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35be2a1badc9840da7bc92183b761b450414004a77d60667ea5e11a37b4192a6 | 2026-01-21T00:00:00-05:00 | AgenticRed: Optimizing Agentic Systems for Automated Red-teaming | arXiv:2601.13518v1 Announce Type: new Abstract: While recent automated red-teaming methods show promise for systematically exposing model vulnerabilities, most existing approaches rely on human-specified workflows. This dependence on manually designed workflows suffers from human biases and makes exploring the broader ... | https://arxiv.org/abs/2601.13518 | Academic Papers | svg |
1f5e312d61e5640ed4d06f6c10a456cb39ec49d6d16b54a8e8113e75b207033d | 2026-01-21T00:00:00-05:00 | Sticky Help, Bounded Effects: Session-by-Session Analytics of Teacher Interventions in K-12 Classrooms | arXiv:2601.13520v1 Announce Type: new Abstract: Teachers' in-the-moment support is a limited resource in technology-supported classrooms, and teachers must decide whom to help and when during ongoing student work. However, less is known about how students' prior help history (whether they were helped earlier) and their... | https://arxiv.org/abs/2601.13520 | Academic Papers | svg |
7265e322d003ca2e761251b234eae1c683b4837e6e20188bca97e2649063abcf | 2026-01-21T00:00:00-05:00 | StoTAM: Stochastic Alternating Minimization for Tucker-Structured Tensor Sensing | arXiv:2601.13522v1 Announce Type: new Abstract: Low-rank tensor sensing is a fundamental problem with broad applications in signal processing and machine learning. Among various tensor models, low-Tucker-rank tensors are particularly attractive for capturing multi-mode subspace structures in high-dimensional data. Exis... | https://arxiv.org/abs/2601.13522 | Academic Papers | svg |
40bf3d19438daac4e15f49af02ab1c6709589aac9d5db1adef804818791c29df | 2026-01-21T00:00:00-05:00 | GO-MLVTON: Garment Occlusion-Aware Multi-Layer Virtual Try-On with Diffusion Models | arXiv:2601.13524v1 Announce Type: new Abstract: Existing Image-based virtual try-on (VTON) methods primarily focus on single-layer or multi-garment VTON, neglecting multi-layer VTON (ML-VTON), which involves dressing multiple layers of garments onto the human body with realistic deformation and layering to generate vis... | https://arxiv.org/abs/2601.13524 | Academic Papers | svg |
d32ecdb95ef81fad418862e539f0c0ee3effdf5ed6dea6624986e3f950273009 | 2026-01-21T00:00:00-05:00 | More Than Efficiency: Embedding Compression Improves Domain Adaptation in Dense Retrieval | arXiv:2601.13525v1 Announce Type: new Abstract: Dense retrievers powered by pretrained embeddings are widely used for document retrieval but struggle in specialized domains due to the mismatches between the training and target domain distributions. Domain adaptation typically requires costly annotation and retraining o... | https://arxiv.org/abs/2601.13525 | Academic Papers | svg |
ee13db51416415435411ec21fddc163816e1899f3da245b5058253e94d540f56 | 2026-01-21T00:00:00-05:00 | Eliciting Harmful Capabilities by Fine-Tuning On Safeguarded Outputs | arXiv:2601.13528v1 Announce Type: new Abstract: Model developers implement safeguards in frontier models to prevent misuse, for example, by employing classifiers to filter dangerous outputs. In this work, we demonstrate that even robustly safeguarded models can be used to elicit harmful capabilities in open-source mode... | https://arxiv.org/abs/2601.13528 | Academic Papers | svg |
780099bd91cfd58022e3754192e4df970df525169679a550b60eed8c0a2f37f6 | 2026-01-21T00:00:00-05:00 | The OncoReach Stylet for Brachytherapy: Design Evaluation and Pilot Study | arXiv:2601.13529v1 Announce Type: new Abstract: Cervical cancer accounts for a significant portion of the global cancer burden among women. Interstitial brachytherapy (ISBT) is a standard procedure for treating cervical cancer; it involves placing a radioactive source through a straight hollow needle within or in close... | https://arxiv.org/abs/2601.13529 | Academic Papers | svg |
b73c5533d1e8ae17f1f8a0be94c99c35886e5ecb22bd2c59208a97c0a7c14340 | 2026-01-21T00:00:00-05:00 | Reasoning While Recommending: Entropy-Guided Latent Reasoning in Generative Re-ranking Models | arXiv:2601.13533v1 Announce Type: new Abstract: Reinforcement learning plays a crucial role in generative re-ranking scenarios due to its exploration-exploitation capabilities, but existing generative methods mostly fail to adapt to the dynamic entropy changes in model difficulty during list generation, making it chall... | https://arxiv.org/abs/2601.13533 | Academic Papers | svg |
f75a274da7167dc3e78e5749e7ed53d9433847f7d0dd842b01f78a20539ebc76 | 2026-01-21T00:00:00-05:00 | MN-TSG:Continuous Time Series Generation with Irregular Observations | arXiv:2601.13534v1 Announce Type: new Abstract: Time series generation (TSG) plays a critical role in a wide range of domains, such as healthcare. However, most existing methods assume regularly sampled observations and fixed output resolutions, which are often misaligned with real-world scenarios where data are irregu... | https://arxiv.org/abs/2601.13534 | Academic Papers | svg |
221b0b2172c2cc3de95247eca48afc78928990cd247c885146dce5dfe6843888 | 2026-01-21T00:00:00-05:00 | Sparse Identification of Nonlinear Distributed-Delay Dynamics via the Linear Chain Trick | arXiv:2601.13536v1 Announce Type: new Abstract: The Sparse Identification of Nonlinear Dynamics (SINDy) framework has been frequently used to discover parsimonious differential equations governing natural and physical systems. This includes recent extensions to SINDy that enable the recovery of discrete delay different... | https://arxiv.org/abs/2601.13536 | Academic Papers | svg |
3c7cb3751b557a1b9c4a6629f5b96006413e749c232b454fa1b9ee2dc201e28f | 2026-01-21T00:00:00-05:00 | When Wording Steers the Evaluation: Framing Bias in LLM judges | arXiv:2601.13537v1 Announce Type: new Abstract: Large language models (LLMs) are known to produce varying responses depending on prompt phrasing, indicating that subtle guidance in phrasing can steer their answers. However, the impact of this framing bias on LLM-based evaluation, where models are expected to make stabl... | https://arxiv.org/abs/2601.13537 | Academic Papers | svg |
7a6d3516a1834fb9e92ce830cbc3626e1c35d2381d52378fb45ef2d5822295ef | 2026-01-21T00:00:00-05:00 | LongSpeech: A Scalable Benchmark for Transcription, Translation and Understanding in Long Speech | arXiv:2601.13539v1 Announce Type: new Abstract: Recent advances in audio-language models have demonstrated remarkable success on short, segment-level speech tasks. However, real-world applications such as meeting transcription, spoken document understanding, and conversational analysis require robust models capable of ... | https://arxiv.org/abs/2601.13539 | Academic Papers | svg |
d3924bfd788c9ac9a128864c7ce833fef88034339823fe6f1f314adcb26985cb | 2026-01-21T00:00:00-05:00 | A hybrid numerical method for a microscopic and macroscopic traffic flow model | arXiv:2601.13541v1 Announce Type: new Abstract: In this paper, we introduce a traffic flow model based on a microscopic follow-the-leader model, while enforcing maximal constraints on the density and velocity of the flow. The related macroscopic model can be represented in conservative formulation. By introducing an ad... | https://arxiv.org/abs/2601.13541 | Academic Papers | svg |
