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5a0910f768dfb9580e37f7b59447bb3953e1747a7d687dfaea1d138ac380463b | 2026-01-07T00:00:00-05:00 | Accurate Table Question Answering with Accessible LLMs | arXiv:2601.03137v1 Announce Type: new Abstract: Given a table T in a database and a question Q in natural language, the table question answering (TQA) task aims to return an accurate answer to Q based on the content of T. Recent state-of-the-art solutions leverage large language models (LLMs) to obtain high-quality ans... | https://arxiv.org/abs/2601.03137 | Academic Papers | svg |
aa19daeed21ce3ca8572edb0c587aa3c28c7b349c6a236e8070106ab2a957f10 | 2026-01-07T00:00:00-05:00 | Time-Varying Kinematics Control for Magnetically-Actuated Satellite Swarm without Additional Actuator | arXiv:2601.03143v1 Announce Type: new Abstract: Electromagnetic Formation Flight is a technology that uses electromagnetic forces and torques to control multiple satellites without conventional fuel-based propulsion. In this paper, the controllability of the system is discussed based on the conservation of the entire s... | https://arxiv.org/abs/2601.03143 | Academic Papers | svg |
bce9c5ef0896c3203b399a75617921b726496c39716e78fa4b39384b8546e2c4 | 2026-01-07T00:00:00-05:00 | Self-Verification is All You Need To Pass The Japanese Bar Examination | arXiv:2601.03144v1 Announce Type: new Abstract: Despite rapid advances in large language models (LLMs), achieving reliable performance on highly professional and structured examinations remains a significant challenge. The Japanese bar examination is a particularly demanding benchmark, requiring not only advanced legal... | https://arxiv.org/abs/2601.03144 | Academic Papers | svg |
2d87ab8f0dadb8a91b0cf6d6bc19f91233a329d47811fdc854474e4b3ccb1a23 | 2026-01-07T00:00:00-05:00 | PersonaLedger: Generating Realistic Financial Transactions with Persona Conditioned LLMs and Rule Grounded Feedback | arXiv:2601.03149v1 Announce Type: new Abstract: Strict privacy regulations limit access to real transaction data, slowing open research in financial AI. Synthetic data can bridge this gap, but existing generators do not jointly achieve behavioral diversity and logical groundedness. Rule-driven simulators rely on hand-c... | https://arxiv.org/abs/2601.03149 | Academic Papers | svg |
22083ec4a596cef1bfd6e6d760ecc4428fd040133e20041c0ae967e5d83effd6 | 2026-01-07T00:00:00-05:00 | Conditioning Aircraft Trajectory Prediction on Meteorological Data with a Physics-Informed Machine Learning Approach | arXiv:2601.03152v1 Announce Type: new Abstract: Accurate aircraft trajectory prediction (TP) in air traffic management systems is confounded by a number of epistemic uncertainties, dominated by uncertain meteorological conditions and operator specific procedures. Handling this uncertainty necessitates the use of probab... | https://arxiv.org/abs/2601.03152 | Academic Papers | svg |
3e6c5054e94e4f0095eb3b497255eb43214fa2c15b87127afb80b67d3d894b31 | 2026-01-07T00:00:00-05:00 | Parallel Latent Reasoning for Sequential Recommendation | arXiv:2601.03153v1 Announce Type: new Abstract: Capturing complex user preferences from sparse behavioral sequences remains a fundamental challenge in sequential recommendation. Recent latent reasoning methods have shown promise by extending test-time computation through multi-step reasoning, yet they exclusively rely ... | https://arxiv.org/abs/2601.03153 | Academic Papers | svg |
9d2c431671e735ec221dfbc71cd4e12119ab7666895933791820b974af9d0775 | 2026-01-07T00:00:00-05:00 | Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective | arXiv:2601.03154v1 Announce Type: new Abstract: Reasoning-tuned LLMs utilizing long Chain-of-Thought (CoT) excel at single-answer tasks, yet their ability to model Human Label Variation--which requires capturing probabilistic ambiguity rather than resolving it--remains underexplored. We investigate this through systema... | https://arxiv.org/abs/2601.03154 | Academic Papers | svg |
c75f64cd6302bc5286d58ce837f74fba7a9f7b298b0374b407a1a461266a5e9a | 2026-01-07T00:00:00-05:00 | Prompt-Counterfactual Explanations for Generative AI System Behavior | arXiv:2601.03156v1 Announce Type: new Abstract: As generative AI systems become integrated into real-world applications, organizations increasingly need to be able to understand and interpret their behavior. In particular, decision-makers need to understand what causes generative AI systems to exhibit specific output c... | https://arxiv.org/abs/2601.03156 | Academic Papers | svg |
8b18752d9d83d31c17720c99559b1775343224932624e87de4ee94ff3cf5db4d | 2026-01-07T00:00:00-05:00 | Rapid Augmentations for Time Series (RATS): A High-Performance Library for Time Series Augmentation | arXiv:2601.03159v1 Announce Type: new Abstract: Time series augmentation is critical for training robust deep learning models, particularly in domains where labelled data is scarce and expensive to obtain. However, existing augmentation libraries for time series, mainly written in Python, suffer from performance bottle... | https://arxiv.org/abs/2601.03159 | Academic Papers | svg |
221bdcf0814924f3cd01c26db077c0c1e30dd7eb56036a645632c6692766f3ca | 2026-01-07T00:00:00-05:00 | Stability, convergence, and geometric properties of second-order-in-time space-time discretizations for linear and semilinear wave equations | arXiv:2601.03160v1 Announce Type: new Abstract: We revisit second-order-in-time space-time discretizations of the linear and semilinear wave equations by establishing precise equivalences with first-order-in-time formulations. Focusing on schemes using continuous piecewise-polynomial trial functions in time, we analyze... | https://arxiv.org/abs/2601.03160 | Academic Papers | svg |
1562cdc02174bedd5a64b5d1ac058201fd235190023dd5f0169a238e595906d7 | 2026-01-07T00:00:00-05:00 | On the Convergence Behavior of Preconditioned Gradient Descent Toward the Rich Learning Regime | arXiv:2601.03162v1 Announce Type: new Abstract: Spectral bias, the tendency of neural networks to learn low frequencies first, can be both a blessing and a curse. While it enhances the generalization capabilities by suppressing high-frequency noise, it can be a limitation in scientific tasks that require capturing fine... | https://arxiv.org/abs/2601.03162 | Academic Papers | svg |
38c1625620f8f8b88b25b501f4ddda92d45a01aebfdf709841b4b3c337da198e | 2026-01-07T00:00:00-05:00 | LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole Slide Images | arXiv:2601.03163v1 Announce Type: new Abstract: Precise and scalable instance segmentation of cell nuclei is essential for computational pathology, yet gigapixel Whole-Slide Images pose major computational challenges. Existing approaches rely on patch-based processing and costly post-processing for instance separation,... | https://arxiv.org/abs/2601.03163 | Academic Papers | svg |
