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d0998d2a6f4e23d75924132e74222f05d6ab41e47e848129f3e96a12d46cb025 | 2026-01-21T00:00:00-05:00 | PREFAB: PREFerence-based Affective Modeling for Low-Budget Self-Annotation | arXiv:2601.13904v1 Announce Type: new Abstract: Self-annotation is the gold standard for collecting affective state labels in affective computing. Existing methods typically rely on full annotation, requiring users to continuously label affective states across entire sessions. While this process yields fine-grained dat... | https://arxiv.org/abs/2601.13904 | Academic Papers | svg |
e79b66521ee2aaecd60b7fd450f8d829ed1d89bdc7fc626ad29317c4a6c4f7f7 | 2026-01-21T00:00:00-05:00 | Decentralized Infrastructure for Digital Notarizing, Signing and Sharing Files using Blockchain | arXiv:2601.13907v1 Announce Type: new Abstract: Traditional paper-based document management has long posed challenges related to security, authenticity, and efficiency. Despite advances in digitalization, official documents remain vulnerable to forgery, loss, and unauthorized access. This thesis proposes a decentralize... | https://arxiv.org/abs/2601.13907 | Academic Papers | svg |
0c9e5f1e98c989c6a655c60604bb90452a98eda1e4e04537cb1ac1d57f6e5186 | 2026-01-21T00:00:00-05:00 | Improving the local solution of the DG predictor of the ADER-DG method for solving systems of ordinary differential equations and its applicability to systems of differential-algebraic equations | arXiv:2601.13908v1 Announce Type: new Abstract: Improved local numerical solution for the ADER-DG numerical method with a local DG predictor for solving the initial value problem for a first-order ODE system is proposed. The improved local numerical solution demonstrates convergence orders of one higher than the conver... | https://arxiv.org/abs/2601.13908 | Academic Papers | svg |
57a898fdbb6e8c4b32c7535c33fd6bdeffc4c45f5e1e69de7ed7c56b21a088fd | 2026-01-21T00:00:00-05:00 | On the Role of Rotation Equivariance in Monocular 3D Human Pose Estimation | arXiv:2601.13913v1 Announce Type: new Abstract: Estimating 3D from 2D is one of the central tasks in computer vision. In this work, we consider the monocular setting, i.e. single-view input, for 3D human pose estimation (HPE). Here, the task is to predict a 3D point set of human skeletal joints from a single 2D input i... | https://arxiv.org/abs/2601.13913 | Academic Papers | svg |
8064c6be9a25c424d33946901463a65efda6fdef1465d8dd01d18179bc8b4153 | 2026-01-21T00:00:00-05:00 | AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization | arXiv:2601.13918v1 Announce Type: new Abstract: Large Language Models have demonstrated profound utility in the medical domain. However, their application to autonomous Electronic Health Records~(EHRs) navigation remains constrained by a reliance on curated inputs and simplified retrieval tasks. To bridge the gap betwe... | https://arxiv.org/abs/2601.13918 | Academic Papers | svg |
2de761a41f675e4c4f66d7c04ada9349099440ef2e3b09c9436d5dbb8aab39a7 | 2026-01-21T00:00:00-05:00 | HyperWalker: Dynamic Hypergraph-Based Deep Diagnosis for Multi-Hop Clinical Modeling across EHR and X-Ray in Medical VLMs | arXiv:2601.13919v1 Announce Type: new Abstract: Automated clinical diagnosis remains a core challenge in medical AI, which usually requires models to integrate multi-modal data and reason across complex, case-specific contexts. Although recent methods have advanced medical report generation (MRG) and visual question an... | https://arxiv.org/abs/2601.13919 | Academic Papers | svg |
a58d7bec09e84027881150e12cfb4161dbacc1e67c8b0964dac5de525f8010a3 | 2026-01-21T00:00:00-05:00 | Asymmetric regularization mechanism for GAN training with Variational Inequalities | arXiv:2601.13920v1 Announce Type: new Abstract: We formulate the training of generative adversarial networks (GANs) as a Nash equilibrium seeking problem. To stabilize the training process and find a Nash equilibrium, we propose an asymmetric regularization mechanism based on the classic Tikhonov step and on a novel ze... | https://arxiv.org/abs/2601.13920 | Academic Papers | svg |
