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5f121604f1060de50eb239906453408ed198385c3e6d5edfff0947bd79a643e0 | 2026-01-23T00:00:00-05:00 | CoNRec: Context-Discerning Negative Recommendation with LLMs | arXiv:2601.15721v1 Announce Type: new Abstract: Understanding what users like is relatively straightforward; understanding what users dislike, however, remains a challenging and underexplored problem. Research into users' negative preferences has gained increasing importance in modern recommendation systems. Numerous p... | https://arxiv.org/abs/2601.15721 | Academic Papers | svg |
a6b47529884c1afb7517f5e43b8961ecc979c39698efb7e978798a072925a47d | 2026-01-23T00:00:00-05:00 | Communication-efficient Federated Graph Classification via Generative Diffusion Modeling | arXiv:2601.15722v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) unlock new ways of learning from graph-structured data, proving highly effective in capturing complex relationships and patterns. Federated GNNs (FGNNs) have emerged as a prominent distributed learning paradigm for training GNNs over decentral... | https://arxiv.org/abs/2601.15722 | Academic Papers | svg |
83933f03767af23cf89e55a2988f1fbb90a1c32c62cc3493c72fa4cfb6c9ed21 | 2026-01-23T00:00:00-05:00 | Generalized Information Inequalities via Submodularity, and Two Combinatorial Problems | arXiv:2601.15723v1 Announce Type: new Abstract: It is well known that there is a strong connection between entropy inequalities and submodularity, since the entropy of a collection of random variables is a submodular function. Unifying frameworks for information inequalities arising from submodularity were developed by... | https://arxiv.org/abs/2601.15723 | Academic Papers | svg |
5f40e2e6c8174597a8eaaf82cc1a77531df042c67d4f11980cd423e98c07ec54 | 2026-01-23T00:00:00-05:00 | VideoThinker: Building Agentic VideoLLMs with LLM-Guided Tool Reasoning | arXiv:2601.15724v1 Announce Type: new Abstract: Long-form video understanding remains a fundamental challenge for current Video Large Language Models. Most existing models rely on static reasoning over uniformly sampled frames, which weakens temporal localization and leads to substantial information loss in long videos... | https://arxiv.org/abs/2601.15724 | Academic Papers | svg |
450c4427241dd13e0f4dd9df4dfa1739244110cf7efdc433323f75a41d97ed1f | 2026-01-23T00:00:00-05:00 | Profit Maximization for Viral Marketing in Online Social Networks using Two Phase Diffusion Approach | arXiv:2601.15726v1 Announce Type: new Abstract: Now-a-days, Online Social Networks (OSNs) are extensively used by different commercial houses for viral marketing. The key problem that arises in this context is to choose a limited number of highly influential users as the initial adopters of a brand such that the influe... | https://arxiv.org/abs/2601.15726 | Academic Papers | svg |
833303cd1af8faff89df8a1a540e22be0c83dff4d08805ece0090ea298360a90 | 2026-01-23T00:00:00-05:00 | Towards Automated Kernel Generation in the Era of LLMs | arXiv:2601.15727v1 Announce Type: new Abstract: The performance of modern AI systems is fundamentally constrained by the quality of their underlying kernels, which translate high-level algorithmic semantics into low-level hardware operations. Achieving near-optimal kernels requires expert-level understanding of hardwar... | https://arxiv.org/abs/2601.15727 | Academic Papers | svg |
812a3e987a24960ae2cf7e5dda006ca4cd0b3c780c834f78d9523026211a1d67 | 2026-01-23T00:00:00-05:00 | Benchmarking Text-to-Python against Text-to-SQL: The Impact of Explicit Logic and Ambiguity | arXiv:2601.15728v1 Announce Type: new Abstract: While Text-to-SQL remains the dominant approach for database interaction, real-world analytics increasingly require the flexibility of general-purpose programming languages such as Python or Pandas to manage file-based data and complex analytical workflows. Despite this g... | https://arxiv.org/abs/2601.15728 | Academic Papers | svg |
1de0950ef26482d463a50818e95ac17fc7b82fb17e4c45ea8a1e5c5d3b9dd9a8 | 2026-01-23T00:00:00-05:00 | DualShield: Safe Model Predictive Diffusion via Reachability Analysis for Interactive Autonomous Driving | arXiv:2601.15729v1 Announce Type: new Abstract: Diffusion models have emerged as a powerful approach for multimodal motion planning in autonomous driving. However, their practical deployment is typically hindered by the inherent difficulty in enforcing vehicle dynamics and a critical reliance on accurate predictions of... | https://arxiv.org/abs/2601.15729 | Academic Papers | svg |
36053b5270c8821910700de261e115c21a02e69aaa2524cec5007970c62a78e6 | 2026-01-23T00:00:00-05:00 | FAIR-ESI: Feature Adaptive Importance Refinement for Electrophysiological Source Imaging | arXiv:2601.15731v1 Announce Type: new Abstract: An essential technique for diagnosing brain disorders is electrophysiological source imaging (ESI). While model-based optimization and deep learning methods have achieved promising results in this field, the accurate selection and refinement of features remains a central ... | https://arxiv.org/abs/2601.15731 | Academic Papers | svg |
ac69b9defa308abe71affe98e5fab8a6c0cf806e25d784ae12beb3382e7b0904 | 2026-01-23T00:00:00-05:00 | Sub-Region-Aware Modality Fusion and Adaptive Prompting for Multi-Modal Brain Tumor Segmentation | arXiv:2601.15734v1 Announce Type: new Abstract: The successful adaptation of foundation models to multi-modal medical imaging is a critical yet unresolved challenge. Existing models often struggle to effectively fuse information from multiple sources and adapt to the heterogeneous nature of pathological tissues. To add... | https://arxiv.org/abs/2601.15734 | Academic Papers | svg |
b3e00489c82b8e534c12fb149367f59b18db23092f451b12a8e6d06e1a3d2319 | 2026-01-23T00:00:00-05:00 | PhysProver: Advancing Automatic Theorem Proving for Physics | arXiv:2601.15737v1 Announce Type: new Abstract: The combination of verifiable languages and LLMs has significantly influenced both the mathematical and computer science communities because it provides a rigorous foundation for theorem proving. Recent advancements in the field provide foundation models and sophisticated... | https://arxiv.org/abs/2601.15737 | Academic Papers | svg |
cb209bbe565351db844c4a3dfa7a90d54c127b79b4eccca504e9430def6d5981 | 2026-01-23T00:00:00-05:00 | LLM-Assisted Automatic Dispatching Rule Design for Dynamic Flexible Assembly Flow Shop Scheduling | arXiv:2601.15738v1 Announce Type: new Abstract: Dynamic multi-product delivery environments demand rapid coordination of part completion and product-level kitting within hybrid processing and assembly systems to satisfy strict hierarchical supply constraints. The flexible assembly flow shop scheduling problem formally ... | https://arxiv.org/abs/2601.15738 | Academic Papers | svg |