8cc4c72b9d3637fda8a0679499c39f8ef6ca1c8acde4a9e66ec555f470f75559 | 2026-01-07T00:00:00-05:00 | WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning | arXiv:2601.03164v1 Announce Type: new Abstract: Large Language Model(LLM)-based agents have shown strong capabilities in web information seeking, with reinforcement learning (RL) becoming a key optimization paradigm. However, planning remains a bottleneck, as existing methods struggle with long-horizon strategies. Our ... | https://arxiv.org/abs/2601.03164 | Academic Papers | svg |
eb980f1392d9fc691bd4899f1e1f1b0575fac86095cda0e270814197c3f26d3a | 2026-01-07T00:00:00-05:00 | On the Euclidean duals of the cyclic codes generated by cyclotomic polynomials | arXiv:2601.03165v1 Announce Type: new Abstract: In this article, we determine the minimum distance of the Euclidean dual of the cyclic code $\mathcal{C}_n$ generated by the $n$th cyclotomic polynomial $Q_n(x)$ over $\mathbb{F}_q$, for every positive integer $n$ co-prime to $q$. In particular, we prove that the minimum ... | https://arxiv.org/abs/2601.03165 | Academic Papers | svg |
e7c03eba70fc526f200547407f3bcd3ec75746218070dafce91839397c43a93b | 2026-01-07T00:00:00-05:00 | Dynamic Hyperparameter Importance for Efficient Multi-Objective Optimization | arXiv:2601.03166v1 Announce Type: new Abstract: Choosing a suitable ML model is a complex task that can depend on several objectives, e.g., accuracy, model size, fairness, inference time, or energy consumption. In practice, this requires trading off multiple, often competing, objectives through multi-objective optimiza... | https://arxiv.org/abs/2601.03166 | Academic Papers | svg |
0ddaa8de2afc6113fae79128b8f66701cbc870fe8500ed547744b965e8fbafcf | 2026-01-07T00:00:00-05:00 | Can Embedding Similarity Predict Cross-Lingual Transfer? A Systematic Study on African Languages | arXiv:2601.03168v1 Announce Type: new Abstract: Cross-lingual transfer is essential for building NLP systems for low-resource African languages, but practitioners lack reliable methods for selecting source languages. We systematically evaluate five embedding similarity metrics across 816 transfer experiments spanning t... | https://arxiv.org/abs/2601.03168 | Academic Papers | svg |
260cea035e255443ccbb50b6ac8a8a114f51ed234e6af37b246aa333637d138b | 2026-01-07T00:00:00-05:00 | Segment-Aware Conditioning for Training-Free Intra-Utterance Emotion and Duration Control in Text-to-Speech | arXiv:2601.03170v1 Announce Type: new Abstract: While controllable Text-to-Speech (TTS) has achieved notable progress, most existing methods remain limited to inter-utterance-level control, making fine-grained intra-utterance expression challenging due to their reliance on non-public datasets or complex multi-stage tra... | https://arxiv.org/abs/2601.03170 | Academic Papers | svg |
9d7beb2b014403fd812add6d4d2b0f27b1c8a5c7d36fa800e635bf232504eeac | 2026-01-07T00:00:00-05:00 | Eco-WakeLoc: An Energy-Neutral and Cooperative UWB Real-Time Locating System | arXiv:2601.03171v1 Announce Type: new Abstract: Indoor localization systems face a fundamental trade-off between efficiency and responsiveness, which is especially important for emerging use cases such as mobile robots operating in GPS-denied environments. Traditional RTLS either require continuously powered infrastruc... | https://arxiv.org/abs/2601.03171 | Academic Papers | svg |
1a4599bdf383dd9ed60e208c644a95cb9d88e585202995afe12ccb7d8ea0af1c | 2026-01-07T00:00:00-05:00 | Predicting Time Pressure of Powered Two-Wheeler Riders for Proactive Safety Interventions | arXiv:2601.03173v1 Announce Type: new Abstract: Time pressure critically influences risky maneuvers and crash proneness among powered two-wheeler riders, yet its prediction remains underexplored in intelligent transportation systems. We present a large-scale dataset of 129,000+ labeled multivariate time-series sequence... | https://arxiv.org/abs/2601.03173 | Academic Papers | svg |
113dac47a04888a13e352bfb3bcaa76f689c4b8fa6e1a4e3d83337d1509e1768 | 2026-01-07T00:00:00-05:00 | DiffBench Meets DiffAgent: End-to-End LLM-Driven Diffusion Acceleration Code Generation | arXiv:2601.03178v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success in image and video generation. However, their inherently multiple step inference process imposes substantial computational overhead, hindering real-world deployment. Accelerating diffusion models is therefore essential, ye... | https://arxiv.org/abs/2601.03178 | Academic Papers | svg |
f3554c449dd4672704225fc84712978dfab4b143ad7ef019e705faebde333cc8 | 2026-01-07T00:00:00-05:00 | Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey | arXiv:2601.03181v1 Announce Type: new Abstract: Foundation models (FMs) are recognized as a transformative breakthrough that has started to reshape the future of artificial intelligence (AI) across both academia and industry. The integration of FMs into wireless networks is expected to enable the development of general... | https://arxiv.org/abs/2601.03181 | Academic Papers | svg |
6b46b84a530f7a24db5ba6cc86e039f28447424157a7045e29c103b2d9903b7a | 2026-01-07T00:00:00-05:00 | Decentralized Autoregressive Generation | arXiv:2601.03184v1 Announce Type: new Abstract: We present a theoretical analysis of decentralization of autoregressive generation. We define the Decentralized Discrete Flow Matching objective, by expressing probability generating velocity as a linear combination of expert flows. We also conduct experiments demonstrat-... | https://arxiv.org/abs/2601.03184 | Academic Papers | svg |
d626f9e854401a12cf437137a8f000e6ab83e960392f2b08dfbf1df559059b72 | 2026-01-07T00:00:00-05:00 | TaNG: Modeling Packet Classification with TSS-assisted Neural Networks on GPUs | arXiv:2601.03187v1 Announce Type: new Abstract: Packet classification is a core function in software-defined networks, and learning-based methods have recently shown significant throughput gains on large-scale rulesets. However, existing learning-based approaches struggle with overlapping rules, leading to incomplete m... | https://arxiv.org/abs/2601.03187 | Academic Papers | svg |
b5b03c2bd7649f78a07758199567acad453d32074318a69519474d2da16de7ee | 2026-01-07T00:00:00-05:00 | Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning | arXiv:2601.03190v1 Announce Type: new Abstract: Machine unlearning aims to forget sensitive knowledge from Large Language Models (LLMs) while maintaining general utility. However, existing approaches typically treat all tokens in a response indiscriminately and enforce uncertainty over the entire vocabulary. This globa... | https://arxiv.org/abs/2601.03190 | Academic Papers | svg |
829136a0f76d8737f226842aa6b6641a1d21d925cf455e08cfc5da03f0d0a1f8 | 2026-01-07T00:00:00-05:00 | AnatomiX, an Anatomy-Aware Grounded Multimodal Large Language Model for Chest X-Ray Interpretation | arXiv:2601.03191v1 Announce Type: new Abstract: Multimodal medical large language models have shown impressive progress in chest X-ray interpretation but continue to face challenges in spatial reasoning and anatomical understanding. Although existing grounding techniques improve overall performance, they often fail to ... | https://arxiv.org/abs/2601.03191 | Academic Papers | svg |