23419ceea0694064421282cd71b8520e61fa89fffd62e9bee942859af6605415 | 2026-01-21T00:00:00-05:00 | Automatic Prompt Optimization for Dataset-Level Feature Discovery | arXiv:2601.13922v1 Announce Type: new Abstract: Feature extraction from unstructured text is a critical step in many downstream classification pipelines, yet current approaches largely rely on hand-crafted prompts or fixed feature schemas. We formulate feature discovery as a dataset-level prompt optimization problem: g... | https://arxiv.org/abs/2601.13922 | Academic Papers | svg |
535a9890edb46000e55099f9c651854786eb03fc7a46b3a285ad633b382f9947 | 2026-01-21T00:00:00-05:00 | Proactive Coded Caching Scheme for D2D Networks | arXiv:2601.13929v1 Announce Type: new Abstract: Coded caching and device-to-device (D2D) communication are two effective techniques for alleviating network traffic. Secure transmission and file privacy have also become critical concerns in these domains. However, prevailing coded caching schemes typically assume that a... | https://arxiv.org/abs/2601.13929 | Academic Papers | svg |
835a35815cc3aabf738cce3111d88f8dad7a330e08c556d1d96a58f436cc72af | 2026-01-21T00:00:00-05:00 | Towards Effective Negation Modeling in Joint Audio-Text Models for Music | arXiv:2601.13931v1 Announce Type: new Abstract: Joint audio-text models are widely used for music retrieval, yet they struggle with semantic phenomena such as negation. Negation is fundamental for distinguishing the absence (or presence) of musical elements (e.g., "with vocals" vs. "without vocals"), but current system... | https://arxiv.org/abs/2601.13931 | Academic Papers | svg |
f7ded84b9a9198578fa95e137a680696cdf778daf824ae53d0c9b0dde5e0c47d | 2026-01-21T00:00:00-05:00 | VulnResolver: A Hybrid Agent Framework for LLM-Based Automated Vulnerability Issue Resolution | arXiv:2601.13933v1 Announce Type: new Abstract: As software systems grow in complexity, security vulnerabilities have become increasingly prevalent, posing serious risks and economic costs. Although automated detection tools such as fuzzers have advanced considerably, effective resolution still often depends on human e... | https://arxiv.org/abs/2601.13933 | Academic Papers | svg |
726f98455998e6673405e2f375ee54ff6402aa84aaf04ad480962ac818d0f97d | 2026-01-21T00:00:00-05:00 | TrackletGPT: A Language-like GPT Framework for White Matter Tract Segmentation | arXiv:2601.13935v1 Announce Type: new Abstract: White Matter Tract Segmentation is imperative for studying brain structural connectivity, neurological disorders and neurosurgery. This task remains complex, as tracts differ among themselves, across subjects and conditions, yet have similar 3D structure across hemisphere... | https://arxiv.org/abs/2601.13935 | Academic Papers | svg |
88870d7f08e9ace1e8b76c79ac7fc6fde99fcd55ac53ec426f0a23f79954b0b2 | 2026-01-21T00:00:00-05:00 | Impact Matters! An Audit Method to Evaluate AI Projects and their Impact for Sustainability and Public Interest | arXiv:2601.13936v1 Announce Type: new Abstract: The overall rapid increase of artificial intelligence (AI) use is linked to various initiatives that propose AI 'for good'. However, there is a lack of transparency in the goals of such projects, as well as a missing evaluation of their actual impacts on society and the p... | https://arxiv.org/abs/2601.13936 | Academic Papers | svg |
87af0868124cb7408eb108eab5377160333440e8828cd28c870c80c9fdbe77e4 | 2026-01-21T00:00:00-05:00 | IF-GEO: Conflict-Aware Instruction Fusion for Multi-Query Generative Engine Optimization | arXiv:2601.13938v1 Announce Type: new Abstract: As Generative Engines revolutionize information retrieval by synthesizing direct answers from retrieved sources, ensuring source visibility becomes a significant challenge. Improving it through targeted content revisions is a practical strategy termed Generative Engine Op... | https://arxiv.org/abs/2601.13938 | Academic Papers | svg |
9572f04ed7411e4fad77f9a6b8b6c0194e673a91b1f9155385805b7cee6f04ee | 2026-01-21T00:00:00-05:00 | Glance-or-Gaze: Incentivizing LMMs to Adaptively Focus Search via Reinforcement Learning | arXiv:2601.13942v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have achieved remarkable success in visual understanding, yet they struggle with knowledge-intensive queries involving long-tail entities or evolving information due to static parametric knowledge. Recent search-augmented approaches attempt ... | https://arxiv.org/abs/2601.13942 | Academic Papers | svg |