414e478367c0d7552251b9013bf842eaebb35e6e3e6ee5969617a984ca017ad2 | 2026-01-23T00:00:00-05:00 | Breaking the Resolution Barrier: Arbitrary-resolution Deep Image Steganography Framework | arXiv:2601.15739v1 Announce Type: new Abstract: Deep image steganography (DIS) has achieved significant results in capacity and invisibility. However, current paradigms enforce the secret image to maintain the same resolution as the cover image during hiding and revealing. This leads to two challenges: secret images wi... | https://arxiv.org/abs/2601.15739 | Academic Papers | svg |
bbcc12ff6bf6c8a2f3f10f079795074499d13f5de321f548dd8e27cf82405086 | 2026-01-23T00:00:00-05:00 | Hallucination Mitigating for Medical Report Generation | arXiv:2601.15745v1 Announce Type: new Abstract: In the realm of medical report generation (MRG), the integration of natural language processing has emerged as a vital tool to alleviate the workload of radiologists. Despite the impressive capabilities demonstrated by large vision language models (LVLMs) in understanding... | https://arxiv.org/abs/2601.15745 | Academic Papers | svg |
00fc12932a84fd9942f9a327ddf32b8506d8dc276d3182d99c83d2264ab44ba5 | 2026-01-23T00:00:00-05:00 | Tabular Incremental Inference | arXiv:2601.15751v1 Announce Type: new Abstract: Tabular data is a fundamental form of data structure. The evolution of table analysis tools reflects humanity's continuous progress in data acquisition, management, and processing. The dynamic changes in table columns arise from technological advancements, changing needs,... | https://arxiv.org/abs/2601.15751 | Academic Papers | svg |
4923f282c2375b949aa3c671fa335e399c0113c2bb2614a15a4e43402bfbe5e9 | 2026-01-23T00:00:00-05:00 | CAFE-GB: Scalable and Stable Feature Selection for Malware Detection via Chunk-wise Aggregated Gradient Boosting | arXiv:2601.15754v1 Announce Type: new Abstract: High-dimensional malware datasets often exhibit feature redundancy, instability, and scalability limitations, which hinder the effectiveness and interpretability of machine learning-based malware detection systems. Although feature selection is commonly employed to mitiga... | https://arxiv.org/abs/2601.15754 | Academic Papers | svg |
9670539357e76e32a04103eda8a42438c4fcb13043de49252b12f1c9b67013dd | 2026-01-23T00:00:00-05:00 | Beyond Marginal Distributions: A Framework to Evaluate the Representativeness of Demographic-Aligned LLMs | arXiv:2601.15755v1 Announce Type: new Abstract: Large language models are increasingly used to represent human opinions, values, or beliefs, and their steerability towards these ideals is an active area of research. Existing work focuses predominantly on aligning marginal response distributions, treating each survey it... | https://arxiv.org/abs/2601.15755 | Academic Papers | svg |
7112ede4a95dbe6f3529275d308ce9af38a9f17d5d33b2c3b3c19cc24def2818 | 2026-01-23T00:00:00-05:00 | CTL* Model Checking on Infinite Families of Finite-State Labeled Transition Systems (Technical Report) | arXiv:2601.15756v1 Announce Type: new Abstract: We study model checking algorithms for infinite families of finite-state labeled transition systems against temporal properties written in CTL*. Such families arise, for example, as models of highly configurable systems or software product lines. We model families using c... | https://arxiv.org/abs/2601.15756 | Academic Papers | svg |
9a084787a9c5af4d7fca012bf173ebb7c2dbfc8b113766de71137021671d357c | 2026-01-23T00:00:00-05:00 | White-Box mHC: Electromagnetic Spectrum-Aware and Interpretable Stream Interactions for Hyperspectral Image Classification | arXiv:2601.15757v1 Announce Type: new Abstract: In hyperspectral image classification (HSIC), most deep learning models rely on opaque spectral-spatial feature mixing, limiting their interpretability and hindering understanding of internal decision mechanisms. We present physical spectrum-aware white-box mHC, named ES-... | https://arxiv.org/abs/2601.15757 | Academic Papers | svg |
611d00f7e4991156ffc470e084ec92bc1b063879be72cd1db44595615f147c6d | 2026-01-23T00:00:00-05:00 | NL4ST: A Natural Language Query Tool for Spatio-Temporal Databases | arXiv:2601.15758v1 Announce Type: new Abstract: The advancement of mobile computing devices and positioning technologies has led to an explosive growth of spatio-temporal data managed in databases. Representative queries over such data include range queries, nearest neighbor queries, and join queries. However, formulat... | https://arxiv.org/abs/2601.15758 | Academic Papers | svg |
cfceee272cbd4f96fcc624c3854c5e4151a90bca4d21dd0506d24a582a128c1c | 2026-01-23T00:00:00-05:00 | Atlas-Assisted Segment Anything Model for Fetal Brain MRI (FeTal-SAM) | arXiv:2601.15759v1 Announce Type: new Abstract: This paper presents FeTal-SAM, a novel adaptation of the Segment Anything Model (SAM) tailored for fetal brain MRI segmentation. Traditional deep learning methods often require large annotated datasets for a fixed set of labels, making them inflexible when clinical or res... | https://arxiv.org/abs/2601.15759 | Academic Papers | svg |
7d46931a95bb03edc853003033582b03d8b52b313f968ad955aeb02f8f705e8a | 2026-01-23T00:00:00-05:00 | Off-Policy Actor-Critic with Sigmoid-Bounded Entropy for Real-World Robot Learning | arXiv:2601.15761v1 Announce Type: new Abstract: Deploying reinforcement learning in the real world remains challenging due to sample inefficiency, sparse rewards, and noisy visual observations. Prior work leverages demonstrations and human feedback to improve learning efficiency and robustness. However, offline-to-onli... | https://arxiv.org/abs/2601.15761 | Academic Papers | svg |
f3cf9a8bbe78c2176f937bde688f72bace70b1f99f0a7a6dacc700a802eee393 | 2026-01-23T00:00:00-05:00 | NMRGym: A Comprehensive Benchmark for Nuclear Magnetic Resonance Based Molecular Structure Elucidation | arXiv:2601.15763v1 Announce Type: new Abstract: Nuclear Magnetic Resonance (NMR) spectroscopy is the cornerstone of small-molecule structure elucidation. While deep learning has demonstrated significant potential in automating structure elucidation and spectral simulation, current progress is severely impeded by the re... | https://arxiv.org/abs/2601.15763 | Academic Papers | svg |
665bfc0552f7c95bd07271962ca7180558b04c8fa7e7494cf66fc87f374a5db5 | 2026-01-23T00:00:00-05:00 | LL-GaussianMap: Zero-shot Low-Light Image Enhancement via 2D Gaussian Splatting Guided Gain Maps | arXiv:2601.15766v1 Announce Type: new Abstract: Significant progress has been made in low-light image enhancement with respect to visual quality. However, most existing methods primarily operate in the pixel domain or rely on implicit feature representations. As a result, the intrinsic geometric structural priors of im... | https://arxiv.org/abs/2601.15766 | Academic Papers | svg |
1d44163d1949f10ba15cdf3b26e1816c30a82d92134f9a3d1cf5f98dc25681ac | 2026-01-23T00:00:00-05:00 | Recursive Flow: A Generative Framework for MIMO Channel Estimation | arXiv:2601.15767v1 Announce Type: new Abstract: Channel estimation is a fundamental challenge in massive multiple-input multiple-output systems, where estimation accuracy governs the spectral efficiency and link reliability. In this work, we introduce Recursive Flow (RC-Flow), a novel solver that leverages pre-trained ... | https://arxiv.org/abs/2601.15767 | Academic Papers | svg |