471da7ecb4d27ec985222ab277b4986822b7664ffc1a876663054507fc475432 | 2026-01-21T00:00:00-05:00 | TruthTensor: Evaluating LLMs Human Imitation through Prediction Market Drift and Holistic Reasoning | arXiv:2601.13545v1 Announce Type: new Abstract: Evaluating language models and AI agents remains fundamentally challenging because static benchmarks fail to capture real-world uncertainty, distribution shift, and the gap between isolated task accuracy and human-aligned decision-making under evolving conditions. This pa... | https://arxiv.org/abs/2601.13545 | Academic Papers | svg |
29f8efdc75dbe0f488039188660e25ff530df3ffc3508a63e6fe02a84efdcdbc | 2026-01-21T00:00:00-05:00 | ChatAD: Reasoning-Enhanced Time-Series Anomaly Detection with Multi-Turn Instruction Evolution | arXiv:2601.13546v1 Announce Type: new Abstract: LLM-driven Anomaly Detection (AD) helps enhance the understanding and explanatory abilities of anomalous behaviors in Time Series (TS). Existing methods face challenges of inadequate reasoning ability, deficient multi-turn dialogue capability, and narrow generalization. T... | https://arxiv.org/abs/2601.13546 | Academic Papers | svg |
282372761012b7ee6e46a2c5adb0284d68f16aa457e3d65591eeb594b9b143be | 2026-01-21T00:00:00-05:00 | HateXScore: A Metric Suite for Evaluating Reasoning Quality in Hate Speech Explanations | arXiv:2601.13547v1 Announce Type: new Abstract: Hateful speech detection is a key component of content moderation, yet current evaluation frameworks rarely assess why a text is deemed hateful. We introduce \textsf{HateXScore}, a four-component metric suite designed to evaluate the reasoning quality of model explanation... | https://arxiv.org/abs/2601.13547 | Academic Papers | svg |
4ddfb6d93ee7a95841fd55c4c4e3e05a2f8ce24c5a3bc5d974ced87a5f0f8755 | 2026-01-21T00:00:00-05:00 | Patterning: The Dual of Interpretability | arXiv:2601.13548v1 Announce Type: new Abstract: Mechanistic interpretability aims to understand how neural networks generalize beyond their training data by reverse-engineering their internal structures. We introduce patterning as the dual problem: given a desired form of generalization, determine what training data pr... | https://arxiv.org/abs/2601.13548 | Academic Papers | svg |
cd6acab5bf8cb079d46f05859afeefd347a1ebac286470495ecee6482a1b1063 | 2026-01-21T00:00:00-05:00 | DiffFace-Edit: A Diffusion-Based Facial Dataset for Forgery-Semantic Driven Deepfake Detection Analysis | arXiv:2601.13551v1 Announce Type: new Abstract: Generative models now produce imperceptible, fine-grained manipulated faces, posing significant privacy risks. However, existing AI-generated face datasets generally lack focus on samples with fine-grained regional manipulations. Furthermore, no researchers have yet studi... | https://arxiv.org/abs/2601.13551 | Academic Papers | svg |
64d52ee189bc1f96b1e2dd6a2de4bff016e72ea8dc26180735bfc817c98257fb | 2026-01-21T00:00:00-05:00 | LogicEnvGen: Task-Logic Driven Generation of Diverse Simulated Environments for Embodied AI | arXiv:2601.13556v1 Announce Type: new Abstract: Simulated environments play an essential role in embodied AI, functionally analogous to test cases in software engineering. However, existing environment generation methods often emphasize visual realism (e.g., object diversity and layout coherence), overlooking a crucial... | https://arxiv.org/abs/2601.13556 | Academic Papers | svg |
312b7f82a7a971abe9c493cb5ce7775c48f10892361c3db7c69fbceccb034824 | 2026-01-21T00:00:00-05:00 | Leveraging ChatGPT and Other NLP Methods for Identifying Risk and Protective Behaviors in MSM: Social Media and Dating apps Text Analysis | arXiv:2601.13558v1 Announce Type: new Abstract: Men who have sex with men (MSM) are at elevated risk for sexually transmitted infections and harmful drinking compared to heterosexual men. Text data collected from social media and dating applications may provide new opportunities for personalized public health intervent... | https://arxiv.org/abs/2601.13558 | Academic Papers | svg |
ab0f2fae08049d9264829b324f18be54fe5241e554062e8c0c17101034b44b50 | 2026-01-21T00:00:00-05:00 | AgentGC: Evolutionary Learning-based Lossless Compression for Genomics Data with LLM-driven Multiple Agent | arXiv:2601.13559v1 Announce Type: new Abstract: Lossless compression has made significant advancements in Genomics Data (GD) storage, sharing and management. Current learning-based methods are non-evolvable with problems of low-level compression modeling, limited adaptability, and user-unfriendly interface. To this end... | https://arxiv.org/abs/2601.13559 | Academic Papers | svg |
1cbd13c82b4de45b383e6344f37ec4fe0a125e482c6c29200f288fec37e4b7cf | 2026-01-21T00:00:00-05:00 | Reasoning is a Modality | arXiv:2601.13562v1 Announce Type: new Abstract: The Abstraction and Reasoning Corpus (ARC) provides a compact laboratory for studying abstract reasoning, an ability central to human intelligence. Modern AI systems, including LLMs and ViTs, largely operate as sequence-of-behavior prediction machines: they match observab... | https://arxiv.org/abs/2601.13562 | Academic Papers | svg |
63c2973382c5fec7c79815235e8d7fec46b2d830bf25114c87c921d6dec661ad | 2026-01-21T00:00:00-05:00 | ButterflyMoE: Sub-Linear Ternary Experts via Structured Butterfly Orbits | arXiv:2601.13563v1 Announce Type: new Abstract: Linear memory scaling stores $N$ independent expert weight matrices requiring $\mathcal{O}(N \cdot d^2)$ memory, which exceeds edge devices memory budget. Current compression methods like quantization, pruning and low-rank factorization reduce constant factors but leave t... | https://arxiv.org/abs/2601.13563 | Academic Papers | svg |
93aa8977ca18850cb6ca6c7c3be1233001d75eba61e6a698d8527d3b7791e4ee | 2026-01-21T00:00:00-05:00 | Multi-objective fluorescent molecule design with a data-physics dual-driven generative framework | arXiv:2601.13564v1 Announce Type: new Abstract: Designing fluorescent small molecules with tailored optical and physicochemical properties requires navigating vast, underexplored chemical space while satisfying multiple objectives and constraints. Conventional generate-score-screen approaches become impractical under s... | https://arxiv.org/abs/2601.13564 | Academic Papers | svg |
897d528ccf31065f80a59192c500304bdb03260567850efaa5a9175ebd37cc46 | 2026-01-21T00:00:00-05:00 | Learning Fine-Grained Correspondence with Cross-Perspective Perception for Open-Vocabulary 6D Object Pose Estimation | arXiv:2601.13565v1 Announce Type: new Abstract: Open-vocabulary 6D object pose estimation empowers robots to manipulate arbitrary unseen objects guided solely by natural language. However, a critical limitation of existing approaches is their reliance on unconstrained global matching strategies. In open-world scenarios... | https://arxiv.org/abs/2601.13565 | Academic Papers | svg |