e9f1d546ba584a2dc40c0c7460ae201c219f82b8d907c52256e6e447e196dfb2 | 2026-01-07T00:00:00-05:00 | MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory | arXiv:2601.03192v1 Announce Type: new Abstract: The hallmark of human intelligence is the ability to master new skills through Constructive Episodic Simulation-retrieving past experiences to synthesize solutions for novel tasks. While Large Language Models possess strong reasoning capabilities, they struggle to emulate... | https://arxiv.org/abs/2601.03192 | Academic Papers | svg |
5e735396554649404015daebafca825b20aef1ada5d3442c694573394c9376a2 | 2026-01-07T00:00:00-05:00 | UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision | arXiv:2601.03193v1 Announce Type: new Abstract: While Unified Multimodal Models (UMMs) have achieved remarkable success in cross-modal comprehension, a significant gap persists in their ability to leverage such internal knowledge for high-quality generation. We formalize this discrepancy as Conduction Aphasia, a phenom... | https://arxiv.org/abs/2601.03193 | Academic Papers | svg |
1a9db7ea49fd22c6ef79dccb31a519478538c9bbf60d495f248583b19b0a63e8 | 2026-01-07T00:00:00-05:00 | X-MuTeST: A Multilingual Benchmark for Explainable Hate Speech Detection and A Novel LLM-consulted Explanation Framework | arXiv:2601.03194v1 Announce Type: new Abstract: Hate speech detection on social media faces challenges in both accuracy and explainability, especially for underexplored Indic languages. We propose a novel explainability-guided training framework, X-MuTeST (eXplainable Multilingual haTe Speech deTection), for hate speec... | https://arxiv.org/abs/2601.03194 | Academic Papers | svg |
7c96e7c472c342b3682895d0ab2173fb3b6b11ffb1f4ec4df31664f0ca8e7cb3 | 2026-01-07T00:00:00-05:00 | Sparse Knowledge Distillation: A Mathematical Framework for Probability-Domain Temperature Scaling and Multi-Stage Compression | arXiv:2601.03195v1 Announce Type: new Abstract: We develop a unified theoretical framework for sparse knowledge distillation based on probability-domain softening operators. While the equivalence $p^{1/T} \propto \mathrm{softmax}(z/T)$ is well known, our contribution is an operator-level analytical framework built on t... | https://arxiv.org/abs/2601.03195 | Academic Papers | svg |
47ff6cbf06eb26f107dc88c218b6039a594d93e466b8e91ee68137315d779c88 | 2026-01-07T00:00:00-05:00 | Software-Defined Agentic Serving | arXiv:2601.03197v1 Announce Type: new Abstract: As multi-agent LLM pipelines grow in complexity, existing serving paradigms fail to adapt to the dynamic serving conditions. We argue that agentic serving systems should be programmable and system-aware, unlike existing serving which statically encode the parameters. In t... | https://arxiv.org/abs/2601.03197 | Academic Papers | svg |
c00baafd2b7f2d3e88df9557c1f58ecb6e15cfea177e75f8a81c6d4c75151933 | 2026-01-07T00:00:00-05:00 | Empowering Reliable Visual-Centric Instruction Following in MLLMs | arXiv:2601.03198v1 Announce Type: new Abstract: Evaluating the instruction-following (IF) capabilities of Multimodal Large Language Models (MLLMs) is essential for rigorously assessing how faithfully model outputs adhere to user-specified intentions. Nevertheless, existing benchmarks for evaluating MLLMs' instruction-f... | https://arxiv.org/abs/2601.03198 | Academic Papers | svg |
7db6a02e216582b6fa3e9981eea8b29d834965c32aa7ac489b4df52666a018d2 | 2026-01-07T00:00:00-05:00 | DIP: Dynamic In-Context Planner For Diffusion Language Models | arXiv:2601.03199v1 Announce Type: new Abstract: Diffusion language models (DLMs) have shown strong potential for general natural language tasks with in-context examples. However, due to the bidirectional attention mechanism, DLMs incur substantial computational cost as context length increases. This work addresses this... | https://arxiv.org/abs/2601.03199 | Academic Papers | svg |
3c0108ec8d029d13e3ba98df427ff7ec56a943f2218f4f26a432757d9d2dc376 | 2026-01-07T00:00:00-05:00 | A High-Fidelity Digital Twin for Robotic Manipulation Based on 3D Gaussian Splatting | arXiv:2601.03200v1 Announce Type: new Abstract: Developing high-fidelity, interactive digital twins is crucial for enabling closed-loop motion planning and reliable real-world robot execution, which are essential to advancing sim-to-real transfer. However, existing approaches often suffer from slow reconstruction, limi... | https://arxiv.org/abs/2601.03200 | Academic Papers | svg |
10cbcf09d541dc29c9c914e3b54f4060b14e411f3f1276bc0d44b75aa1547aa3 | 2026-01-07T00:00:00-05:00 | Recursive querying of neural networks via weighted structures | arXiv:2601.03201v1 Announce Type: new Abstract: Expressive querying of machine learning models - viewed as a form of intentional data - enables their verification and interpretation using declarative languages, thereby making learned representations of data more accessible. Motivated by the querying of feedforward neur... | https://arxiv.org/abs/2601.03201 | Academic Papers | svg |
49495ac0ff57c76f7f47462672ab10535d244213023832cee380265bca0bd45a | 2026-01-07T00:00:00-05:00 | Counterfactual Fairness with Graph Uncertainty | arXiv:2601.03203v1 Announce Type: new Abstract: Evaluating machine learning (ML) model bias is key to building trustworthy and robust ML systems. Counterfactual Fairness (CF) audits allow the measurement of bias of ML models with a causal framework, yet their conclusions rely on a single causal graph that is rarely kno... | https://arxiv.org/abs/2601.03203 | Academic Papers | svg |
90cb3756e1a97edb85232a302dc182cff864fcd3882363755e61cb31f70a0271 | 2026-01-07T00:00:00-05:00 | InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agents | arXiv:2601.03204v1 Announce Type: new Abstract: LLM agents can reason and use tools, but they often break down on long-horizon tasks due to unbounded context growth and accumulated errors. Common remedies such as context compression or retrieval-augmented prompting introduce trade-offs between information fidelity and ... | https://arxiv.org/abs/2601.03204 | Academic Papers | svg |
35f78b9f8fae6b24ccaa8d16aef143b8e0938381c7e94c64b6231b5106e47350 | 2026-01-07T00:00:00-05:00 | UltraLogic: Enhancing LLM Reasoning through Large-Scale Data Synthesis and Bipolar Float Reward | arXiv:2601.03205v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated significant potential in natural language processing , complex general-purpose reasoning requiring multi-step logic, planning, and verification remains a critical bottleneck. Although Reinforcement Learning with Verifia... | https://arxiv.org/abs/2601.03205 | Academic Papers | svg |
5fd5bd8e7de67514c1dde95b7cb161a9bca4fc4abf19aabff1f6a052d43b3f82 | 2026-01-07T00:00:00-05:00 | Fine-tuning Small Language Models as Efficient Enterprise Search Relevance Labelers | arXiv:2601.03211v1 Announce Type: new Abstract: In enterprise search, building high-quality datasets at scale remains a central challenge due to the difficulty of acquiring labeled data. To resolve this challenge, we propose an efficient approach to fine-tune small language models (SLMs) for accurate relevance labeling... | https://arxiv.org/abs/2601.03211 | Academic Papers | svg |