453a65fa04183c26993d5012d4b7774a866cbf593e8aa52d55a204af0e138537 | 2026-01-23T00:00:00-05:00 | Rethinking Drug-Drug Interaction Modeling as Generalizable Relation Learning | arXiv:2601.15771v1 Announce Type: new Abstract: Drug-drug interaction (DDI) prediction is central to drug discovery and clinical development, particularly in the context of increasingly prevalent polypharmacy. Although existing computational methods achieve strong performance on standard benchmarks, they often fail to ... | https://arxiv.org/abs/2601.15771 | Academic Papers | svg |
13b5ad1612d5fdbd80012b8c8a6aebb4be2ee4f49d8a7954d5a2ded17347347a | 2026-01-23T00:00:00-05:00 | LL-GaussianImage: Efficient Image Representation for Zero-shot Low-Light Enhancement with 2D Gaussian Splatting | arXiv:2601.15772v1 Announce Type: new Abstract: 2D Gaussian Splatting (2DGS) is an emerging explicit scene representation method with significant potential for image compression due to high fidelity and high compression ratios. However, existing low-light enhancement algorithms operate predominantly within the pixel do... | https://arxiv.org/abs/2601.15772 | Academic Papers | svg |
1556a7ac1ef54909b3765bee46aef0563b139bf9a7ab90c822be531e63f9bbc1 | 2026-01-23T00:00:00-05:00 | Next Generation Active Learning: Mixture of LLMs in the Loop | arXiv:2601.15773v1 Announce Type: new Abstract: With the rapid advancement and strong generalization capabilities of large language models (LLMs), they have been increasingly incorporated into the active learning pipelines as annotators to reduce annotation costs. However, considering the annotation quality, labels gen... | https://arxiv.org/abs/2601.15773 | Academic Papers | svg |
c4115373fe5f18f790c24c9d1c9196f161d436ef52902295c5f71599a2b46961 | 2026-01-23T00:00:00-05:00 | FirmReBugger: A Benchmark Framework for Monolithic Firmware Fuzzers | arXiv:2601.15774v1 Announce Type: new Abstract: Monolithic Firmware is widespread. Unsurprisingly, fuzz testing firmware is an active research field with new advances addressing the unique challenges in the domain. However, understanding and evaluating improvements by deriving metrics such as code coverage and unique c... | https://arxiv.org/abs/2601.15774 | Academic Papers | svg |
5e882293d280e63e4cd158a35d6e0d2f97e5dc08bd36b34f3abf4db87f92e160 | 2026-01-23T00:00:00-05:00 | Glove2UAV: A Wearable IMU-Based Glove for Intuitive Control of UAV | arXiv:2601.15775v1 Announce Type: new Abstract: This paper presents Glove2UAV, a wearable IMU-glove interface for intuitive UAV control through hand and finger gestures, augmented with vibrotactile warnings for exceeding predefined speed thresholds. To promote safer and more predictable interaction in dynamic flight, G... | https://arxiv.org/abs/2601.15775 | Academic Papers | svg |
a6149878fc62c74e0618bd37146557069f7537e99a81663f8b90be774791bfba | 2026-01-23T00:00:00-05:00 | UXCascade: Scalable Usability Testing with Simulated User Agents | arXiv:2601.15777v1 Announce Type: new Abstract: Simulated user agents are increasingly used in usability testing to support fast, iterative UX workflows, as they generate rich data such as action logs and think-aloud reasoning, but the unstructured nature of this output often obscures actionable insights. We present UX... | https://arxiv.org/abs/2601.15777 | Academic Papers | svg |
d5832bae371bae527b31c892df5ec33ce4788e3efae030a7f07ea2bc5cd45461 | 2026-01-23T00:00:00-05:00 | Agentic Confidence Calibration | arXiv:2601.15778v1 Announce Type: new Abstract: AI agents are rapidly advancing from passive language models to autonomous systems executing complex, multi-step tasks. Yet their overconfidence in failure remains a fundamental barrier to deployment in high-stakes settings. Existing calibration methods, built for static ... | https://arxiv.org/abs/2601.15778 | Academic Papers | svg |
a275c4a4e2bed08d4a292a98c6501d3c92991f434a7a1e17266dbeb2587408a4 | 2026-01-23T00:00:00-05:00 | Diffusion Model-Based Data Augmentation for Enhanced Neuron Segmentation | arXiv:2601.15779v1 Announce Type: new Abstract: Neuron segmentation in electron microscopy (EM) aims to reconstruct the complete neuronal connectome; however, current deep learning-based methods are limited by their reliance on large-scale training data and extensive, time-consuming manual annotations. Traditional meth... | https://arxiv.org/abs/2601.15779 | Academic Papers | svg |
d3b1b3355f5e7564c4e2067d8dcbb2bfb4900d72cbef8b042081b76f2a5791a8 | 2026-01-23T00:00:00-05:00 | Assessing Situational and Spatial Awareness of VLMs with Synthetically Generated Video | arXiv:2601.15780v1 Announce Type: new Abstract: Spatial reasoning in vision language models (VLMs) remains fragile when semantics hinge on subtle temporal or geometric cues. We introduce a synthetic benchmark that probes two complementary skills: situational awareness (recognizing whether an interaction is harmful or b... | https://arxiv.org/abs/2601.15780 | Academic Papers | svg |
b6d78ed170839614d443a001c0e628ba7440e444ad0a6094fea06b4df219466e | 2026-01-23T00:00:00-05:00 | Endowing Molecular Language with Geometry Perception via Modality Compensation for High-Throughput Quantum Hamiltonian Prediction | arXiv:2601.15786v1 Announce Type: new Abstract: The quantum Hamiltonian is a fundamental property that governs a molecule's electronic structure and behavior, and its calculation and prediction are paramount in computational chemistry and materials science. Accurate prediction is highly reliant on extensive training da... | https://arxiv.org/abs/2601.15786 | Academic Papers | svg |
ed0caaa991e756b9cdbad8f57d4e5946be80716d2daa25c7cf9513f3ded073dc | 2026-01-23T00:00:00-05:00 | Efficient Numerical Reconstruction of Wave Equation Sources via Droplet-Induced Asymptotics | arXiv:2601.15787v1 Announce Type: new Abstract: In this paper, we develop and numerically implement a novel approach for solving the inverse source problem of the acoustic wave equation in three dimensions. By injecting a small high-contrast droplet into the medium, we exploit the resulting wave field perturbation meas... | https://arxiv.org/abs/2601.15787 | Academic Papers | svg |
9c06157ab06848c4fa4a42baa042472f748cb65c4c9f737af337964ab2daac3d | 2026-01-23T00:00:00-05:00 | HumanLLM: Towards Personalized Understanding and Simulation of Human Nature | arXiv:2601.15793v1 Announce Type: new Abstract: Motivated by the remarkable progress of large language models (LLMs) in objective tasks like mathematics and coding, there is growing interest in their potential to simulate human behavior--a capability with profound implications for transforming social science research a... | https://arxiv.org/abs/2601.15793 | Academic Papers | svg |