71224c3cd6e90dcac5c5225f78ad15c6585003aab6b82562f56720be0045dbcb | 2026-01-21T00:00:00-05:00 | Self-Improvement as Coherence Optimization: A Theoretical Account | arXiv:2601.13566v1 Announce Type: new Abstract: Can language models improve their accuracy without external supervision? Methods such as debate, bootstrap, and internal coherence maximization achieve this surprising feat, even matching golden finetuning performance. Yet why they work remains theoretically unclear. We s... | https://arxiv.org/abs/2601.13566 | Academic Papers | svg |
ce1190f53143101a6d0cd4b372198093ecdd3d384cbc6eb9a5892f173c3b075c | 2026-01-21T00:00:00-05:00 | DRGW: Learning Disentangled Representations for Robust Graph Watermarking | arXiv:2601.13569v1 Announce Type: new Abstract: Graph-structured data is foundational to numerous web applications, and watermarking is crucial for protecting their intellectual property and ensuring data provenance. Existing watermarking methods primarily operate on graph structures or entangled graph representations,... | https://arxiv.org/abs/2601.13569 | Academic Papers | svg |
647cdae994eeceeaf8e3964134b8d3f52dbe6c7a117ba8e614d1a03c2f7f8cd6 | 2026-01-21T00:00:00-05:00 | GeoDynamics: A Geometric State-Space Neural Network for Understanding Brain Dynamics on Riemannian Manifolds | arXiv:2601.13570v1 Announce Type: new Abstract: State-space models (SSMs) have become a cornerstone for unraveling brain dynamics, revealing how latent neural states evolve over time and give rise to observed signals. By combining the flexibility of deep learning with the principled dynamical structure of SSMs, recent ... | https://arxiv.org/abs/2601.13570 | Academic Papers | svg |
558fb85c29468313509329e04d7bf88cd5f748069c9a782509cc138467ede12e | 2026-01-21T00:00:00-05:00 | Stochastic Dynamic Pricing of Electric Vehicle Charging with Heterogeneous User Behavior: A Stackelberg Game Framework | arXiv:2601.13571v1 Announce Type: new Abstract: The rapid adoption of electric vehicles (EVs) introduces complex spatiotemporal demand management challenges for charging station operators (CSOs), exacerbated by demand imbalances, behavioral heterogeneity, and system uncertainty. Traditional dynamic pricing models, ofte... | https://arxiv.org/abs/2601.13571 | Academic Papers | svg |
0685bce678f8eb69050aa2b9b59086839018711f67e5536319484d3ffde06323 | 2026-01-21T00:00:00-05:00 | Behavior Knowledge Merge in Reinforced Agentic Models | arXiv:2601.13572v1 Announce Type: new Abstract: Reinforcement learning (RL) is central to post-training, particularly for agentic models that require specialized reasoning behaviors. In this setting, model merging offers a practical mechanism for integrating multiple RL-trained agents from different tasks into a single... | https://arxiv.org/abs/2601.13572 | Academic Papers | svg |
3a3d6d57a1ffbfbac241f77023d1299bd99b0b9b9d8433405fb71d82f3c6e8c7 | 2026-01-21T00:00:00-05:00 | TRGCN: A Hybrid Framework for Social Network Rumor Detection | arXiv:2601.13573v1 Announce Type: new Abstract: Accurate and efficient rumor detection is critical for information governance, particularly in the context of the rapid spread of misinformation on social networks. Traditional rumor detection relied primarily on manual analysis. With the continuous advancement of technol... | https://arxiv.org/abs/2601.13573 | Academic Papers | svg |
2d8273275e3b50ae0b06144da03e5a973f33b997a4c92d1dd8e30a8a5685dbc6 | 2026-01-21T00:00:00-05:00 | Highly Deformable Proprioceptive Membrane for Real-Time 3D Shape Reconstruction | arXiv:2601.13574v1 Announce Type: new Abstract: Reconstructing the three-dimensional (3D) geometry of object surfaces is essential for robot perception, yet vision-based approaches are generally unreliable under low illumination or occlusion. This limitation motivates the design of a proprioceptive membrane that confor... | https://arxiv.org/abs/2601.13574 | Academic Papers | svg |
18a1c7ef1c0d5f104cee74c5318a0dac2a2e50df843da2af2334e9368a38272c | 2026-01-21T00:00:00-05:00 | Comparing Without Saying: A Dataset and Benchmark for Implicit Comparative Opinion Mining from Same-User Reviews | arXiv:2601.13575v1 Announce Type: new Abstract: Existing studies on comparative opinion mining have mainly focused on explicit comparative expressions, which are uncommon in real-world reviews. This leaves implicit comparisons - here users express preferences across separate reviews - largely underexplored. We introduc... | https://arxiv.org/abs/2601.13575 | Academic Papers | svg |
fe89e113c309503b20744a5dab99d117bd954d9f4f7a34ba88551f857435519a | 2026-01-21T00:00:00-05:00 | FG-OrIU: Towards Better Forgetting via Feature-Gradient Orthogonality for Incremental Unlearning | arXiv:2601.13578v1 Announce Type: new Abstract: Incremental unlearning (IU) is critical for pre-trained models to comply with sequential data deletion requests, yet existing methods primarily suppress parameters or confuse knowledge without explicit constraints on both feature and gradient level, resulting in \textit{s... | https://arxiv.org/abs/2601.13578 | Academic Papers | svg |
4fb23107d20c9d163abd0ef80c7baa57ea27b344a3860977edccc3e05fb12ff7 | 2026-01-21T00:00:00-05:00 | A Kubernetes custom scheduler based on reinforcement learning for compute-intensive pods | arXiv:2601.13579v1 Announce Type: new Abstract: With the rise of cloud computing and lightweight containers, Docker has emerged as a leading technology for rapid service deployment, with Kubernetes responsible for pod orchestration. However, for compute-intensive workloads-particularly web services executing containeri... | https://arxiv.org/abs/2601.13579 | Academic Papers | svg |
004a462c5e48f6c875e43be625bee94a9331db54487cf089eb94aa30a35056cc | 2026-01-21T00:00:00-05:00 | Neural Organ Transplantation (NOT): Checkpoint-Based Modular Adaptation for Transformer Models | arXiv:2601.13580v1 Announce Type: new Abstract: We introduce Neural Organ Transplantation (NOT), a modular adaptation framework that enables trained transformer layers to function as reusable transferable checkpoints for domain adaptation. Unlike conventional fine-tuning approaches that tightly couple trained parameter... | https://arxiv.org/abs/2601.13580 | Academic Papers | svg |
7d67fb4386cf5f6373e4e6d3da1605966b2ca409a92a5950d29cc601a17a8563 | 2026-01-21T00:00:00-05:00 | SCRIPTMIND: Crime Script Inference and Cognitive Evaluation for LLM-based Social Engineering Scam Detection System | arXiv:2601.13581v1 Announce Type: new Abstract: Social engineering scams increasingly employ personalized, multi-turn deception, exposing the limits of traditional detection methods. While Large Language Models (LLMs) show promise in identifying deception, their cognitive assistance potential remains underexplored. We ... | https://arxiv.org/abs/2601.13581 | Academic Papers | svg |
