpora with Adaptive Soft Memory | arXiv:2601.02708v1 Announce Type: new Abstract: Information retrieval (IR) in dynamic data streams is emerging as a challenging task, as shifts in data distribution degrade the performance of AI-powered IR systems. To mitigate this issue, memory-based continual learning has been widely adopted for IR. However, existing... | https://arxiv.org/abs/2601.02708 | Academic Papers | svg |
64396dc7c3e523fbd8c540b23deda768cef08b299b55680ea0d27aff3ca03eaf | 2026-01-07T00:00:00-05:00 | GRRE: Leveraging G-Channel Removed Reconstruction Error for Robust Detection of AI-Generated Images | arXiv:2601.02709v1 Announce Type: new Abstract: The rapid progress of generative models, particularly diffusion models and GANs, has greatly increased the difficulty of distinguishing synthetic images from real ones. Although numerous detection methods have been proposed, their accuracy often degrades when applied to i... | https://arxiv.org/abs/2601.02709 | Academic Papers | svg |
c3bc0ae0aae25a104502e9795862833f7960e7d6d732fc300373de36f9f26b5c | 2026-01-07T00:00:00-05:00 | Time-Scaling Is What Agents Need Now | arXiv:2601.02714v1 Announce Type: new Abstract: Early artificial intelligence paradigms exhibited separated cognitive functions: Neural Networks focused on "perception-representation," Reinforcement Learning on "decision-making-behavior," and Symbolic AI on "knowledge-reasoning." With Transformer-based large models and... | https://arxiv.org/abs/2601.02714 | Academic Papers | svg |
6b89b151f7d3ec39c325c778659c6bfe9bd0ff371dd9be70cb0e97a0843bfe4a | 2026-01-07T00:00:00-05:00 | CAMO: Category-Agnostic 3D Motion Transfer from Monocular 2D Videos | arXiv:2601.02716v1 Announce Type: new Abstract: Motion transfer from 2D videos to 3D assets is a challenging problem, due to inherent pose ambiguities and diverse object shapes, often requiring category-specific parametric templates. We propose CAMO, a category-agnostic framework that transfers motion to diverse target... | https://arxiv.org/abs/2601.02716 | Academic Papers | svg |
fdd2c5a8056c916c0e2ce336f0c94e93ba8cfd33ce1976e3c23f8035e123e86f | 2026-01-07T00:00:00-05:00 | Privacy-Preserving AI-Enabled Decentralized Learning and Employment Records System | arXiv:2601.02720v1 Announce Type: new Abstract: Learning and Employment Record (LER) systems are emerging as critical infrastructure for securely compiling and sharing educational and work achievements. Existing blockchain-based platforms leverage verifiable credentials but typically lack automated skill-credential gen... | https://arxiv.org/abs/2601.02720 | Academic Papers | svg |
ce9e3299fceacc2884d802fbf3c047404f8db49de901f2d2af82167f6b980948 | 2026-01-07T00:00:00-05:00 | Robust Mesh Saliency GT Acquisition in VR via View Cone Sampling and Geometric Smoothing | arXiv:2601.02721v1 Announce Type: new Abstract: Reliable 3D mesh saliency ground truth (GT) is essential for human-centric visual modeling in virtual reality (VR). However, current 3D mesh saliency GT acquisition methods are generally consistent with 2D image methods, ignoring the differences between 3D geometry topolo... | https://arxiv.org/abs/2601.02721 | Academic Papers | svg |
31bd94bb1adf05269ffc37a8039932ebdf261210f6f9e612d02d205f76759db3 | 2026-01-07T00:00:00-05:00 | Loop Closure using AnyLoc Visual Place Recognition in DPV-SLAM | arXiv:2601.02723v1 Announce Type: new Abstract: Loop closure is crucial for maintaining the accuracy and consistency of visual SLAM. We propose a method to improve loop closure performance in DPV-SLAM. Our approach integrates AnyLoc, a learning-based visual place recognition technique, as a replacement for the classica... | https://arxiv.org/abs/2601.02723 | Academic Papers | svg |
67fda2dba9b6cf5eb56b5c1e142df9a3fff8911e89e3d8843e5522e4ea1ba294 | 2026-01-07T00:00:00-05:00 | Foreground-Aware Dataset Distillation via Dynamic Patch Selection | arXiv:2601.02727v1 Announce Type: new Abstract: In this paper, we propose a foreground-aware dataset distillation method that enhances patch selection in a content-adaptive manner. With the rising computational cost of training large-scale deep models, dataset distillation has emerged as a promising approach for constr... | https://arxiv.org/abs/2601.02727 | Academic Papers | svg |
95614e5c3ab218f4d946326b052ab34ba9fc04ec59c1d8bd6f33df58ede2f2bb | 2026-01-07T00:00:00-05:00 | CRoPE: Efficient Parametrization of Rotary Positional Embedding | arXiv:2601.02728v1 Announce Type: new Abstract: Rotary positional embedding has become the state-of-the-art approach to encode position information in transformer-based models. While it is often succinctly expressed in complex linear algebra, we note that the actual implementation of $Q/K/V$-projections is not equivale... | https://arxiv.org/abs/2601.02728 | Academic Papers | svg |
733b93333eefa1d1fffc664a2cf0d77579679ecb39e19dd1890df9c5ce7a8179 | 2026-01-07T00:00:00-05:00 | HOLO: Homography-Guided Pose Estimator Network for Fine-Grained Visual Localization on SD Maps | arXiv:2601.02730v1 Announce Type: new Abstract: Visual localization on standard-definition (SD) maps has emerged as a promising low-cost and scalable solution for autonomous driving. However, existing regression-based approaches often overlook inherent geometric priors, resulting in suboptimal training efficiency and l... | https://arxiv.org/abs/2601.02730 | Academic Papers | svg |
e322b5a7592aee45753f62f224208545b821b16463e2e68a8645f85ef80da578 | 2026-01-07T00:00:00-05:00 | Omni2Sound: Towards Unified Video-Text-to-Audio Generation | arXiv:2601.02731v1 Announce Type: new Abstract: Training a unified model integrating video-to-audio (V2A), text-to-audio (T2A), and joint video-text-to-audio (VT2A) generation offers significant application flexibility, yet faces two unexplored foundational challenges: (1) the scarcity of high-quality audio captions wi... | https://arxiv.org/abs/2601.02731 | Academic Papers | svg |
38fcc630c991b2c62e45ed5891b0c95cf9ef5d289333433e28f335c533067a0e | 2026-01-07T00:00:00-05:00 | Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices | arXiv:2601.02732v1 Announce Type: new Abstract: As contemporary microservice systems become increasingly popular and complex-often comprising hundreds or even thousands of fine-grained, interdependent subsystems-they are experiencing more frequent failures. Ensuring system reliability thus demands accurate root cause l... | https://arxiv.org/abs/2601.02732 | Academic Papers | svg |
d58da2defdc5db51af987374119e712e21963f23dd3f16bc887119d373f0ea73 | 2026-01-07T00:00:00-05:00 | Scalable Tree Ensemble Proximities in Python | arXiv:2601.02735v1 Announce Type: new Abstract: Tree ensemble methods such as Random Forests naturally induce supervised similarity measures through their decision tree structure, but existing implementations of proximities derived from tree ensembles typically suffer from quadratic time or memory complexity, limiting ... | https://arxiv.org/abs/2601.02735 | Academic Papers | svg |
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