ae8f889529a1ca4ddd9655538c9ee61fe80357e1c69b8fa697dcb601e5fc8687 | 2026-01-23T00:00:00-05:00 | Creativity in the Age of AI: Rethinking the Role of Intentional Agency | arXiv:2601.15797v1 Announce Type: new Abstract: Many theorists of creativity maintain that intentional agency is a necessary condition of creativity. We argue that this requirement, which we call the Intentional Agency Condition (IAC), should be rejected as a general condition of creativity, while retaining its relevan... | https://arxiv.org/abs/2601.15797 | Academic Papers | svg |
372689a806161decfcfe15bc4bb9c06e0752d8393581d235133bb217bb4d3ce2 | 2026-01-23T00:00:00-05:00 | VitalDiagnosis: AI-Driven Ecosystem for 24/7 Vital Monitoring and Chronic Disease Management | arXiv:2601.15798v1 Announce Type: new Abstract: Chronic diseases have become the leading cause of death worldwide, a challenge intensified by strained medical resources and an aging population. Individually, patients often struggle to interpret early signs of deterioration or maintain adherence to care plans. In this p... | https://arxiv.org/abs/2601.15798 | Academic Papers | svg |
a57f9f22423cb46dd2bc12e07e23da5910ac7dacf6f953bb3c0392f5e408bcac | 2026-01-23T00:00:00-05:00 | Attributing and Exploiting Safety Vectors through Global Optimization in Large Language Models | arXiv:2601.15801v1 Announce Type: new Abstract: While Large Language Models (LLMs) are aligned to mitigate risks, their safety guardrails remain fragile against jailbreak attacks. This reveals limited understanding of components governing safety. Existing methods rely on local, greedy attribution that assumes independe... | https://arxiv.org/abs/2601.15801 | Academic Papers | svg |
f614f6de16d1f8e4dc4a6f9b209e0dc377f777b07b85febccb207d69be4681bb | 2026-01-23T00:00:00-05:00 | A Beacon Based Solution for Autonomous UUVs GNSS-Denied Stealthy Navigation | arXiv:2601.15802v1 Announce Type: new Abstract: Autonomous Unmanned Underwater Vehicles (UUVs) enable military and civilian covert operations in coastal areas without relying on support vessels or Global Navigation Satellite Systems (GNSS). Such operations are critical when surface access is not possible and stealthy n... | https://arxiv.org/abs/2601.15802 | Academic Papers | svg |
edfc7193fe25cbbd516ffdec6aee3400d7c93acf9ccbf99bd09a3ef2d76a56db | 2026-01-23T00:00:00-05:00 | Entangled Life and Code: A Computational Design Taxonomy for Synergistic Bio-Digital Systems | arXiv:2601.15804v1 Announce Type: new Abstract: Bio-digital systems that merge microbial life with technology promise new modes of computation, combining biological adaptability with digital precision. Yet realizing this potential symbiotically -- where biological and digital agents co-adapt and co-process -- remains e... | https://arxiv.org/abs/2601.15804 | Academic Papers | svg |
9176619881da157ed99c54ebd3254e21c1ce7e5082cec7abe7a6a8b135f6fff6 | 2026-01-23T00:00:00-05:00 | Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification | arXiv:2601.15808v1 Announce Type: new Abstract: Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving. While the majority of existing efforts focus on enhancing policy capabilities via post-training, we propose an alternative paradigm: self-evolving the agent'... | https://arxiv.org/abs/2601.15808 | Academic Papers | svg |
6a51d966a77e7a372acb9508f07c650ea0ec195141f11103452f4b2558f5aabc | 2026-01-23T00:00:00-05:00 | SteerEval: Inference-time Interventions Strengthen Multilingual Generalization in Neural Summarization Metrics | arXiv:2601.15809v1 Announce Type: new Abstract: An increasing body of work has leveraged multilingual language models for Natural Language Generation tasks such as summarization. A major empirical bottleneck in this area is the shortage of accurate and robust evaluation metrics for many languages, which hinders progres... | https://arxiv.org/abs/2601.15809 | Academic Papers | svg |
c350b7342f433fcf0e29cc7810ff63304634914ca4cb51946229b34c8e4f5b4a | 2026-01-23T00:00:00-05:00 | A Mobile Application for Flower Recognition System Based on Convolutional Neural Networks | arXiv:2601.15810v1 Announce Type: new Abstract: A convolutional neural network (CNN) is a deep learning algorithm that has been specifically designed for computer vision applications. The CNNs proved successful in handling the increasing amount of data in many computer vision problems, where classical machine learning ... | https://arxiv.org/abs/2601.15810 | Academic Papers | svg |
3a188347fef5c9ec802a3719e83a5de9ab0fef5505fa1a9e624cbb7778d0b010 | 2026-01-23T00:00:00-05:00 | Contractions of quasi relation algebras and applications to representability | arXiv:2601.15811v1 Announce Type: new Abstract: Quasi relation algebras (qRAs) were first described by Galatos and Jipsen in 2013. They are generalisations of relation algebras and can also be viewed as certain residuated lattice expansions. We identify positive symmetric idempotent elements in qRAs and show that they ... | https://arxiv.org/abs/2601.15811 | Academic Papers | svg |
b1868937603bc59a9db2a08807b48a7d57afdb2522302713113b056ec78bac54 | 2026-01-23T00:00:00-05:00 | ErrorMap and ErrorAtlas: Charting the Failure Landscape of Large Language Models | arXiv:2601.15812v1 Announce Type: new Abstract: Large Language Models (LLM) benchmarks tell us when models fail, but not why they fail. A wrong answer on a reasoning dataset may stem from formatting issues, calculation errors, or dataset noise rather than weak reasoning. Without disentangling such causes, benchmarks re... | https://arxiv.org/abs/2601.15812 | Academic Papers | svg |
77021fa0fa48d0e5cbccf52e45949cfba3f5a3b21b99dab44a303238381224ce | 2026-01-23T00:00:00-05:00 | Beyond Off-the-Shelf Models: A Lightweight and Accessible Machine Learning Pipeline for Ecologists Working with Image Data | arXiv:2601.15813v1 Announce Type: new Abstract: We introduce a lightweight experimentation pipeline designed to lower the barrier for applying machine learning (ML) methods for classifying images in ecological research. We enable ecologists to experiment with ML models independently, thus they can move beyond off-the-s... | https://arxiv.org/abs/2601.15813 | Academic Papers | svg |
42099162248231eb7e88db5baf85f9b1e5b0dba0a0942fa60d4ef1174b570d3d | 2026-01-23T00:00:00-05:00 | Improved Approximation Ratios for the Shortest Common Superstring Problem with Reverse Complements | arXiv:2601.15814v1 Announce Type: new Abstract: The Shortest Common Superstring (SCS) problem asks for the shortest string that contains each of a given set of strings as a substring. Its reverse-complement variant, the Shortest Common Superstring problem with Reverse Complements (SCS-RC), naturally arises in bioinform... | https://arxiv.org/abs/2601.15814 | Academic Papers | svg |
28ebe10cb35256e61df8c9dc35996236a5516cd040603923afb814b5e7dd1b52 | 2026-01-23T00:00:00-05:00 | Virtual Traffic Police: Large Language Model-Augmented Traffic Signal Control for Unforeseen Incidents | arXiv:2601.15816v1 Announce Type: new Abstract: Adaptive traffic signal control (TSC) has demonstrated strong effectiveness in managing dynamic traffic flows. However, conventional methods often struggle when unforeseen traffic incidents occur (e.g., accidents and road maintenance), which typically require labor-intens... | https://arxiv.org/abs/2601.15816 | Academic Papers | svg |