6a2dbda471725dbebd5ec9e86c45baaba5c0db2cdcab194eaeb7907764246047 | 2026-01-21T00:00:00-05:00 | Nonlinear fractional-periodic boundary value problems with Hilfer fractional derivative: existence and numerical approximations of solutions | arXiv:2601.13584v1 Announce Type: new Abstract: We prove conditions for existence of analytical solutions for boundary value problems with the Hilfer fractional derivative, generalizing the commonly used Riemann-Liouville and Caputo operators. The boundary values, referred to in this paper as fractional-periodic, are f... | https://arxiv.org/abs/2601.13584 | Academic Papers | svg |
d63aa81468911fd345e64e1ebd92a78d34e0c5e106ad8d172f11c6c4a938e3f0 | 2026-01-21T00:00:00-05:00 | TREX: Tokenizer Regression for Optimal Data Mixture | arXiv:2601.13588v1 Announce Type: new Abstract: Building effective tokenizers for multilingual Large Language Models (LLMs) requires careful control over language-specific data mixtures. While a tokenizer's compression performance critically affects the efficiency of LLM training and inference, existing approaches rely... | https://arxiv.org/abs/2601.13588 | Academic Papers | svg |
c209bb63cb09408dd893d1f50cee77acafe6db700160b6629d987de941beb71f | 2026-01-21T00:00:00-05:00 | Motion-to-Response Content Generation via Multi-Agent AI System with Real-Time Safety Verification | arXiv:2601.13589v1 Announce Type: new Abstract: This paper proposes a multi-agent artificial intelligence system that generates response-oriented media content in real time based on audio-derived emotional signals. Unlike conventional speech emotion recognition studies that focus primarily on classification accuracy, o... | https://arxiv.org/abs/2601.13589 | Academic Papers | svg |
09584cdea2c218acfe65081321fb94118116fd2aefe80e4388829e60f175e141 | 2026-01-21T00:00:00-05:00 | Vulnerability of LLMs' Belief Systems? LLMs Belief Resistance Check Through Strategic Persuasive Conversation Interventions | arXiv:2601.13590v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly employed in various question-answering tasks. However, recent studies showcase that LLMs are susceptible to persuasion and could adopt counterfactual beliefs. We present a systematic evaluation of LLM susceptibility to persuas... | https://arxiv.org/abs/2601.13590 | Academic Papers | svg |
edece1ad031b3d580681bcc9b6f677bcfd424c557abb0e6b246022563ec195fc | 2026-01-21T00:00:00-05:00 | DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems | arXiv:2601.13591v1 Announce Type: new Abstract: Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning. However, the open-ended nature of real-world data science problems, which often span multiple taxonomies and lack standard answers, poses a significant challenge f... | https://arxiv.org/abs/2601.13591 | Academic Papers | svg |
579d9ff5f794ebaf789fb4b293a2cab8c41892b3cf16ce25b488e7628320ca0d | 2026-01-21T00:00:00-05:00 | Machine learning based radiative parameterization scheme and its performance in operational reforecast experiments | arXiv:2601.13592v1 Announce Type: new Abstract: Radiation is typically the most time-consuming physical process in numerical models. One solution is to use machine learning methods to simulate the radiation process to improve computational efficiency. From an operational standpoint, this study investigates critical lim... | https://arxiv.org/abs/2601.13592 | Academic Papers | svg |
f9d0fcda86bfd6ff73a3da7d1ae9933e575e3a8f4fa8b2aa2a538376e0373de7 | 2026-01-21T00:00:00-05:00 | AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development | arXiv:2601.13597v1 Announce Type: new Abstract: Large language model (LLM)-based coding agents increasingly act as autonomous contributors that generate and merge pull requests, yet their real-world effects on software projects are unclear, especially relative to widely adopted IDE-based AI assistants. We present a lon... | https://arxiv.org/abs/2601.13597 | Academic Papers | svg |
b25b97733a33d97b431a1c12c14b8b53bf82ae3f92e54ebadad7a6c1ecf9a2d3 | 2026-01-21T00:00:00-05:00 | Diffusion In Diffusion: Breaking the Autoregressive Bottleneck in Block Diffusion Models | arXiv:2601.13599v1 Announce Type: new Abstract: Block diffusion language models, operating as semi-autoregressive paradigms, combine the strengths of both autoregressive and diffusion paradigms. However, their strict unidirectional block dependencies introduce irreversibility and sacrifice the global planning capabilit... | https://arxiv.org/abs/2601.13599 | Academic Papers | svg |
a3194684b950f9892290e4ddd75bb70e342aa467f11e0624bbe271775d91c7eb | 2026-01-21T00:00:00-05:00 | Foundations of Global Consistency Checking with Noisy LLM Oracles | arXiv:2601.13600v1 Announce Type: new Abstract: Ensuring that collections of natural-language facts are globally consistent is essential for tasks such as fact-checking, summarization, and knowledge base construction. While Large Language Models (LLMs) can assess the consistency of small subsets of facts, their judgmen... | https://arxiv.org/abs/2601.13600 | Academic Papers | svg |
b40cced59f3bbcd3738b3fca5a217447df2183bae240daabb3e57e2edca1e8b9 | 2026-01-21T00:00:00-05:00 | An Elementary Approach to Scheduling in Generative Diffusion Models | arXiv:2601.13602v1 Announce Type: new Abstract: An elementary approach to characterizing the impact of noise scheduling and time discretization in generative diffusion models is developed. Considering a simplified model where the source distribution is multivariate Gaussian with a given covariance matrix, the explicit ... | https://arxiv.org/abs/2601.13602 | Academic Papers | svg |
87fdf16dd45995fec6f8ea96ff27be5daff4d66315ab07aa751909f59f2fae3a | 2026-01-21T00:00:00-05:00 | DCCVT: Differentiable Clipped Centroidal Voronoi Tessellation | arXiv:2601.13603v1 Announce Type: new Abstract: While Marching Cubes (MC) and Marching Tetrahedra (MTet) are widely adopted in 3D reconstruction pipelines due to their simplicity and efficiency, their differentiable variants remain suboptimal for mesh extraction. This often limits the quality of 3D meshes reconstructed... | https://arxiv.org/abs/2601.13603 | Academic Papers | svg |
88dbe43aeae8e99036d9b5f04f458a797ddbcb1964a96dbd919174f82ec29fac | 2026-01-21T00:00:00-05:00 | Optimizing Parallel Schemes with Lyapunov Exponents and kNN-LLE Estimation | arXiv:2601.13604v1 Announce Type: new Abstract: Inverse parallel schemes remain indispensable tools for computing the roots of nonlinear systems, yet their dynamical behavior can be unexpectedly rich, ranging from strong contraction to oscillatory or chaotic transients depending on the choice of algorithmic parameters ... | https://arxiv.org/abs/2601.13604 | Academic Papers | svg |
b216c6d3b7465f6c523ad626beeac82b988998bdd8d9bf5309a480a0fe34bf47 | 2026-01-21T00:00:00-05:00 | Outage Identification from Electricity Market Data: Quickest Change Detection Approach | arXiv:2601.13605v1 Announce Type: new Abstract: Power system outages expose market participants to significant financial risk unless promptly detected and hedged. We develop an outage identification method from public market signals grounded in the parametric quickest change detection (QCD) theory. Parametric QCD opera... | https://arxiv.org/abs/2601.13605 | Academic Papers | svg |