989a5bb87a5e0520902fe9379c1bf4369faefd9a69ae5f3c282d0d6b50dc763c | 2026-01-07T00:00:00-05:00 | Critic-Guided Reinforcement Unlearning in Text-to-Image Diffusion | arXiv:2601.03213v1 Announce Type: new Abstract: Machine unlearning in text-to-image diffusion models aims to remove targeted concepts while preserving overall utility. Prior diffusion unlearning methods typically rely on supervised weight edits or global penalties; reinforcement-learning (RL) approaches, while flexible... | https://arxiv.org/abs/2601.03213 | Academic Papers | svg |
b50a66ffaa087d1d7cbddacee4a3990d2f19b1e52caa769e7fada324b057940c | 2026-01-07T00:00:00-05:00 | oneTwin: Online Digital Network Twin via Neural Radio Radiance Field | arXiv:2601.03216v1 Announce Type: new Abstract: Digital network twin is a promising technology that replicates real-world networks in real-time and assists with the design, operation, and management of next-generation networks. However, existing approaches (e.g., simulator-based and neural-based) cannot effectively rea... | https://arxiv.org/abs/2601.03216 | Academic Papers | svg |
749690d95b3ce2b301826a6c06c3889034d8a88ee576a2b22d7e90961008ee3b | 2026-01-07T00:00:00-05:00 | MalruleLib: Large-Scale Executable Misconception Reasoning with Step Traces for Modeling Student Thinking in Mathematics | arXiv:2601.03217v1 Announce Type: new Abstract: Student mistakes in mathematics are often systematic: a learner applies a coherent but wrong procedure and repeats it across contexts. We introduce MalruleLib, a learning-science-grounded framework that translates documented misconceptions into executable procedures, draw... | https://arxiv.org/abs/2601.03217 | Academic Papers | svg |
d265bcb8280257e725fbfa3650cda5df9a4758ffbf3dfadf088d053efa0d7566 | 2026-01-07T00:00:00-05:00 | Enhancing Safety in Automated Ports: A Virtual Reality Study of Pedestrian-Autonomous Vehicle Interactions under Time Pressure, Visual Constraints, and Varying Vehicle Size | arXiv:2601.03218v1 Announce Type: new Abstract: Autonomous driving improves traffic efficiency but presents safety challenges in complex port environments. This study investigates how environmental factors, traffic factors, and pedestrian characteristics influence interaction safety between autonomous vehicles and pede... | https://arxiv.org/abs/2601.03218 | Academic Papers | svg |
489d7ad4abbe8c976a656e9798f4ff33617500253529b7a0e37b7eee99be3a8e | 2026-01-07T00:00:00-05:00 | inRAN: Interpretable Online Bayesian Learning for Network Automation in Open Radio Access Networks | arXiv:2601.03219v1 Announce Type: new Abstract: Emerging AI/ML techniques have been showing great potential in automating network control in open radio access networks (Open RAN). However, existing approaches heavily rely on blackbox policies parameterized by deep neural networks, which inherently lack interpretability... | https://arxiv.org/abs/2601.03219 | Academic Papers | svg |
cbf6c5694f136213a494c18fe2896caefe62049596920b2484d3d4ae30dc0966 | 2026-01-07T00:00:00-05:00 | From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence | arXiv:2601.03220v1 Announce Type: new Abstract: Can we learn more from data than existed in the generating process itself? Can new and useful information be constructed from merely applying deterministic transformations to existing data? Can the learnable content in data be evaluated without considering a downstream ta... | https://arxiv.org/abs/2601.03220 | Academic Papers | svg |
abeb7d8e14493a2cabccb93610b35aa66afbc6726609af23f27f43b915c08d9f | 2026-01-07T00:00:00-05:00 | The Fake Friend Dilemma: Trust and the Political Economy of Conversational AI | arXiv:2601.03222v1 Announce Type: new Abstract: As conversational AI systems become increasingly integrated into everyday life, they raise pressing concerns about user autonomy, trust, and the commercial interests that influence their behavior. To address these concerns, this paper develops the Fake Friend Dilemma (FFD... | https://arxiv.org/abs/2601.03222 | Academic Papers | svg |
c6cc140461e6cbe4a448751124928b19678f24668cffa1f208f19f5d3b077689 | 2026-01-07T00:00:00-05:00 | Are eHMIs always helpful? Investigating how eHMIs interfere with pedestrian behavior on multi-lane streets: An eye-tracking virtual reality experiment | arXiv:2601.03223v1 Announce Type: new Abstract: Appropriate communication is crucial for efficient and safe interactions between pedestrians and autonomous vehicles (AVs). External human-machine interfaces (eHMIs) on AVs, which can be categorized as allocentric or egocentric, are considered a promising solution. While ... | https://arxiv.org/abs/2601.03223 | Academic Papers | svg |
2b3977e36f57fef6c42b7b4f956e1c0efb4b6401570be4a0930748c5ce1815f9 | 2026-01-07T00:00:00-05:00 | Wait or cross? Understanding the influence of behavioral tendency, trust, and risk perception on pedestrian gap-acceptance of automated truck platoons | arXiv:2601.03225v1 Announce Type: new Abstract: Although automated trucks have the potential to improve freight efficiency, reduce costs, and address driver shortages, organizing two or more trucks in a convoy has raised considerable concerns for pedestrian safety. This study conducted a controlled experiment to examin... | https://arxiv.org/abs/2601.03225 | Academic Papers | svg |
40eeb70a2b7a6340d36ce83e1e2df39d3e9ca56443b943233e10b7f6a4a7e9e7 | 2026-01-07T00:00:00-05:00 | The Sonar Moment: Benchmarking Audio-Language Models in Audio Geo-Localization | arXiv:2601.03227v1 Announce Type: new Abstract: Geo-localization aims to infer the geographic origin of a given signal. In computer vision, geo-localization has served as a demanding benchmark for compositional reasoning and is relevant to public safety. In contrast, progress on audio geo-localization has been constrai... | https://arxiv.org/abs/2601.03227 | Academic Papers | svg |
7632f8f0c6225c9da1b2298b964be7be2fbd43562a5a92fe2c729b537c13d444 | 2026-01-07T00:00:00-05:00 | SpANNS: Optimizing Approximate Nearest Neighbor Search for Sparse Vectors Using Near Memory Processing | arXiv:2601.03229v1 Announce Type: new Abstract: Approximate Nearest Neighbor Search (ANNS) is a fundamental operation in vector databases, enabling efficient similarity search in high-dimensional spaces. While dense ANNS has been optimized using specialized hardware accelerators, sparse ANNS remains limited by CPU-base... | https://arxiv.org/abs/2601.03229 | Academic Papers | svg |
f5eea5b3b6d3b7732492a716c084b1a3bb5106356af7e2712af2200e6517bd86 | 2026-01-07T00:00:00-05:00 | Multi-RADS Synthetic Radiology Report Dataset and Head-to-Head Benchmarking of 41 Open-Weight and Proprietary Language Models | arXiv:2601.03232v1 Announce Type: new Abstract: Background: Reporting and Data Systems (RADS) standardize radiology risk communication but automated RADS assignment from narrative reports is challenging because of guideline complexity, output-format constraints, and limited benchmarking across RADS frameworks and model... | https://arxiv.org/abs/2601.03232 | Academic Papers | svg |