37f012ef00754f59ebb62f04781da1fdb77d1352447bd8895690937efe72e0c8 | 2026-01-23T00:00:00-05:00 | ExDR: Explanation-driven Dynamic Retrieval Enhancement for Multimodal Fake News Detection | arXiv:2601.15820v1 Announce Type: new Abstract: The rapid spread of multimodal fake news poses a serious societal threat, as its evolving nature and reliance on timely factual details challenge existing detection methods. Dynamic Retrieval-Augmented Generation provides a promising solution by triggering keyword-based r... | https://arxiv.org/abs/2601.15820 | Academic Papers | svg |
0f31823c2cf92ff4b1cc877f8e0da3cdc1c10aa88758979c099397275a80037c | 2026-01-23T00:00:00-05:00 | Introducing the Generative Application Firewall (GAF) | arXiv:2601.15824v1 Announce Type: new Abstract: This paper introduces the Generative Application Firewall (GAF), a new architectural layer for securing LLM applications. Existing defenses -- prompt filters, guardrails, and data-masking -- remain fragmented; GAF unifies them into a single enforcement point, much like a ... | https://arxiv.org/abs/2601.15824 | Academic Papers | svg |
d3ba0bcd8e8458d499821da2103ce732e43307155a6cb0b8a0008ba3bd4e927c | 2026-01-23T00:00:00-05:00 | Can professional translators identify machine-generated text? | arXiv:2601.15828v1 Announce Type: new Abstract: This study investigates whether professional translators can reliably identify short stories generated in Italian by artificial intelligence (AI) without prior specialized training. Sixty-nine translators took part in an in-person experiment, where they assessed three ano... | https://arxiv.org/abs/2601.15828 | Academic Papers | svg |
54fa11e402dad71d6a8693a02ee627bdd6611a4c9ff8e97d96bc1c91c70dd6c8 | 2026-01-23T00:00:00-05:00 | Towards Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion | arXiv:2601.15829v1 Announce Type: new Abstract: Recent years have witnessed the remarkable success of deep learning in remote sensing image interpretation, driven by the availability of large-scale benchmark datasets. However, this reliance on massive training data also brings two major challenges: (1) high storage and... | https://arxiv.org/abs/2601.15829 | Academic Papers | svg |
ea1095ffe200f8372a34afc92b7372f3b778457d1c9bbeedcc047b60fa2579b5 | 2026-01-23T00:00:00-05:00 | An IoT-Based Smart Plant Monitoring and Irrigation System with Real-Time Environmental Sensing, Automated Alerts, and Cloud Analytics | arXiv:2601.15830v1 Announce Type: new Abstract: The increasing global demand for sustainable agriculture necessitates intelligent monitoring systems that optimize resource utilization and plant health management. Traditional farming methods rely on manual observation and periodic watering, often leading to water wastag... | https://arxiv.org/abs/2601.15830 | Academic Papers | svg |
cf35ddef55aba133250ea2dae0ab5521b22843619bd941914ee1dfc71afecb5a | 2026-01-23T00:00:00-05:00 | RF Intelligence for Health: Classification of SmartBAN Signals in overcrowded ISM band | arXiv:2601.15836v1 Announce Type: new Abstract: Accurate classification of Radio-Frequency (RF) signals is essential for reliable wearable health-monitoring systems, providing awareness of the interference conditions in which medical protocols operate. In the overcrowded 2.4 GHz ISM band, however, identifying low-power... | https://arxiv.org/abs/2601.15836 | Academic Papers | svg |
d00c3b3c76e60efc755d247045ff2d8e83a3c3065f6b77fbe58ebd2dc4cc4ed2 | 2026-01-23T00:00:00-05:00 | TinySense: Effective CSI Compression for Scalable and Accurate Wi-Fi Sensing | arXiv:2601.15838v1 Announce Type: new Abstract: With the growing demand for device-free and privacy-preserving sensing solutions, Wi-Fi sensing has emerged as a promising approach for human pose estimation (HPE). However, existing methods often process vast amounts of channel state information (CSI) data directly, ulti... | https://arxiv.org/abs/2601.15838 | Academic Papers | svg |
ee40298fad499d9ac6ccf61ef94ff9448b17b943318b41ae8e8aa8a3f6257d19 | 2026-01-23T00:00:00-05:00 | Determinants of Training Corpus Size for Clinical Text Classification | arXiv:2601.15846v1 Announce Type: new Abstract: Introduction: Clinical text classification using natural language processing (NLP) models requires adequate training data to achieve optimal performance. For that, 200-500 documents are typically annotated. The number is constrained by time and costs and lacks justificati... | https://arxiv.org/abs/2601.15846 | Academic Papers | svg |
677b1f56431e853963fba89892cb6e16393a09a6aa4bfd62e917f2d8a3960a72 | 2026-01-23T00:00:00-05:00 | CGPT: Cluster-Guided Partial Tables with LLM-Generated Supervision for Table Retrieval | arXiv:2601.15849v1 Announce Type: new Abstract: General-purpose embedding models have demonstrated strong performance in text retrieval but remain suboptimal for table retrieval, where highly structured content leads to semantic compression and query-table mismatch. Recent LLM-based retrieval augmentation methods mitig... | https://arxiv.org/abs/2601.15849 | Academic Papers | svg |
8e34499b8aa40978a6c1ed2f4ea765f89dcf39d15f98602ad5ccf68b74c8b962 | 2026-01-23T00:00:00-05:00 | Practical applications of Set Shaping Theory to Non-Uniform Sequences | arXiv:2601.15853v1 Announce Type: new Abstract: Set Shaping Theory (SST) moves beyond the classical fixed-space model by constructing bijective mappings the original sequence set into structured regions of a larger sequence space. These shaped subsets are characterized by a reduced average information content, measured... | https://arxiv.org/abs/2601.15853 | Academic Papers | svg |
f19a7f858f78e419d8ca7180c1c051c384036fe05132c0087e4ca41c3700cd81 | 2026-01-23T00:00:00-05:00 | How to Tamper with a Parliament: Strategic Campaigns in Apportionment Elections | arXiv:2601.15855v1 Announce Type: new Abstract: In parliamentary elections, parties compete for a limited, typically fixed number of seats. Most parliaments are assembled using apportionment methods that distribute the seats based on the parties' vote counts. Common apportionment methods include divisor sequence method... | https://arxiv.org/abs/2601.15855 | Academic Papers | svg |
0284a3d47f1faeb43efc4b7ceaa9a394a5e7c67bc27f51d965b8d0e61d902571 | 2026-01-23T00:00:00-05:00 | Uncertainty-guided Generation of Dark-field Radiographs | arXiv:2601.15859v1 Announce Type: new Abstract: X-ray dark-field radiography provides complementary diagnostic information to conventional attenuation imaging by visualizing microstructural tissue changes through small-angle scattering. However, the limited availability of such data poses challenges for developing robu... | https://arxiv.org/abs/2601.15859 | Academic Papers | svg |