d0a1719cff52b92b18f01b6b972ebcbdf2abfce7f55af0600ea9ea6344b18460 | 2026-01-21T00:00:00-05:00 | ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch | arXiv:2601.13606v1 Announce Type: new Abstract: Chart reasoning is a critical capability for Vision Language Models (VLMs). However, the development of open-source models is severely hindered by the lack of high-quality training data. Existing datasets suffer from a dual challenge: synthetic charts are often simplistic... | https://arxiv.org/abs/2601.13606 | Academic Papers | svg |
dc22077c2a074ff46ef2648735d0e62a86c715c8351ed92bcd73e644ea0676a6 | 2026-01-21T00:00:00-05:00 | When Reasoning Leaks Membership: Membership Inference Attack on Black-box Large Reasoning Models | arXiv:2601.13607v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have rapidly gained prominence for their strong performance in solving complex tasks. Many modern black-box LRMs expose the intermediate reasoning traces through APIs to improve transparency (e.g., Gemini-2.5 and Claude-sonnet). Despite their... | https://arxiv.org/abs/2601.13607 | Academic Papers | svg |
650fefab858915399ae5f0eb83541d7aeafd330c9d3ebeca7d5390cab0050b56 | 2026-01-21T00:00:00-05:00 | Fisher-Informed Parameterwise Aggregation for Federated Learning with Heterogeneous Data | arXiv:2601.13608v1 Announce Type: new Abstract: Federated learning aggregates model updates from distributed clients, but standard first order methods such as FedAvg apply the same scalar weight to all parameters from each client. Under non-IID data, these uniformly weighted updates can be strongly misaligned across cl... | https://arxiv.org/abs/2601.13608 | Academic Papers | svg |
a15be4ba9c5c9ee19e3256ecd676078d7c6f466e28d8eacbe837e0ebd251ca0e | 2026-01-21T00:00:00-05:00 | Balancing Fairness and High Match Rates in Reciprocal Recommender Systems: A Nash Social Welfare Approach | arXiv:2601.13609v1 Announce Type: new Abstract: Matching platforms, such as online dating services and job recommendations, have become increasingly prevalent. For the success of these platforms, it is crucial to design reciprocal recommender systems (RRSs) that not only increase the total number of matches but also av... | https://arxiv.org/abs/2601.13609 | Academic Papers | svg |
06c69a3236967dc09023baac92ee3ea3588a398dad7511068347915effcaced7 | 2026-01-21T00:00:00-05:00 | Secure Multi-Path Routing with All-or-Nothing Transform for Network-on-Chip Architectures | arXiv:2601.13610v1 Announce Type: new Abstract: Ensuring Network-on-Chip (NoC) security is crucial to design trustworthy NoC-based System-on-Chip (SoC) architectures. While there are various threats that exploit on-chip communication vulnerabilities, eavesdropping attacks via malicious nodes are among the most common a... | https://arxiv.org/abs/2601.13610 | Academic Papers | svg |
8b0ba169fcfa5e67eab4fed8bf9d07b45684c8587f97ddcd5a70a077fa4d0a63 | 2026-01-21T00:00:00-05:00 | PINA: Prompt Injection Attack against Navigation Agents | arXiv:2601.13612v1 Announce Type: new Abstract: Navigation agents powered by large language models (LLMs) convert natural language instructions into executable plans and actions. Compared to text-based applications, their security is far more critical: a successful prompt injection attack does not just alter outputs bu... | https://arxiv.org/abs/2601.13612 | Academic Papers | svg |
a0a9d9ae4398503c96ddb949eac48612e2eee9c8890b86e4e6cfbc741f3df36b | 2026-01-21T00:00:00-05:00 | CauScientist: Teaching LLMs to Respect Data for Causal Discovery | arXiv:2601.13614v1 Announce Type: new Abstract: Causal discovery is fundamental to scientific understanding and reliable decision-making. Existing approaches face critical limitations: purely data-driven methods suffer from statistical indistinguishability and modeling assumptions, while recent LLM-based methods either... | https://arxiv.org/abs/2601.13614 | Academic Papers | svg |
11b8d0d28912b5275622c4ecff3089ce776f700a7eca1bd650abdf580e450ee8 | 2026-01-21T00:00:00-05:00 | Resilient Hierarchical Power Control for Hybrid GFL/GFM Microgrids Under Mixed Cyber-Attacks and Physical Constraints | arXiv:2601.13615v1 Announce Type: new Abstract: Hybrid microgrids integrating Grid-Following (GFL) and Grid-Forming (GFM) inverters present complex control challenges arising from the decoupling between long-term economic dispatch and real-time dynamic regulation, as well as the distinct physical limitations of heterog... | https://arxiv.org/abs/2601.13615 | Academic Papers | svg |
cd0839b96325b8231fcc23084cca21a1089ce0a8ecd4c5d3e384658eb554c9ae | 2026-01-21T00:00:00-05:00 | Reflections over the Sea: Reconfigurable Intelligent Surface for Maritime Self-Powered Communications | arXiv:2601.13618v1 Announce Type: new Abstract: Maritime communication is becoming a vital component of 6G networks, driven by the rapid expansion of the maritime economy. However, existing technologies face critical challenges in signal coverage, availability, and robustness, especially under harsh sea conditions. Thi... | https://arxiv.org/abs/2601.13618 | Academic Papers | svg |
526ff3eae24b97c67c8a3f83a4682e5c0c87aefaf004f2e3dc8c693876e8b396 | 2026-01-21T00:00:00-05:00 | CARPE: Context-Aware Image Representation Prioritization via Ensemble for Large Vision-Language Models | arXiv:2601.13622v1 Announce Type: new Abstract: Recent advancements in Large Vision-Language Models (LVLMs) have pushed them closer to becoming general-purpose assistants. Despite their strong performance, LVLMs still struggle with vision-centric tasks such as image classification, underperforming compared to their bas... | https://arxiv.org/abs/2601.13622 | Academic Papers | svg |
26ee5add32c8e849d7d16a9762f4dca5cf75fbc201fa32f352d518736cf21400 | 2026-01-21T00:00:00-05:00 | PRIMAL: Processing-In-Memory Based Low-Rank Adaptation for LLM Inference Accelerator | arXiv:2601.13628v1 Announce Type: new Abstract: This paper presents PRIMAL, a processing-in-memory (PIM) based large language model (LLM) inference accelerator with low-rank adaptation (LoRA). PRIMAL integrates heterogeneous PIM processing elements (PEs), interconnected by 2D-mesh inter-PE computational network (IPCN).... | https://arxiv.org/abs/2601.13628 | Academic Papers | svg |
3ee8d9ea5f6281a8a820293509462ecf85c8fa028da35c1653f937bf427e50e7 | 2026-01-21T00:00:00-05:00 | Activation-Space Anchored Access Control for Multi-Class Permission Reasoning in Large Language Models | arXiv:2601.13630v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed over knowledge bases for efficient knowledge retrieval and question answering. However, LLMs can inadvertently answer beyond a user's permission scope, leaking sensitive content, thus making it difficult to deploy kno... | https://arxiv.org/abs/2601.13630 | Academic Papers | svg |
7ff9d50b580cb47d456362ac5e0e9aa1fb10cdd25f5bde9412fdf58d06a1ed1e | 2026-01-21T00:00:00-05:00 | ContiguousKV: Accelerating LLM Prefill with Granularity-Aligned KV Cache Management | arXiv:2601.13631v1 Announce Type: new Abstract: Efficiently serving Large Language Models (LLMs) with persistent Prefix Key-Value (KV) Cache is critical for applications like conversational search and multi-turn dialogue. Serving a request requires loading the pre-computed prefix KV cache and generating the first token... | https://arxiv.org/abs/2601.13631 | Academic Papers | svg |