b3226fa1bfffe9d2492ac142aa36d76b0da89cd3a487ff13974ed10594fb5e9a | 2026-01-07T00:00:00-05:00 | LTX-2: Efficient Joint Audio-Visual Foundation Model | arXiv:2601.03233v1 Announce Type: new Abstract: Recent text-to-video diffusion models can generate compelling video sequences, yet they remain silent -- missing the semantic, emotional, and atmospheric cues that audio provides. We introduce LTX-2, an open-source foundational model capable of generating high-quality, te... | https://arxiv.org/abs/2601.03233 | Academic Papers | svg |
bae4cacc5f2ed85379d4cd86fc1818f10dfe6dd534cf26ecece19883ed11f700 | 2026-01-07T00:00:00-05:00 | MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents | arXiv:2601.03236v1 Announce Type: new Abstract: Memory-Augmented Generation (MAG) extends Large Language Models with external memory to support long-context reasoning, but existing approaches largely rely on semantic similarity over monolithic memory stores, entangling temporal, causal, and entity information. This des... | https://arxiv.org/abs/2601.03236 | Academic Papers | svg |
d69435060e22d29b179b6ea8450d72b5038e508562129b5984a454422b9ff599 | 2026-01-07T00:00:00-05:00 | PET-TURTLE: Deep Unsupervised Support Vector Machines for Imbalanced Data Clusters | arXiv:2601.03237v1 Announce Type: new Abstract: Foundation vision, audio, and language models enable zero-shot performance on downstream tasks via their latent representations. Recently, unsupervised learning of data group structure with deep learning methods has gained popularity. TURTLE, a state of the art deep clust... | https://arxiv.org/abs/2601.03237 | Academic Papers | svg |
ad947b2936fb547974ef8dbac7f0b6f8bc6a0d234c01cef3770c8f838ec2db65 | 2026-01-07T00:00:00-05:00 | On the Capacity Region of Individual Key Rates in Vector Linear Secure Aggregation | arXiv:2601.03241v1 Announce Type: new Abstract: We provide new insights into an open problem recently posed by Yuan-Sun [ISIT 2025], concerning the minimum individual key rate required in the vector linear secure aggregation problem. Consider a distributed system with $K$ users, where each user $k\in [K]$ holds a data ... | https://arxiv.org/abs/2601.03241 | Academic Papers | svg |
826d5dec5795e1a9b0de3baf76769beafd6f2932744af9310d6d322fabea3005 | 2026-01-07T00:00:00-05:00 | SLIM: Stealthy Low-Coverage Black-Box Watermarking via Latent-Space Confusion Zones | arXiv:2601.03242v1 Announce Type: new Abstract: Training data is a critical and often proprietary asset in Large Language Model (LLM) development, motivating the use of data watermarking to embed model-transferable signals for usage verification. We identify low coverage as a vital yet largely overlooked requirement fo... | https://arxiv.org/abs/2601.03242 | Academic Papers | svg |
59aefcbedc32bc8129909c5a6e5b7cc8edce1390fcd71bb123de1924a79d7132 | 2026-01-07T00:00:00-05:00 | $\mathsf{QAC}^0$ Contains $\mathsf{TC}^0$ (with Many Copies of the Input) | arXiv:2601.03243v1 Announce Type: new Abstract: $\mathsf{QAC}^0$ is the class of constant-depth polynomial-size quantum circuits constructed from arbitrary single-qubit gates and generalized Toffoli gates. It is arguably the smallest natural class of constant-depth quantum computation which has not been shown useful fo... | https://arxiv.org/abs/2601.03243 | Academic Papers | svg |
377325808dd04b98f43700e92ec9765faa11d9cc9487d7593f9c671de66482a4 | 2026-01-07T00:00:00-05:00 | STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning | arXiv:2601.03248v1 Announce Type: new Abstract: Spatio-temporal reasoning in time series involves the explicit synthesis of temporal dynamics, spatial dependencies, and textual context. This capability is vital for high-stakes decision-making in systems such as traffic networks, power grids, and disease propagation. Ho... | https://arxiv.org/abs/2601.03248 | Academic Papers | svg |
2cad9a4296320eb0e3fe08a5e0b5e50caee35c3c8c7f91344d69053571898a5e | 2026-01-07T00:00:00-05:00 | Proceedings 16th International Workshop on Graph Computation Models | arXiv:2601.03249v1 Announce Type: new Abstract: This volume contains the post-proceedings of the Sixteenth International Workshop on Graph Computation Models (GCM 2025). The workshops took place in Koblenz, Germany on June 10 as part of STAF (Software Technologies: Applications and Foundations). Graphs are common mathe... | https://arxiv.org/abs/2601.03249 | Academic Papers | svg |
b20d7846ae3c14471a445f8b1e7cc476767f5dfc0cfc6394c7bdeb53f46468ee | 2026-01-07T00:00:00-05:00 | A Versatile Multimodal Agent for Multimedia Content Generation | arXiv:2601.03250v1 Announce Type: new Abstract: With the advancement of AIGC (AI-generated content) technologies, an increasing number of generative models are revolutionizing fields such as video editing, music generation, and even film production. However, due to the limitations of current AIGC models, most models ca... | https://arxiv.org/abs/2601.03250 | Academic Papers | svg |
fcb9e8a8e0a041262005bfb7974892005c30520a3e2d16df2c0345d4814b3926 | 2026-01-07T00:00:00-05:00 | NavAI: A Generalizable LLM Framework for Navigation Tasks in Virtual Reality Environments | arXiv:2601.03251v1 Announce Type: new Abstract: Navigation is one of the fundamental tasks for automated exploration in Virtual Reality (VR). Existing technologies primarily focus on path optimization in 360-degree image datasets and 3D simulators, which cannot be directly applied to immersive VR environments. To addre... | https://arxiv.org/abs/2601.03251 | Academic Papers | svg |
2ba8d2eac40ad3973f7f75a51177d2dff4f12c74ab854999fd20603a17ae82b8 | 2026-01-07T00:00:00-05:00 | InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields | arXiv:2601.03252v1 Announce Type: new Abstract: Existing depth estimation methods are fundamentally limited to predicting depth on discrete image grids. Such representations restrict their scalability to arbitrary output resolutions and hinder the geometric detail recovery. This paper introduces InfiniDepth, which repr... | https://arxiv.org/abs/2601.03252 | Academic Papers | svg |
ec809140d9d4dc1f2b9fabd3634db8cf42a5b9280a5a18b6bf3832212944f3dc | 2026-01-07T00:00:00-05:00 | Automated Semantic Rules Detection (ASRD) for Emergent Communication Interpretation | arXiv:2601.03254v1 Announce Type: new Abstract: The field of emergent communication within multi-agent systems examines how autonomous agents can independently develop communication strategies, without explicit programming, and adapt them to varied environments. However, few studies have focused on the interpretability... | https://arxiv.org/abs/2601.03254 | Academic Papers | svg |