63ee8c51a507bf80c5104ca996e357c9bd479f0aa0921c78246040f942ba179a | 2026-01-23T00:00:00-05:00 | STAR: Semantic Table Representation with Header-Aware Clustering and Adaptive Weighted Fusion | arXiv:2601.15860v1 Announce Type: new Abstract: Table retrieval is the task of retrieving the most relevant tables from large-scale corpora given natural language queries. However, structural and semantic discrepancies between unstructured text and structured tables make embedding alignment particularly challenging. Re... | https://arxiv.org/abs/2601.15860 | Academic Papers | svg |
4288c91af4fa115b9fb88869531bd0ca7b1d4a0cd8a92867ef189d717996e542 | 2026-01-23T00:00:00-05:00 | Finding large sparse induced subgraphs in graphs of small (but not very small) tree-independence number | arXiv:2601.15861v1 Announce Type: new Abstract: The independence number of a tree decomposition is the size of a largest independent set contained in a single bag. The tree-independence number of a graph $G$ is the minimum independence number of a tree decomposition of $G$. As shown recently by Lima et al. [ESA~2024], ... | https://arxiv.org/abs/2601.15861 | Academic Papers | svg |
38c8e8d9ed39fbd2287aff5f651750c898b617196fb6d596a4689eb6f785dbc8 | 2026-01-23T00:00:00-05:00 | Minimum Envy Graphical House Allocation Beyond Identical Valuations | arXiv:2601.15864v1 Announce Type: new Abstract: House allocation is an extremely well-studied problem in the field of fair allocation, where the goal is to assign $n$ houses to $n$ agents while satisfying certain fairness criterion, e.g., envy-freeness. To model social interactions, the Graphical House Allocation frame... | https://arxiv.org/abs/2601.15864 | Academic Papers | svg |
6af99e9580894b8434cc07cd44a70d28550bfb9852260831d4c94986f5b347c2 | 2026-01-23T00:00:00-05:00 | A Lightweight Brain-Inspired Machine Learning Framework for Coronary Angiography: Hybrid Neural Representation and Robust Learning Strategies | arXiv:2601.15865v1 Announce Type: new Abstract: Background: Coronary angiography (CAG) is a cornerstone imaging modality for assessing coronary artery disease and guiding interventional treatment decisions. However, in real-world clinical settings, angiographic images are often characterized by complex lesion morpholog... | https://arxiv.org/abs/2601.15865 | Academic Papers | svg |
cb7de58379cec7328b5040745aa18571733d5652efec22cc81ad7e56fac7e7f0 | 2026-01-23T00:00:00-05:00 | Out-of-Distribution Detection Based on Total Variation Estimation | arXiv:2601.15867v1 Announce Type: new Abstract: This paper introduces a novel approach to securing machine learning model deployments against potential distribution shifts in practical applications, the Total Variation Out-of-Distribution (TV-OOD) detection method. Existing methods have produced satisfactory results, b... | https://arxiv.org/abs/2601.15867 | Academic Papers | svg |
f6d14e96ac51c378cd449b4a82fc8f91fc00f1f5e360bdc6db05c68439f52ae2 | 2026-01-23T00:00:00-05:00 | Artificial Rigidities vs. Biological Noise: A Comparative Analysis of Multisensory Integration in AV-HuBERT and Human Observers | arXiv:2601.15869v1 Announce Type: new Abstract: This study evaluates AV-HuBERT's perceptual bio-fidelity by benchmarking its response to incongruent audiovisual stimuli (McGurk effect) against human observers (N=44). Results reveal a striking quantitative isomorphism: AI and humans exhibited nearly identical auditory d... | https://arxiv.org/abs/2601.15869 | Academic Papers | svg |
95ddab98200c2d306c47ededbe1c09187249922292f0a7946f7e237bbeafbc28 | 2026-01-23T00:00:00-05:00 | Why Inference in Large Models Becomes Decomposable After Training | arXiv:2601.15871v1 Announce Type: new Abstract: Inference in large-scale AI models is typically performed on dense parameter matrices, leading to inference cost and system complexity that scale unsustainably with model size. This limitation does not arise from insufficient model capacity, but from treating post-trainin... | https://arxiv.org/abs/2601.15871 | Academic Papers | svg |
7f06f1c9b4ead26e022e3588b4a18f75e09a5b1918d289fc4104273e8a535eff | 2026-01-23T00:00:00-05:00 | PF-D2M: A Pose-free Diffusion Model for Universal Dance-to-Music Generation | arXiv:2601.15872v1 Announce Type: new Abstract: Dance-to-music generation aims to generate music that is aligned with dance movements. Existing approaches typically rely on body motion features extracted from a single human dancer and limited dance-to-music datasets, which restrict their performance and applicability t... | https://arxiv.org/abs/2601.15872 | Academic Papers | svg |
85ffb0e82ecfed7d069079b81ed5b7b441d8be35b757c0d9eebaf21ed5e2e363 | 2026-01-23T00:00:00-05:00 | SoK: Challenges in Tabular Membership Inference Attacks | arXiv:2601.15874v1 Announce Type: new Abstract: Membership Inference Attacks (MIAs) are currently a dominant approach for evaluating privacy in machine learning applications. Despite their significance in identifying records belonging to the training dataset, several concerns remain unexplored, particularly with regard... | https://arxiv.org/abs/2601.15874 | Academic Papers | svg |
9d0d578a5b2aba17ba707e8ab3cdb521ed581efb8de6aef5af91fad99e7f460f | 2026-01-23T00:00:00-05:00 | EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience | arXiv:2601.15876v1 Announce Type: new Abstract: The development of native computer-use agents (CUA) represents a significant leap in multimodal AI. However, their potential is currently bottlenecked by the constraints of static data scaling. Existing paradigms relying primarily on passive imitation of static datasets s... | https://arxiv.org/abs/2601.15876 | Academic Papers | svg |
ac6b500be86b38bfeb0d7f849a3b5b8ae20688b59b0ebb3ad5f0682edcd85adf | 2026-01-23T00:00:00-05:00 | Evaluating and Achieving Controllable Code Completion in Code LLM | arXiv:2601.15879v1 Announce Type: new Abstract: Code completion has become a central task, gaining significant attention with the rise of large language model (LLM)-based tools in software engineering. Although recent advances have greatly improved LLMs' code completion abilities, evaluation methods have not advanced e... | https://arxiv.org/abs/2601.15879 | Academic Papers | svg |
0586413d6bd0f43c2e81d7df7fd9a20d1b4544e5bddb1766014442636787267f | 2026-01-23T00:00:00-05:00 | PMPBench: A Paired Multi-Modal Pan-Cancer Benchmark for Medical Image Synthesis | arXiv:2601.15884v1 Announce Type: new Abstract: Contrast medium plays a pivotal role in radiological imaging, as it amplifies lesion conspicuity and improves detection for the diagnosis of tumor-related diseases. However, depending on the patient's health condition or the medical resources available, the use of contras... | https://arxiv.org/abs/2601.15884 | Academic Papers | svg |
c6ff692652a1642b8f4e760fc5c90d4ded7200c44b07e4838f30aade56ddd702 | 2026-01-23T00:00:00-05:00 | Understanding the Transfer Limits of Vision Foundation Models | arXiv:2601.15888v1 Announce Type: new Abstract: Foundation models leverage large-scale pretraining to capture extensive knowledge, demonstrating generalization in a wide range of language tasks. By comparison, vision foundation models (VFMs) often exhibit uneven improvements across downstream tasks, despite substantial... | https://arxiv.org/abs/2601.15888 | Academic Papers | svg |