8b9e99ac73b476009e61dff08effef0ce05f2761e31f855b79771dc5ec57441a | 2026-01-21T00:00:00-05:00 | RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository | arXiv:2601.13943v1 Announce Type: new Abstract: Large language models and agents have achieved remarkable progress in code generation. However, existing benchmarks focus on isolated function/class-level generation (e.g., ClassEval) or modifications to existing codebases (e.g., SWE-Bench), neglecting complete microservi... | https://arxiv.org/abs/2601.13943 | Academic Papers | svg |
d36ad546e1fbce4b348957354222d9a75fdff50d18dd40ded880b0f48636cd97 | 2026-01-21T00:00:00-05:00 | Efficient Coordination with the System-Level Shared State: An Embodied-AI Native Modular Framework | arXiv:2601.13945v1 Announce Type: new Abstract: As Embodied AI systems move from research prototypes to real world deployments, they tend to evolve rapidly while remaining reliable under workload changes and partial failures. In practice, many deployments are only partially decoupled: middleware moves messages, but sha... | https://arxiv.org/abs/2601.13945 | Academic Papers | svg |
28c2c987e31d7d0639ed02f4438329002fddbf82988b58b1520120048af4ae67 | 2026-01-21T00:00:00-05:00 | VTONGuard: Automatic Detection and Authentication of AI-Generated Virtual Try-On Content | arXiv:2601.13951v1 Announce Type: new Abstract: With the rapid advancement of generative AI, virtual try-on (VTON) systems are becoming increasingly common in e-commerce and digital entertainment. However, the growing realism of AI-generated try-on content raises pressing concerns about authenticity and responsible use... | https://arxiv.org/abs/2601.13951 | Academic Papers | svg |
fc367803b85749910cc49eb226d7c3834f362cce5bc783b41e62a97c0953401a | 2026-01-21T00:00:00-05:00 | Differentiable Logic Synthesis: Spectral Coefficient Selection via Sinkhorn-Constrained Composition | arXiv:2601.13953v1 Announce Type: new Abstract: Learning precise Boolean logic via gradient descent remains challenging: neural networks typically converge to "fuzzy" approximations that degrade under quantization. We introduce Hierarchical Spectral Composition, a differentiable architecture that selects spectral coeff... | https://arxiv.org/abs/2601.13953 | Academic Papers | svg |
f7749f17d336ad541b8f435086b8aeb679b915e606031daa26b65efe8c06c1f7 | 2026-01-21T00:00:00-05:00 | DExTeR: Weakly Semi-Supervised Object Detection with Class and Instance Experts for Medical Imaging | arXiv:2601.13954v1 Announce Type: new Abstract: Detecting anatomical landmarks in medical imaging is essential for diagnosis and intervention guidance. However, object detection models rely on costly bounding box annotations, limiting scalability. Weakly Semi-Supervised Object Detection (WSSOD) with point annotations p... | https://arxiv.org/abs/2601.13954 | Academic Papers | svg |
2234ddab9c70dbb6169fdf6d87878d5cbeedb3cce7312c686bf91d5bf3d96199 | 2026-01-21T00:00:00-05:00 | Where to Place a Heavy Payload on a Multirotor UAV for Best Control Performance | arXiv:2601.13958v1 Announce Type: new Abstract: This paper studies the impact of rigidly attached heavy payload placement - where the payload mass significantly influences the UAV's dynamics - on the stability and control performance of a multirotor unmanned aerial vehicle (UAV). In particular, we focus on how the posi... | https://arxiv.org/abs/2601.13958 | Academic Papers | svg |
d89c8e9a51bbe464256adeea2a4476c8b9bdddbb56a49436b69f180d1135f55e | 2026-01-21T00:00:00-05:00 | RL-BioAug: Label-Efficient Reinforcement Learning for Self-Supervised EEG Representation Learning | arXiv:2601.13964v1 Announce Type: new Abstract: The quality of data augmentation serves as a critical determinant for the performance of contrastive learning in EEG tasks. Although this paradigm is promising for utilizing unlabeled data, static or random augmentation strategies often fail to preserve intrinsic informat... | https://arxiv.org/abs/2601.13964 | Academic Papers | svg |
85ac8cc26ac670bb88d5691a89c6af6bb7b75849f4f2cdae3561c327cb09de13 | 2026-01-21T00:00:00-05:00 | Autonomous Knowledge Graph Exploration with Adaptive Breadth-Depth Retrieval | arXiv:2601.13969v1 Announce Type: new Abstract: Retrieving evidence for language model queries from knowledge graphs requires balancing broad search across the graph with multi-hop traversal to follow relational links. Similarity-based retrievers provide coverage but remain shallow, whereas traversal-based methods rely... | https://arxiv.org/abs/2601.13969 | Academic Papers | svg |