97c5e9115ef3dcf78e1e7b300ebf9a7bc2f78b6f2a99088cdacda653cea13624 | 2026-01-07T00:00:00-05:00 | Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training | arXiv:2601.03256v1 Announce Type: new Abstract: We present Muses, the first training-free method for fantastic 3D creature generation in a feed-forward paradigm. Previous methods, which rely on part-aware optimization, manual assembly, or 2D image generation, often produce unrealistic or incoherent 3D assets due to the... | https://arxiv.org/abs/2601.03256 | Academic Papers | svg |
bbd6c5f508c6cbf0c0725a576d6448c38d7a36b5372b41f426aaf7c66e7e02b2 | 2026-01-07T00:00:00-05:00 | TWIST: Training-free and Label-free Short Text Clustering through Iterative Vector Updating with LLMs | arXiv:2510.06747v1 Announce Type: cross Abstract: In this paper, we propose a training-free and label-free method for short text clustering that can be used on top of any existing embedder. In the context of customer-facing chatbots, companies are dealing with large amounts of user utterances that need to be clustered ... | https://arxiv.org/abs/2510.06747 | Academic Papers | svg |
00f30f172693d9f13938ba8a7d24a8e468c9c9822e8274f03f42204f414100fe | 2026-01-07T00:00:00-05:00 | Effect of Electric Charge on Biotherapeutic Transport, Binding and Absorption: A Computational Study | arXiv:2601.00505v1 Announce Type: cross Abstract: This study explores the effects of electric charge on the dynamics of drug transport and absorption in subcutaneous injections of monoclonal antibodies (mAbs). We develop a novel mathematical and computational model, based on the Nernst-Planck equations and porous media... | https://arxiv.org/abs/2601.00505 | Academic Papers | svg |
398b24aab6fbfa05313eaba31b0ae98b35bd429639fd9d1ef2a0d6413968089d | 2026-01-07T00:00:00-05:00 | On (Newcomb-)Benford's law: a tale of two papers and of their disproportionate citations. How citation counts can become biased | arXiv:2601.02395v1 Announce Type: cross Abstract: The first digit (FD) phenomenon i.e., the significant digits of numbers in large data are often distributed according to a logarithmically decreasing function was first reported by S. Newcomb and then many decades later independently by F. Benford. After its century lon... | https://arxiv.org/abs/2601.02395 | Academic Papers | svg |
036cbeceaa6f3ff3bf3807875ed096525af21a4c58dd9c915ee8a7fa0af2f8b2 | 2026-01-07T00:00:00-05:00 | Detecting and Mitigating Treatment Leakage in Text-Based Causal Inference: Distillation and Sensitivity Analysis | arXiv:2601.02400v1 Announce Type: cross Abstract: Text-based causal inference increasingly employs textual data as proxies for unobserved confounders, yet this approach introduces a previously undertheorized source of bias: treatment leakage. Treatment leakage occurs when text intended to capture confounding informatio... | https://arxiv.org/abs/2601.02400 | Academic Papers | svg |
92937ca0b813d10847539c4814f5c64f2208754778690cae42ce5a1673b83893 | 2026-01-07T00:00:00-05:00 | OpenFOAM computational fluid dynamics (CFD) solver for magnetohydrodynamic open cycles, applied to the Sakhalin pulsed magnetohydrodynamic generator (PMHDG) | arXiv:2601.02406v1 Announce Type: cross Abstract: In the current study, we present a mathematical and computational fluid dynamics (CFD) model for simulating open-cycle linear Faraday-type continuous-electrode channels of magnetohydrodynamic (MHD) power generators, operating on combustion plasma. The model extends the ... | https://arxiv.org/abs/2601.02406 | Academic Papers | svg |
24d7b9ec1f7b86f8a1855c12b63b7583cd25bb97293590bcc227fe361fe38c58 | 2026-01-07T00:00:00-05:00 | A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design | arXiv:2601.02424v1 Announce Type: cross Abstract: The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and physicochemical properties. Although deep learning offers a potential methodology to achieve generative inverse design, it is stil... | https://arxiv.org/abs/2601.02424 | Academic Papers | svg |
c90405c328bcb11fcb1c5fc16ffd2efc34881b42e19e93313ce0c877c0fa6a8f | 2026-01-07T00:00:00-05:00 | Formal Modeling and Verification of Grover's Algorithm | arXiv:2601.02435v1 Announce Type: cross Abstract: Grover's algorithm relies on the superposition and interference of quantum mechanics, which is more efficient than classical computing in specific tasks such as searching an unsorted database. Due to the high complexity of quantum mechanics, the correctness of quantum a... | https://arxiv.org/abs/2601.02435 | Academic Papers | svg |
d3f58de0db35628ae8dcaa22bb81eda88edb9660af5c7a487bb75c9d7a3a67c4 | 2026-01-07T00:00:00-05:00 | Deep Learning Superresolution for 7T Knee MR Imaging: Impact on Image Quality and Diagnostic Performance | arXiv:2601.02436v1 Announce Type: cross Abstract: Background: Deep learning superresolution (SR) may enhance musculoskeletal MR image quality, but its diagnostic value in knee imaging at 7T is unclear. Objectives: To compare image quality and diagnostic performance of SR, low-resolution (LR), and high-resolution (HR) 7... | https://arxiv.org/abs/2601.02436 | Academic Papers | svg |
47acc33b14977e8745c6587970e77eab33ef89aca6b663ced2975dac25fda71a | 2026-01-07T00:00:00-05:00 | Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss | arXiv:2601.02440v1 Announce Type: cross Abstract: Anomaly detection is crucial in industrial applications for identifying rare and unseen patterns to ensure system reliability. Traditional models, trained on a single class of normal data, struggle with real-world distributions where normal data exhibit diverse patterns... | https://arxiv.org/abs/2601.02440 | Academic Papers | svg |
fd6876f5b54a4707e4fc710e308766241cc6b21c41e08c3f8d2ed717e1afebab | 2026-01-07T00:00:00-05:00 | Star Formation in Galaxy Collisions: Dependence on Impact Velocity and Gas Mass of Galaxies in GADGET-4 Simulations | arXiv:2601.02506v1 Announce Type: cross Abstract: This work investigates variations in the star formation rate during galaxy collisions when the initial conditions of velocity and gas mass are altered. For this purpose, hydrodynamic simulations were performed using the GADGET-4 code, with initial conditions generated b... | https://arxiv.org/abs/2601.02506 | Academic Papers | svg |
4f8333c1957eb86c7c87d91cddabe40ccdd5881b3ee7a4b6c1c5737672d3cfb3 | 2026-01-07T00:00:00-05:00 | Compressed Qubit Noise Spectroscopy: Piecewise-Linear Modeling and Rademacher Measurements | arXiv:2601.02516v1 Announce Type: cross Abstract: Random pulse sequences are a powerful method for qubit noise spectroscopy, enabling efficient reconstruction of sparse noise spectra. Here, we advance this method in two complementary directions. First, we extend the method using a regularizer based on the total general... | https://arxiv.org/abs/2601.02516 | Academic Papers | svg |
4aa9728eeb8bdfc43e37fac3f20447a2595390c2fba7867dd5348dc8fcb53c4e | 2026-01-07T00:00:00-05:00 | Diffusion Computation versus Quantum Computation: A Comparative Model for Order Finding and Factoring | arXiv:2601.02518v1 Announce Type: cross Abstract: We study a hybrid computational model for integer factorization in which the only non-classical resource is access to an \emph{iterated diffusion process} on a finite graph. Concretely, a \emph{diffusion step} is defined to be one application of a symmetric stochastic m... | https://arxiv.org/abs/2601.02518 | Academic Papers | svg |