d17098f63e015d6c1cc2e42754afb49474ff48d85382bdc609c0db890692f4b5 | 2026-01-23T00:00:00-05:00 | Existential Positive Transductions of Sparse Graphs | arXiv:2601.15890v1 Announce Type: new Abstract: Monadic stability generalizes many tameness notions from structural graph theory such as planarity, bounded degree, bounded tree-width, and nowhere density. The sparsification conjecture predicts that the (possibly dense) monadically stable graph classes are exactly those... | https://arxiv.org/abs/2601.15890 | Academic Papers | svg |
7c4780c52d9b21e22af51557d1c6d1522824d1efd1b844aef71b21a4ebfba3ab | 2026-01-23T00:00:00-05:00 | RadJEPA: Radiology Encoder for Chest X-Rays via Joint Embedding Predictive Architecture | arXiv:2601.15891v1 Announce Type: new Abstract: Recent advances in medical vision language models guide the learning of visual representations; however, this form of supervision is constrained by the availability of paired image text data, raising the question of whether robust radiology encoders can be learned without... | https://arxiv.org/abs/2601.15891 | Academic Papers | svg |
c3898579ae5f04f3a75e8d4ff6318faa5fe13b96cc992c2918f9e7aee85f1116 | 2026-01-23T00:00:00-05:00 | Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model | arXiv:2601.15892v1 Announce Type: new Abstract: Diffusion-based language models (DLLMs) offer non-sequential, block-wise generation and richer data reuse compared to autoregressive (AR) models, but existing code DLLMs still lag behind strong AR baselines under comparable budgets. We revisit this setting in a controlled... | https://arxiv.org/abs/2601.15892 | Academic Papers | svg |
cd0e292a9842e108f501c3f3f98afdc583ab34fd95797e9fb117a49aebd95ffb | 2026-01-23T00:00:00-05:00 | Iterative Amortized Hierarchical VAE | arXiv:2601.15894v1 Announce Type: new Abstract: In this paper we propose the Iterative Amortized Hierarchical Variational Autoencoder (IA-HVAE), which expands on amortized inference with a hybrid scheme containing an initial amortized guess and iterative refinement with decoder gradients. We achieve this by creating a ... | https://arxiv.org/abs/2601.15894 | Academic Papers | svg |
98722913c179121a6e1eb59f70a6232d4a563f0e19e4590dc3c7a12abdde3972 | 2026-01-23T00:00:00-05:00 | Co-Constructing Alignment: A Participatory Approach to Situate AI Values | arXiv:2601.15895v1 Announce Type: new Abstract: As AI systems become embedded in everyday practice, value misalignment has emerged as a pressing concern. Yet, dominant alignment approaches remain model centric, treating users as passive recipients of prespecified values rather than as epistemic agents who encounter and... | https://arxiv.org/abs/2601.15895 | Academic Papers | svg |
b9f56ce39071f98f7e459eec677cb019ef51b45d0a86e565605841e4a0c57955 | 2026-01-23T00:00:00-05:00 | ThermoSplat: Cross-Modal 3D Gaussian Splatting with Feature Modulation and Geometry Decoupling | arXiv:2601.15897v1 Announce Type: new Abstract: Multi-modal scene reconstruction integrating RGB and thermal infrared data is essential for robust environmental perception across diverse lighting and weather conditions. However, extending 3D Gaussian Splatting (3DGS) to multi-spectral scenarios remains challenging. Cur... | https://arxiv.org/abs/2601.15897 | Academic Papers | svg |
a2dd4d21dfa0eaf0110bb8c6f783b826255d35cd94dd2ddef9a3e58c9624b30e | 2026-01-23T00:00:00-05:00 | Blind Identification of Channel Codes: A Subspace-Coding Approach | arXiv:2601.15903v1 Announce Type: new Abstract: The problem of blind identification of channel codes at a receiver involves identifying a code chosen by a transmitter from a known code-family, by observing the transmitted codewords through the channel. Most existing approaches for code-identification are contingent upo... | https://arxiv.org/abs/2601.15903 | Academic Papers | svg |
a9e9f7947d394736ef4aedeaf7406c74facd9cf6cc2d643a35815c0fdbdaeb9d | 2026-01-23T00:00:00-05:00 | Dynamic Server Allocation Under Stochastic Switchover on Time-Varying Links | arXiv:2601.15904v1 Announce Type: new Abstract: Dynamic resource allocation to parallel queues is a cornerstone of network scheduling, yet classical solutions often fail when accounting for the overhead of switching delays to queues with superior link conditions. In particular, system performance is further degraded wh... | https://arxiv.org/abs/2601.15904 | Academic Papers | svg |
8babf70b43b9158a54791d752a8df8acea452db5a7c12d81e66bc73f16eb0f4b | 2026-01-23T00:00:00-05:00 | Pregroup representable expansions of residuated lattices | arXiv:2601.15905v1 Announce Type: new Abstract: Group representable relation algebras play an important role in the study of representable relation algebras. The class of distributive involutive FL-algebras (DInFL-algebras) generalises relation algebras, as well as Sugihara monoids and MV-algebras. We construct DInFL-a... | https://arxiv.org/abs/2601.15905 | Academic Papers | svg |
f9eebbb8c69104e4562cea02d2bbc5f2645478ff51d9911985b6ecefdc4026bf | 2026-01-23T00:00:00-05:00 | Opening the Black Box: Preliminary Insights into Affective Modeling in Multimodal Foundation Models | arXiv:2601.15906v1 Announce Type: new Abstract: Understanding where and how emotions are represented in large-scale foundation models remains an open problem, particularly in multimodal affective settings. Despite the strong empirical performance of recent affective models, the internal architectural mechanisms that su... | https://arxiv.org/abs/2601.15906 | Academic Papers | svg |
e9f5b37d2bb506ce78d8abe01013ceb168c50138a6a4eac7d1cfd4abed11094e | 2026-01-23T00:00:00-05:00 | Transfer Learning from ImageNet for MEG-Based Decoding of Imagined Speech | arXiv:2601.15909v1 Announce Type: new Abstract: Non-invasive decoding of imagined speech remains challenging due to weak, distributed signals and limited labeled data. Our paper introduces an image-based approach that transforms magnetoencephalography (MEG) signals into time-frequency representations compatible with pr... | https://arxiv.org/abs/2601.15909 | Academic Papers | svg |
69e875c70e0030c8dd68ee0ba29d52219c11231f00a6dac5b4cba93426d73ad3 | 2026-01-23T00:00:00-05:00 | A fully diagonalized spectral method on the unit ball | arXiv:2601.15911v1 Announce Type: new Abstract: Our main objective in this work is to show how Sobolev orthogonal polynomials emerge as a useful tool within the framework of spectral methods for boundary-value problems. The solution of a boundary-value problem for a stationary Schr\"odinger equation on the unit ball ca... | https://arxiv.org/abs/2601.15911 | Academic Papers | svg |