99dc864be260eade8e2f0ebc85d1ac90a132720a8f4cdf3d759571b01afc84d4 | 2026-01-21T00:00:00-05:00 | Resilient Routing: Risk-Aware Dynamic Routing in Smart Logistics via Spatiotemporal Graph Learning | arXiv:2601.13632v1 Announce Type: new Abstract: With the rapid development of the e-commerce industry, the logistics network is experiencing unprecedented pressure. The traditional static routing strategy most time cannot tolerate the traffic congestion and fluctuating retail demand. In this paper, we propose a Risk-Aw... | https://arxiv.org/abs/2601.13632 | Academic Papers | svg |
cc7aaa344c03c287fec0998338ab8fb0b1d6d34eba2e35ea0aae2d4844d51a9e | 2026-01-21T00:00:00-05:00 | Scaling Test-time Inference for Visual Grounding | arXiv:2601.13633v1 Announce Type: new Abstract: Visual grounding is an essential capability of Visual Language Models (VLMs) to understand the real physical world. Previous state-of-the-art grounding visual language models usually have large model sizes, making them heavy for deployment and slow for inference. However,... | https://arxiv.org/abs/2601.13633 | Academic Papers | svg |
8f6ee034a192dd2b558089813d51cb26411de942760f45c7b256e474eeb44437 | 2026-01-21T00:00:00-05:00 | Direct Finite-Time Contraction (Step-Log) Profiling--Driven Optimization of Parallel Schemes for Nonlinear Problems on Multicore Architectures | arXiv:2601.13637v1 Announce Type: new Abstract: Efficient computation of all distinct solutions of nonlinear problems is essential in many scientific and engineering applications. Although high-order parallel iterative schemes offer fast convergence, their practical performance is often limited by sensitivity to intern... | https://arxiv.org/abs/2601.13637 | Academic Papers | svg |
59e22356ae6d3bd9c3b1f12ca83cc99a2dcf9b453422e025729fa552bd8e7076 | 2026-01-21T00:00:00-05:00 | A General One-Shot Multimodal Active Perception Framework for Robotic Manipulation: Learning to Predict Optimal Viewpoint | arXiv:2601.13639v1 Announce Type: new Abstract: Active perception in vision-based robotic manipulation aims to move the camera toward more informative observation viewpoints, thereby providing high-quality perceptual inputs for downstream tasks. Most existing active perception methods rely on iterative optimization, le... | https://arxiv.org/abs/2601.13639 | Academic Papers | svg |
3f65e8512b2ac2b54fec40309e18bec7a5528cd93825d2a67682f7996234a5e3 | 2026-01-21T00:00:00-05:00 | Towards Token-Level Text Anomaly Detection | arXiv:2601.13644v1 Announce Type: new Abstract: Despite significant progress in text anomaly detection for web applications such as spam filtering and fake news detection, existing methods are fundamentally limited to document-level analysis, unable to identify which specific parts of a text are anomalous. We introduce... | https://arxiv.org/abs/2601.13644 | Academic Papers | svg |
69417aee5951b8462b856ba50c327ce44389b878cc87383ea2f5327f5d4315f1 | 2026-01-21T00:00:00-05:00 | Quadratic Upper Bound for Boosting Robustness | arXiv:2601.13645v1 Announce Type: new Abstract: Fast adversarial training (FAT) aims to enhance the robustness of models against adversarial attacks with reduced training time, however, FAT often suffers from compromised robustness due to insufficient exploration of adversarial space. In this paper, we develop a loss f... | https://arxiv.org/abs/2601.13645 | Academic Papers | svg |
064e534a6717ce79bdbaeb6b2a1a07dc0af2ed9f0ca9fe33a989ef4f21371f92 | 2026-01-21T00:00:00-05:00 | Fusion Segment Transformer: Bi-Directional Attention Guided Fusion Network for AI-Generated Music Detection | arXiv:2601.13647v1 Announce Type: new Abstract: With the rise of generative AI technology, anyone can now easily create and deploy AI-generated music, which has heightened the need for technical solutions to address copyright and ownership issues. While existing works mainly focused on short-audio, the challenge of ful... | https://arxiv.org/abs/2601.13647 | Academic Papers | svg |
6cdff121bba36986d34ce41ae5d7e12d9b47a2b0f1a4d0b038893bfb32d6b9a1 | 2026-01-21T00:00:00-05:00 | Fairness or Fluency? An Investigation into Language Bias of Pairwise LLM-as-a-Judge | arXiv:2601.13649v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have incentivized the development of LLM-as-a-judge, an application of LLMs where they are used as judges to decide the quality of a certain piece of text given a certain context. However, previous studies have demonstrated ... | https://arxiv.org/abs/2601.13649 | Academic Papers | svg |
7c250b7c5427dee3167706d2c1e8a6fe3e30ece594aebc1327c312ba6f30deb8 | 2026-01-21T00:00:00-05:00 | Face-Voice Association with Inductive Bias for Maximum Class Separation | arXiv:2601.13651v1 Announce Type: new Abstract: Face-voice association is widely studied in multimodal learning and is approached representing faces and voices with embeddings that are close for a same person and well separated from those of others. Previous work achieved this with loss functions. Recent advancements i... | https://arxiv.org/abs/2601.13651 | Academic Papers | svg |
bb8392305e031acedf9adc44d8868413dbd7971b2897e534a6ecf7e883d3f743 | 2026-01-21T00:00:00-05:00 | TimeART: Towards Agentic Time Series Reasoning via Tool-Augmentation | arXiv:2601.13653v1 Announce Type: new Abstract: Time series data widely exist in real-world cyber-physical systems. Though analyzing and interpreting them contributes to significant values, e.g, disaster prediction and financial risk control, current workflows mainly rely on human data scientists, which requires signif... | https://arxiv.org/abs/2601.13653 | Academic Papers | svg |
04fd3b6373fc793858b3952f4041c5d0a0ec72232a81c624c217fb6612c803c3 | 2026-01-21T00:00:00-05:00 | Why Does the LLM Stop Computing: An Empirical Study of User-Reported Failures in Open-Source LLMs | arXiv:2601.13655v1 Announce Type: new Abstract: The democratization of open-source Large Language Models (LLMs) allows users to fine-tune and deploy models on local infrastructure but exposes them to a First Mile deployment landscape. Unlike black-box API consumption, the reliability of user-managed orchestration remai... | https://arxiv.org/abs/2601.13655 | Academic Papers | svg |
4f14980e5fa4e8acecb9885a2cc56ca9e95c2f5ed46eff58e1d81f69a283d52e | 2026-01-21T00:00:00-05:00 | Communication-Free Collective Navigation for a Swarm of UAVs via LiDAR-Based Deep Reinforcement Learning | arXiv:2601.13657v1 Announce Type: new Abstract: This paper presents a deep reinforcement learning (DRL) based controller for collective navigation of unmanned aerial vehicle (UAV) swarms in communication-denied environments, enabling robust operation in complex, obstacle-rich environments. Inspired by biological swarms... | https://arxiv.org/abs/2601.13657 | Academic Papers | svg |