3e4c6443c8b96d981a10fa2678dea76fd2eceec7caae11730721ad5f96d8c47c | 2026-01-07T00:00:00-05:00 | First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data | arXiv:2601.02523v1 Announce Type: cross Abstract: Artificial intelligence has advanced rapidly through large neural networks trained on massive datasets using thousands of GPUs or TPUs. Such training can occupy entire data centers for weeks and requires enormous computational and energy resources. Yet the optimization ... | https://arxiv.org/abs/2601.02523 | Academic Papers | svg |
bf2428cc58451882bfe24f0fecbfd2058c42b55bdcbf47f4c5e65d478fe4c577 | 2026-01-07T00:00:00-05:00 | A Green Solution for Breast Region Segmentation Using Deep Active Learning | arXiv:2601.02538v1 Announce Type: cross Abstract: Purpose: Annotation of medical breast images is an essential step toward better diagnostic but a time consuming task. This research aims to focus on different selecting sample strategies within deep active learning on Breast Region Segmentation (BRS) to lessen computati... | https://arxiv.org/abs/2601.02538 | Academic Papers | svg |
73a7118c77d81cae1e781775018443c31344bc2b3285646f897ead7b782dc066 | 2026-01-07T00:00:00-05:00 | AI-exposed jobs deteriorated before ChatGPT | arXiv:2601.02554v1 Announce Type: cross Abstract: Public debate links worsening job prospects for AI-exposed occupations to the release of ChatGPT in late 2022. Using monthly U.S. unemployment insurance records, we measure occupation- and location-specific unemployment risk and find that risk rose in AI-exposed occupat... | https://arxiv.org/abs/2601.02554 | Academic Papers | svg |
c8265555aa10183fbddaeeafebf1285c58cef0a4d84e6616bab4066da79178e2 | 2026-01-07T00:00:00-05:00 | Comparative Analysis of Binarization Methods For Medical Image Hashing On Odir Dataset | arXiv:2601.02564v1 Announce Type: cross Abstract: In this study, we evaluated four binarization methods. Locality-Sensitive Hashing (LSH), Iterative Quantization (ITQ), Kernel-based Supervised Hashing (KSH), and Supervised Discrete Hashing (SDH) on the ODIR dataset using deep feature embeddings. Experimental results sh... | https://arxiv.org/abs/2601.02564 | Academic Papers | svg |
7a96904d432c2d0d2f15cb6113bc52d8637482f943eef590e4c0e15fc5dd44d2 | 2026-01-07T00:00:00-05:00 | Annealed Langevin Posterior Sampling (ALPS): A Rapid Algorithm for Image Restoration with Multiscale Energy Models | arXiv:2601.02594v1 Announce Type: cross Abstract: Solving inverse problems in imaging requires models that support efficient inference, uncertainty quantification, and principled probabilistic reasoning. Energy-Based Models (EBMs), with their interpretable energy landscapes and compositional structure, are well-suited ... | https://arxiv.org/abs/2601.02594 | Academic Papers | svg |
03a86cecb8faed50a471905c71028d308d13bda215e286a9da8bde22d734c699 | 2026-01-07T00:00:00-05:00 | Structural reducibility of hypergraphs | arXiv:2601.02603v1 Announce Type: cross Abstract: Higher-order interactions provide a nuanced understanding of the relational structure of complex systems beyond traditional pairwise interactions. However, higher-order network analyses also incur more cumbersome interpretations and greater computational demands than th... | https://arxiv.org/abs/2601.02603 | Academic Papers | svg |
3c097f06dfa8f7423621bfa326a76165f1b121d69db4fb15342bbc2a9903ed32 | 2026-01-07T00:00:00-05:00 | Extremum Seeking Control for Wave-PDE Actuation with Distributed Effects | arXiv:2601.02607v1 Announce Type: cross Abstract: This paper deals with the gradient-based extremum seeking control (ESC) with actuation dynamics governed by distributed wave partial differential equations (PDEs). To achieve the control objective of real-time optimization for this class of infinite-dimensional systems,... | https://arxiv.org/abs/2601.02607 | Academic Papers | svg |
8f61b8901182490365eb38946ddcd7c3d85b17e2a2042589e2d0376edc9d269c | 2026-01-07T00:00:00-05:00 | Hierarchical temporal receptive windows and zero-shot timescale generalization in biologically constrained scale-invariant deep networks | arXiv:2601.02618v1 Announce Type: cross Abstract: Human cognition integrates information across nested timescales. While the cortex exhibits hierarchical Temporal Receptive Windows (TRWs), local circuits often display heterogeneous time constants. To reconcile this, we trained biologically constrained deep networks, ba... | https://arxiv.org/abs/2601.02618 | Academic Papers | svg |
7d7bc31fb66e64dc6d9393f0a82251e1b9a208ca44e84431d9ee6b26c3a02690 | 2026-01-07T00:00:00-05:00 | Statistical Inference for Fuzzy Clustering | arXiv:2601.02656v1 Announce Type: cross Abstract: Clustering is a central tool in biomedical research for discovering heterogeneous patient subpopulations, where group boundaries are often diffuse rather than sharply separated. Traditional methods produce hard partitions, whereas soft clustering methods such as fuzzy $... | https://arxiv.org/abs/2601.02656 | Academic Papers | svg |
9107c8dfddc1ce147632af7415f2a1d6e0ffd12c5696ef0115cc2565027966e8 | 2026-01-07T00:00:00-05:00 | Branching $k$-path vertex cover of forests | arXiv:2601.02685v1 Announce Type: cross Abstract: We define a set $P$ to be a branching $k$-path vertex cover of an undirected forest $F$ if all leaves and isolated vertices (vertices of degree at most $1$) of $F$ belong to $P$ and every path on $k$ vertices (of length $k-1$) contains either a branching vertex (a verte... | https://arxiv.org/abs/2601.02685 | Academic Papers | svg |
033164ba54904145b6b0dca2f77e300f78b0e152bb233dde7fcd20bb0637eb3a | 2026-01-07T00:00:00-05:00 | Transform and Entropy Coding in AV2 | arXiv:2601.02712v1 Announce Type: cross Abstract: AV2 is the successor to the AV1 royalty-free video coding standard developed by the Alliance for Open Media (AOMedia). Its primary objective is to deliver substantial compression gains and subjective quality improvements while maintaining low-complexity encoder and deco... | https://arxiv.org/abs/2601.02712 | Academic Papers | svg |
16d82987201716d4c7211d4059628c084b8d78ac53c7b010fef2c82079afdd0c | 2026-01-07T00:00:00-05:00 | Fast Conformal Prediction using Conditional Interquantile Intervals | arXiv:2601.02769v1 Announce Type: cross Abstract: We introduce Conformal Interquantile Regression (CIR), a conformal regression method that efficiently constructs near-minimal prediction intervals with guaranteed coverage. CIR leverages black-box machine learning models to estimate outcome distributions through interqu... | https://arxiv.org/abs/2601.02769 | Academic Papers | svg |