d3a11046d6fe5593e3b18825e9f94573b4f1aa352e75d6e9d74f722bce17ed39 | 2026-01-23T00:00:00-05:00 | TeNet: Text-to-Network for Compact Policy Synthesis | arXiv:2601.15912v1 Announce Type: new Abstract: Robots that follow natural-language instructions often either plan at a high level using hand-designed interfaces or rely on large end-to-end models that are difficult to deploy for real-time control. We propose TeNet (Text-to-Network), a framework for instantiating compa... | https://arxiv.org/abs/2601.15912 | Academic Papers | svg |
6d6a775a7de83d85ab920edfc809bcfa232f54dfe565346368b0149083ba4321 | 2026-01-23T00:00:00-05:00 | The Latency Wall: Benchmarking Off-the-Shelf Emotion Recognition for Real-Time Virtual Avatars | arXiv:2601.15914v1 Announce Type: new Abstract: In the realm of Virtual Reality (VR) and Human-Computer Interaction (HCI), real-time emotion recognition shows promise for supporting individuals with Autism Spectrum Disorder (ASD) in improving social skills. This task requires a strict latency-accuracy trade-off, with m... | https://arxiv.org/abs/2601.15914 | Academic Papers | svg |
83ab6e27d6f71f56bf0088b0774380ac0e553fab85cdfc11ebdafeb82ae6a241 | 2026-01-23T00:00:00-05:00 | A Multi-View Pipeline and Benchmark Dataset for 3D Hand Pose Estimation in Surgery | arXiv:2601.15918v1 Announce Type: new Abstract: Purpose: Accurate 3D hand pose estimation supports surgical applications such as skill assessment, robot-assisted interventions, and geometry-aware workflow analysis. However, surgical environments pose severe challenges, including intense and localized lighting, frequent... | https://arxiv.org/abs/2601.15918 | Academic Papers | svg |
3a787210556a53f2de4ffd7a99fe1757d8dd02ddbf2bad821338a0eb1e1025d4 | 2026-01-23T00:00:00-05:00 | Class Confidence Aware Reweighting for Long Tailed Learning | arXiv:2601.15924v1 Announce Type: new Abstract: Deep neural network models degrade significantly in the long-tailed data distribution, with the overall training data dominated by a small set of classes in the head, and the tail classes obtaining less training examples. Addressing the imbalance in the classes, attention... | https://arxiv.org/abs/2601.15924 | Academic Papers | svg |
4f37ecb677665fb3f38e3f6df7bc186eca82ca56850e505d076a61608a538b54 | 2026-01-23T00:00:00-05:00 | A Remark on Downlink Massive Random Access | arXiv:2601.15928v1 Announce Type: new Abstract: In downlink massive random access (DMRA), a base station transmits messages to a typically small subset of active users, selected randomly from a massive number of total users. Explicitly encoding the identities of active users would incur a significant overhead scaling l... | https://arxiv.org/abs/2601.15928 | Academic Papers | svg |
fe7beb6a32a77cd2b8f1340c318edf8576c4dffc27416662e866324ccf379663 | 2026-01-23T00:00:00-05:00 | NeuroMamba: Multi-Perspective Feature Interaction with Visual Mamba for Neuron Segmentation | arXiv:2601.15929v1 Announce Type: new Abstract: Neuron segmentation is the cornerstone of reconstructing comprehensive neuronal connectomes, which is essential for deciphering the functional organization of the brain. The irregular morphology and densely intertwined structures of neurons make this task particularly cha... | https://arxiv.org/abs/2601.15929 | Academic Papers | svg |
b2f31563b0f764114560d6d318c56ed8b4748b75418ecab3e824f6f4347cfd04 | 2026-01-23T00:00:00-05:00 | MMGRid: Navigating Temporal-aware and Cross-domain Generative Recommendation via Model Merging | arXiv:2601.15930v1 Announce Type: new Abstract: Model merging (MM) offers an efficient mechanism for integrating multiple specialized models without access to original training data or costly retraining. While MM has demonstrated success in domains like computer vision, its role in recommender systems (RSs) remains lar... | https://arxiv.org/abs/2601.15930 | Academic Papers | svg |
883c5ddbd7515e7410a6861361f7332d7b12c1e0431cdc9d08860b5936e64f8e | 2026-01-23T00:00:00-05:00 | ICON: Invariant Counterfactual Optimization with Neuro-Symbolic Priors for Text-Based Person Search | arXiv:2601.15931v1 Announce Type: new Abstract: Text-Based Person Search (TBPS) holds unique value in real-world surveillance bridging visual perception and language understanding, yet current paradigms utilizing pre-training models often fail to transfer effectively to complex open-world scenarios. The reliance on "Pa... | https://arxiv.org/abs/2601.15931 | Academic Papers | svg |
e979b2fd13f4d195496b5532ca9c7efe95a98b5e4ec30ae067f41aed0438729e | 2026-01-23T00:00:00-05:00 | Layered automata: A canonical model for automata over infinite words | arXiv:2601.15940v1 Announce Type: new Abstract: We introduce layered automata, a subclass of alternating parity automata that generalises deterministic automata. Assuming a consistency property, these automata are history deterministic and 0-1 probabilistic. We show that every omega-regular language is recognised by a ... | https://arxiv.org/abs/2601.15940 | Academic Papers | svg |
3459878f5876310897902e403fae8df594aa2bd7ecf92f637c4c4b6618b3667c | 2026-01-23T00:00:00-05:00 | Accurate Calibration and Robust LiDAR-Inertial Odometry for Spinning Actuated LiDAR Systems | arXiv:2601.15946v1 Announce Type: new Abstract: Accurate calibration and robust localization are fundamental for downstream tasks in spinning actuated LiDAR applications. Existing methods, however, require parameterizing extrinsic parameters based on different mounting configurations, limiting their generalizability. A... | https://arxiv.org/abs/2601.15946 | Academic Papers | svg |
bafe79ecc85d613fe3e12c742e4fa19c8dd24bf2caac525e0abf8f5d546fd7a9 | 2026-01-23T00:00:00-05:00 | Natural Language-Driven Global Mapping of Martian Landforms | arXiv:2601.15949v1 Announce Type: new Abstract: Planetary surfaces are typically analyzed using high-level semantic concepts in natural language, yet vast orbital image archives remain organized at the pixel level. This mismatch limits scalable, open-ended exploration of planetary surfaces. Here we present MarScope, a ... | https://arxiv.org/abs/2601.15949 | Academic Papers | svg |
e86f00c1ba67e72de883bbe1af05cacd90a0aa34acfe92d1514fc9d42f6de6da | 2026-01-23T00:00:00-05:00 | EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis | arXiv:2601.15951v1 Announce Type: new Abstract: Novel view synthesis (NVS) of static and dynamic urban scenes is essential for autonomous driving simulation, yet existing methods often struggle to balance reconstruction time with quality. While state-of-the-art neural radiance fields and 3D Gaussian Splatting approache... | https://arxiv.org/abs/2601.15951 | Academic Papers | svg |
3292f83c68481c9fdce9dbeaa32c5d581640c651529d96d47a1bc77cfd561890 | 2026-01-23T00:00:00-05:00 | Decoupling Return-to-Go for Efficient Decision Transformer | arXiv:2601.15953v1 Announce Type: new Abstract: The Decision Transformer (DT) has established a powerful sequence modeling approach to offline reinforcement learning. It conditions its action predictions on Return-to-Go (RTG), using it both to distinguish trajectory quality during training and to guide action generatio... | https://arxiv.org/abs/2601.15953 | Academic Papers | svg |
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