7a85dcb5e6867faac267a9dfac062515f30a771748b8f3a35872901293d91b41 | 2026-01-21T00:00:00-05:00 | Beyond Known Facts: Generating Unseen Temporal Knowledge to Address Data Contamination in LLM Evaluation | arXiv:2601.13658v1 Announce Type: new Abstract: The automatic extraction of information is important for populating large web knowledge bases such as Wikidata. The temporal version of that task, temporal knowledge graph extraction (TKGE), involves extracting temporally grounded facts from text, represented as semantic ... | https://arxiv.org/abs/2601.13658 | Academic Papers | svg |
c2c86e068a61c108dff7902d1d2dce225626e9b16b4b994a381d3aa1d05c6dc2 | 2026-01-21T00:00:00-05:00 | Temporal-Spatial Decouple before Act: Disentangled Representation Learning for Multimodal Sentiment Analysis | arXiv:2601.13659v1 Announce Type: new Abstract: Multimodal Sentiment Analysis integrates Linguistic, Visual, and Acoustic. Mainstream approaches based on modality-invariant and modality-specific factorization or on complex fusion still rely on spatiotemporal mixed modeling. This ignores spatiotemporal heterogeneity, le... | https://arxiv.org/abs/2601.13659 | Academic Papers | svg |
a1a639a71a47f724d05c957eddd0a3159de0a991f80fd8981fac22a3c72fa8ae | 2026-01-21T00:00:00-05:00 | Reinforcement Learning for Opportunistic Routing in Software-Defined LEO-Terrestrial Systems | arXiv:2601.13662v1 Announce Type: new Abstract: The proliferation of large-scale low Earth orbit (LEO) satellite constellations is driving the need for intelligent routing strategies that can effectively deliver data to terrestrial networks under rapidly time-varying topologies and intermittent gateway visibility. Leve... | https://arxiv.org/abs/2601.13662 | Academic Papers | svg |
9b10853dea962c39b0469fa3372119d53329ffccf566a49b56ff56495cbd3ff6 | 2026-01-21T00:00:00-05:00 | On the stability, complexity, and distribution of similarity classes of the longest edge bisection process for triangles | arXiv:2601.13663v1 Announce Type: new Abstract: The Longest Edge Bisection (LEB) of a triangle is performed by joining the midpoint of its longest edge to the opposite vertex. Applying this procedure iteratively produces an infinite family of triangles. Surprisingly, a classical result of Adler (1983) shows that for an... | https://arxiv.org/abs/2601.13663 | Academic Papers | svg |
b99002644d5e7f2854558e9f43a69d54769912f03fe918f3f671ac288cdb391c | 2026-01-21T00:00:00-05:00 | VIAFormer: Voxel-Image Alignment Transformer for High-Fidelity Voxel Refinement | arXiv:2601.13664v1 Announce Type: new Abstract: We propose VIAFormer, a Voxel-Image Alignment Transformer model designed for Multi-view Conditioned Voxel Refinement--the task of repairing incomplete noisy voxels using calibrated multi-view images as guidance. Its effectiveness stems from a synergistic design: an Image ... | https://arxiv.org/abs/2601.13664 | Academic Papers | svg |
1992e357b34ae1e2486e321cb80609afadb2188edb20d21d5b6364a35e38fe19 | 2026-01-21T00:00:00-05:00 | Transformer based Multi-task Fusion Network for Food Spoilage Detection and Shelf life Forecasting | arXiv:2601.13665v1 Announce Type: new Abstract: Food wastage is one of the critical challenges in the agricultural supply chain, and accurate and effective spoilage detection can help to reduce it. Further, it is highly important to forecast the spoilage information. This aids the longevity of the supply chain manageme... | https://arxiv.org/abs/2601.13665 | Academic Papers | svg |
51e718b95b340bf31a44ede5e960d8c94cf392a1b868810f20a3cba521a8cbca | 2026-01-21T00:00:00-05:00 | CommunityBench: Benchmarking Community-Level Alignment across Diverse Groups and Tasks | arXiv:2601.13669v1 Announce Type: new Abstract: Large language models (LLMs) alignment ensures model behaviors reflect human value. Existing alignment strategies primarily follow two paths: one assumes a universal value set for a unified goal (i.e., one-size-fits-all), while the other treats every individual as unique ... | https://arxiv.org/abs/2601.13669 | Academic Papers | svg |
4a9355177df80dc1d6d457549ca86f19f5308df0443bbe80452640477e93c106 | 2026-01-21T00:00:00-05:00 | The Orchestration of Multi-Agent Systems: Architectures, Protocols, and Enterprise Adoption | arXiv:2601.13671v1 Announce Type: new Abstract: Orchestrated multi-agent systems represent the next stage in the evolution of artificial intelligence, where autonomous agents collaborate through structured coordination and communication to achieve complex, shared objectives. This paper consolidates and formalizes the t... | https://arxiv.org/abs/2601.13671 | Academic Papers | svg |
812e4725986c0e628c722aff3f55a2766f8ef79e66a96311e03c3fa5acd48c2c | 2026-01-21T00:00:00-05:00 | Autoregressive deep learning for real-time simulation of soft tissue dynamics during virtual neurosurgery | arXiv:2601.13676v1 Announce Type: new Abstract: Accurate simulation of brain deformation is a key component for developing realistic, interactive neurosurgical simulators, as complex nonlinear deformations must be captured to ensure realistic tool-tissue interactions. However, traditional numerical solvers often fall s... | https://arxiv.org/abs/2601.13676 | Academic Papers | svg |
2eb70f492db27edfd691b9d2e13263005307cb9d3e703c033c26cea4d62f56f8 | 2026-01-21T00:00:00-05:00 | Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging | arXiv:2601.13677v1 Announce Type: new Abstract: Active learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image segmentation, where expert labeling of volumetric data is both time-consuming and expensive. Yet, existing AL methods are unable to consistently outperform improved random... | https://arxiv.org/abs/2601.13677 | Academic Papers | svg |
636ae61cc826d040c85895c97ef9daf42ab0e0a5634f6f6f072a74ad729ea977 | 2026-01-21T00:00:00-05:00 | Ultra-Lightweight Network for Ship-Radiated Sound Classification on Embedded Deployment | arXiv:2601.13679v1 Announce Type: new Abstract: This letter presents ShuffleFAC, a lightweight acoustic model for ship-radiated sound classification in resource-constrained maritime monitoring systems. ShuffleFAC integrates Frequency-Aware convolution into an efficiency-oriented backbone using separable convolution, po... | https://arxiv.org/abs/2601.13679 | Academic Papers | svg |
b6feb1ef7e6903964f41a788768aa72f6aba7ff1edfb8385ca63cf4821542516 | 2026-01-21T00:00:00-05:00 | ORCA -- An Automated Threat Analysis Pipeline for O-RAN Continuous Development | arXiv:2601.13681v1 Announce Type: new Abstract: The Open-Radio Access Network (O-RAN) integrates numerous software components in a cloud-like deployment, opening the radio access network to previously unconsidered security threats. With the ever-evolving threat landscape, integrating security practices through a DevSec... | https://arxiv.org/abs/2601.13681 | Academic Papers | svg |
b4f48c35acb62e963cfcd9d6d8dc7c11febcefe5b4ae10a1258016be23b74c43 | 2026-01-21T00:00:00-05:00 | CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation | arXiv:2601.13682v1 Announce Type: new Abstract: The rise of reasoning models necessitates large-scale verifiable data, for which programming tasks serve as an ideal source. However, while competitive programming platforms provide abundant problems and solutions, high-quality test cases for verification remain scarce. E... | https://arxiv.org/abs/2601.13682 | Academic Papers | svg |