0e658870d04aaa72126e5bab24375d783022b11387cc2e314a7155bd05768ee7 | 2026-01-07T00:00:00-05:00 | The Sequence Reconstruction of Permutations under Hamming Metric with Small Errors | arXiv:2601.02844v1 Announce Type: cross Abstract: The sequence reconstruction problem asks for the recovery of a sequence from multiple noisy copies, where each copy may contain up to $r$ errors. In the case of permutations on \(n\) letters under the Hamming metric, this problem is closely related to the parameter $N(n... | https://arxiv.org/abs/2601.02844 | Academic Papers | svg |
753db3ac069be3dc6ca2ef5e867a5120c267f6d3ec3f5ccb069a70cbf5b60022 | 2026-01-07T00:00:00-05:00 | Lesion Segmentation in FDG-PET/CT Using Swin Transformer U-Net 3D: A Robust Deep Learning Framework | arXiv:2601.02864v1 Announce Type: cross Abstract: Accurate and automated lesion segmentation in Positron Emission Tomography / Computed Tomography (PET/CT) imaging is essential for cancer diagnosis and therapy planning. This paper presents a Swin Transformer UNet 3D (SwinUNet3D) framework for lesion segmentation in Flu... | https://arxiv.org/abs/2601.02864 | Academic Papers | svg |
298c7ed2145fd7b360b0f5cbe8a8f380e5d139cd351ee0bca4c053b7d2664ebd | 2026-01-07T00:00:00-05:00 | STIPP: Space-time in situ postprocessing over the French Alps using proper scoring rules | arXiv:2601.02882v1 Announce Type: cross Abstract: We propose Space-time in situ postprocessing (STIPP), a machine learning model that generates spatio-temporally consistent weather forecasts for a network of station locations. Gridded forecasts from classical numerical weather prediction or data-driven models often lac... | https://arxiv.org/abs/2601.02882 | Academic Papers | svg |
66120439eb4fcfe7b345d7e797f4f93cb23f159bbbfa7fb97cb605ba74a910a5 | 2026-01-07T00:00:00-05:00 | Enhanced 3D Gravity Inversion Using ResU-Net with Density Logging Constraints: A Dual-Phase Training Approach | arXiv:2601.02890v1 Announce Type: cross Abstract: Gravity exploration has become an important geophysical method due to its low cost and high efficiency. With the rise of artificial intelligence, data-driven gravity inversion methods based on deep learning (DL) possess physical property recovery capabilities that conve... | https://arxiv.org/abs/2601.02890 | Academic Papers | svg |
17613e90e61baac531bc3004dc3c6c4393bbb1631992de57e4893f624d099cf2 | 2026-01-07T00:00:00-05:00 | Transducing Linear Decompositions of Tournaments | arXiv:2601.02999v1 Announce Type: cross Abstract: Boja\'nczyk, Pilipczuk, and Grohe [LICS '18] proved that for graphs of bounded linear clique-width, clique-decompositions of bounded width can be produced by a CMSO transduction. We show that in the case of tournaments, a first-order transduction suffices. This implies ... | https://arxiv.org/abs/2601.02999 | Academic Papers | svg |
d5f796293acf03853d296b4cdac073f4cd17045c269c975107a293d1aa528f9b | 2026-01-07T00:00:00-05:00 | DNACHUNKER: Learnable Tokenization for DNA Language Models | arXiv:2601.03019v1 Announce Type: cross Abstract: DNA language models have emerged as powerful tools for decoding the complex language of DNA sequences. However, the performance of these models is heavily affected by their tokenization strategy, i.e., a method used to parse DNA sequences into a shorter sequence of chun... | https://arxiv.org/abs/2601.03019 | Academic Papers | svg |
69bafaffd8a414b5d1df7f7a2af742f8b1ab1d9eb719d40564a8e04e972e0f10 | 2026-01-07T00:00:00-05:00 | Similarity-Sensitive Entropy: Induced Kernels and Data-Processing Inequalities | arXiv:2601.03064v1 Announce Type: cross Abstract: We study an entropy functional $H_K$ that is sensitive to a prescribed similarity structure on a state space. For finite spaces, $H_K$ coincides with the order-1 similarity-sensitive entropy of Leinster and Cobbold. We work in the general measure-theoretic setting of ke... | https://arxiv.org/abs/2601.03064 | Academic Papers | svg |
3e21ba937bc695f648f434246756daf9e5ee82d50b3bf07761a23c363c297ef1 | 2026-01-07T00:00:00-05:00 | Computationally Efficient Estimation of Localized Treatment Effects in High-Dimensional Design Spaces using Gaussian Process Regression | arXiv:2601.03105v1 Announce Type: cross Abstract: Population-scale agent-based simulations of the opioid epidemic help evaluate intervention strategies and overdose outcomes in heterogeneous communities and provide estimates of localized treatment effects, which support the design of locally-tailored policies for preci... | https://arxiv.org/abs/2601.03105 | Academic Papers | svg |
a8f16da8fca90292eeb494da725884a44003c5154f76d5ca7006defe0649cc7b | 2026-01-07T00:00:00-05:00 | DiT-JSCC: Rethinking Deep JSCC with Diffusion Transformers and Semantic Representations | arXiv:2601.03112v1 Announce Type: cross Abstract: Generative joint source-channel coding (GJSCC) has emerged as a new Deep JSCC paradigm for achieving high-fidelity and robust image transmission under extreme wireless channel conditions, such as ultra-low bandwidth and low signal-to-noise ratio. Recent studies commonly... | https://arxiv.org/abs/2601.03112 | Academic Papers | svg |
5ffcfbc0e7e994c18ae89e439e3300d4f7f2f33cadf376648d12774b2ef9dcf6 | 2026-01-07T00:00:00-05:00 | Transformers self-organize like newborn visual systems when trained in prenatal worlds | arXiv:2601.03117v1 Announce Type: cross Abstract: Do transformers learn like brains? A key challenge in addressing this question is that transformers and brains are trained on fundamentally different data. Brains are initially "trained" on prenatal sensory experiences (e.g., retinal waves), whereas transformers are typ... | https://arxiv.org/abs/2601.03117 | Academic Papers | svg |
9dfd2ffa952d7fe2532d7a52a5bba6ae7cac215f5341e6b4ddfb96d8ec7d1663 | 2026-01-07T00:00:00-05:00 | Gradient descent reliably finds depth- and gate-optimal circuits for generic unitaries | arXiv:2601.03123v1 Announce Type: cross Abstract: When the gate set has continuous parameters, synthesizing a unitary operator as a quantum circuit is always possible using exact methods, but finding minimal circuits efficiently remains a challenging problem. The landscape is very different for compiled unitaries, whic... | https://arxiv.org/abs/2601.03123 | Academic Papers | svg |
30b6a3ea2cf6281428eacf1a14fa7fb9395ca5da65b7ece5f3e10c6d025c2046 | 2026-01-07T00:00:00-05:00 | A short proof of a bound on the size of finite irreducible semigroups of rational matrices | arXiv:2601.03206v1 Announce Type: cross Abstract: I give a short proof of a recent result due to Kiefer and Ryzhikov showing that a finite irreducible semigroup of $n\times n$ matrices has cardinality at most $3^{n^2}$. | https://arxiv.org/abs/2601.03206 | Academic Papers | svg |
63e0ab2ecf3d8b5d2a24fbe254090a907d1a5b5af57d6dbb0c71191290be954b | 2026-01-07T00:00:00-05:00 | Shallow-circuit Supervised Learning on a Quantum Processor | arXiv:2601.03235v1 Announce Type: cross Abstract: Quantum computing has long promised transformative advances in data analysis, yet practical quantum machine learning has remained elusive due to fundamental obstacles such as a steep quantum cost for the loading of classical data and poor trainability of many quantum ma... | https://arxiv.org/abs/2601.03235 | Academic Papers | svg |
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