747f3c289fe9c8108f284642b2edea2ad7231e1296860b9c7ac9d96cae3f8089 | 2026-01-21T00:00:00-05:00 | Dynamic Differential Linear Attention: Enhancing Linear Diffusion Transformer for High-Quality Image Generation | arXiv:2601.13683v1 Announce Type: new Abstract: Diffusion transformers (DiTs) have emerged as a powerful architecture for high-fidelity image generation, yet the quadratic cost of self-attention poses a major scalability bottleneck. To address this, linear attention mechanisms have been adopted to reduce computational ... | https://arxiv.org/abs/2601.13683 | Academic Papers | svg |
2323360c5a11d7875d3aa1736de5b26da596b17bb42f98ac7fbd65b8d2df2c66 | 2026-01-21T00:00:00-05:00 | HeteroCache: A Dynamic Retrieval Approach to Heterogeneous KV Cache Compression for Long-Context LLM Inference | arXiv:2601.13684v1 Announce Type: new Abstract: The linear memory growth of the KV cache poses a significant bottleneck for LLM inference in long-context tasks. Existing static compression methods often fail to preserve globally important information, principally because they overlook the attention drift phenomenon whe... | https://arxiv.org/abs/2601.13684 | Academic Papers | svg |
bb02fd702e9d6b3220fda992a3522538ee6c8f71a0d9c2e0828bd9dcbd8581ee | 2026-01-21T00:00:00-05:00 | Understanding Mental States to Guide Social Influence in Multi-Person Group Dialogue | arXiv:2601.13687v1 Announce Type: new Abstract: Existing dynamic Theory of Mind (ToM) benchmarks mostly place language models in a passive role: the model reads a sequence of connected scenarios and reports what people believe, feel, intend, and do as these states change. In real social interaction, ToM is also used fo... | https://arxiv.org/abs/2601.13687 | Academic Papers | svg |
c39aa7792359e200b969b1ffedb17245a1db8398f321417353d2eef825b3459b | 2026-01-21T00:00:00-05:00 | Criminator: An Easy-to-Use XR "Crime Animator" for Rapid Reconstruction and Analysis of Dynamic Crime Scenes | arXiv:2601.13689v1 Announce Type: new Abstract: Law enforcement authorities are increasingly interested in 3D modelling for virtual crime scene reconstruction, enabling offline analysis without the cost and contamination risk of on-site investigation. Past work has demonstrated spatial relationships through static mode... | https://arxiv.org/abs/2601.13689 | Academic Papers | svg |
5d76faa53f6fd592cb223c398f603f0694cfca99eeb404221e0624d0b03fc4d2 | 2026-01-21T00:00:00-05:00 | Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning | arXiv:2601.13690v1 Announce Type: new Abstract: Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face notable challenges, including high maintenance costs and low generalization capability. Recently, Large Language Models (LLMs) have been widely adopted in health... | https://arxiv.org/abs/2601.13690 | Academic Papers | svg |
95a09957c776347d0ebc1858bdafa3a97b80dd6383a58f82476f5ca1d1a06c5a | 2026-01-21T00:00:00-05:00 | Generative Intent Prediction Agentic AI empowered Edge Service Function Chain Orchestration | arXiv:2601.13694v1 Announce Type: new Abstract: With the development of artificial intelligence (AI), Agentic AI (AAI) based on large language models (LLMs) is gradually being applied to network management. However, in edge network environments, high user mobility and implicit service intents pose significant challenge... | https://arxiv.org/abs/2601.13694 | Academic Papers | svg |
2cc461cc68827f51c9c88098ad29169c96b93c23d3c072a093e2021c1fe20135 | 2026-01-21T00:00:00-05:00 | OptiSQL: Executable SQL Generation from Optical TokensOptiSQL: Executable SQL Generation from Optical Tokens | arXiv:2601.13695v1 Announce Type: new Abstract: Executable SQL generation is typically studied in text-to-SQL settings, where tables are provided as fully linearized textual schemas and contents. While effective, this formulation assumes access to structured text and incurs substantial token overhead, which is misalign... | https://arxiv.org/abs/2601.13695 | Academic Papers | svg |
ff527609b3785e9b8434c633837b832d41cd60b540d51e066aa7082868ded765 | 2026-01-21T00:00:00-05:00 | Uncertainty-Aware Gradient Signal-to-Noise Data Selection for Instruction Tuning | arXiv:2601.13697v1 Announce Type: new Abstract: Instruction tuning is a standard paradigm for adapting large language models (LLMs), but modern instruction datasets are large, noisy, and redundant, making full-data fine-tuning costly and often unnecessary. Existing data selection methods either build expensive gradient... | https://arxiv.org/abs/2601.13697 | Academic Papers | svg |
61e66af673dc7707961c82c8c7a2b7ebf2b9c22ffa239c5225bc5bc7ae81b68c | 2026-01-21T00:00:00-05:00 | Does Privacy Always Harm Fairness? Data-Dependent Trade-offs via Chernoff Information Neural Estimation | arXiv:2601.13698v1 Announce Type: new Abstract: Fairness and privacy are two vital pillars of trustworthy machine learning. Despite extensive research on these individual topics, the relationship between fairness and privacy has received significantly less attention. In this paper, we utilize the information-theoretic ... | https://arxiv.org/abs/2601.13698 | Academic Papers | svg |
b878e4dd96390b6ead61ddacffec77a55265cb9f106382447303c1336586c4c4 | 2026-01-21T00:00:00-05:00 | DistilMOS: Layer-Wise Self-Distillation For Self-Supervised Learning Model-Based MOS Prediction | arXiv:2601.13700v1 Announce Type: new Abstract: With the advancement of self-supervised learning (SSL), fine-tuning pretrained SSL models for mean opinion score (MOS) prediction has achieved state-of-the-art performance. However, during fine-tuning, these SSL-based MOS prediction models often suffer from catastrophic f... | https://arxiv.org/abs/2601.13700 | Academic Papers | svg |
66a1b5ab948eac38304cca557e34136320cd6725aa76f72f9699795b8f1892e3 | 2026-01-21T00:00:00-05:00 | IGAA: Intent-Driven General Agentic AI for Edge Services Scheduling using Generative Meta Learning | arXiv:2601.13702v1 Announce Type: new Abstract: Agentic AI (AAI), which extends Large Language Models with enhanced reasoning capabilities, has emerged as a promising paradigm for autonomous edge service scheduling. However, user mobility creates highly dynamic service demands in edge networks, and existing service sch... | https://arxiv.org/abs/2601.13702 | Academic Papers | svg |
04fda3cdef46e5c6ef08f93082cc2143a0d9f92cc83972048a75716bd80a1dc7 | 2026-01-21T00:00:00-05:00 | Performance and Complexity Trade-off Optimization of Speech Models During Training | arXiv:2601.13704v1 Announce Type: new Abstract: In speech machine learning, neural network models are typically designed by choosing an architecture with fixed layer sizes and structure. These models are then trained to maximize performance on metrics aligned with the task's objective. While the overall architecture is... | https://arxiv.org/abs/2601.13704 | Academic Papers | svg |
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