ea38ae3a2846e0e47b61e70d538f861a4271c1730a228a447465ee94d8675d4d | 2026-01-21T00:00:00-05:00 | The Transparency Paradox in Explainable AI: A Theory of Autonomy Depletion Through Cognitive Load | arXiv:2601.13973v1 Announce Type: new Abstract: Objective: This paper develops a theoretical framework explaining when and why AI explanations enhance versus impair human decision-making. Background: Transparency is advocated as universally beneficial for human-AI interaction, yet identical AI explanations improve deci... | https://arxiv.org/abs/2601.13973 | Academic Papers | svg |
da1bfc60e6df30a714799ea0ca8897876c81314c93079b95a69c58b48391ebc0 | 2026-01-21T00:00:00-05:00 | STEC: A Reference-Free Spatio-Temporal Entropy Coverage Metric for Evaluating Sampled Video Frames | arXiv:2601.13974v1 Announce Type: new Abstract: Frame sampling is a fundamental component in video understanding and video--language model pipelines, yet evaluating the quality of sampled frames remains challenging. Existing evaluation metrics primarily focus on perceptual quality or reconstruction fidelity, and are no... | https://arxiv.org/abs/2601.13974 | Academic Papers | svg |
57d1eb0f7a51ab7c6d6dfcc5bcf5695cb7bd37933c5234ccf1a81d2bdb197068 | 2026-01-21T00:00:00-05:00 | Harmonizing the Deep: A Unified Information Pipeline for Robust Marine Biodiversity Assessment Across Heterogeneous Domains | arXiv:2601.13975v1 Announce Type: new Abstract: Marine biodiversity monitoring requires scalability and reliability across complex underwater environments to support conservation and invasive-species management. Yet existing detection solutions often exhibit a pronounced deployment gap, with performance degrading sharp... | https://arxiv.org/abs/2601.13975 | Academic Papers | svg |
76a4a650dff250a1d9f6197cb85479137bab175ed23734d0ba71383f366c5ee1 | 2026-01-21T00:00:00-05:00 | FantasyVLN: Unified Multimodal Chain-of-Thought Reasoning for Vision-Language Navigation | arXiv:2601.13976v1 Announce Type: new Abstract: Achieving human-level performance in Vision-and-Language Navigation (VLN) requires an embodied agent to jointly understand multimodal instructions and visual-spatial context while reasoning over long action sequences. Recent works, such as NavCoT and NavGPT-2, demonstrate... | https://arxiv.org/abs/2601.13976 | Academic Papers | svg |
9843e901592f8cc8779f5d7314e93b5f32194bfce02cb70d4a5082a8b034ffdb | 2026-01-21T00:00:00-05:00 | Active Cross-Modal Visuo-Tactile Perception of Deformable Linear Objects | arXiv:2601.13979v1 Announce Type: new Abstract: This paper presents a novel cross-modal visuo-tactile perception framework for the 3D shape reconstruction of deformable linear objects (DLOs), with a specific focus on cables subject to severe visual occlusions. Unlike existing methods relying predominantly on vision, wh... | https://arxiv.org/abs/2601.13979 | Academic Papers | svg |
36fef0698cfece4b4d7a4b062c735bf3e88a1c0aeac655dfa6930c8f26d22e48 | 2026-01-21T00:00:00-05:00 | VirtualCrime: Evaluating Criminal Potential of Large Language Models via Sandbox Simulation | arXiv:2601.13981v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong capabilities in multi-step decision-making, planning and actions, and are increasingly integrated into various real-world applications. It is concerning whether their strong problem-solving abilities may be misused for crimes... | https://arxiv.org/abs/2601.13981 | Academic Papers | svg |
9ecc7c211c91c9ec6c4285526871f8668e1cf0bbef91787c1150fd2f5f9949bd | 2026-01-21T00:00:00-05:00 | Equivariant Learning for Unsupervised Image Dehazing | arXiv:2601.13986v1 Announce Type: new Abstract: Image Dehazing (ID) aims to produce a clear image from an observation contaminated by haze. Current ID methods typically rely on carefully crafted priors or extensive haze-free ground truth, both of which are expensive or impractical to acquire, particularly in the contex... | https://arxiv.org/abs/2601.13986 | Academic Papers | svg |
9fbdb6fcce3ed676602e71632d15c94033a7f3bb2012e59fb65d7207f1af64a3 | 2026-01-21T00:00:00-05:00 | A universal linearized subspace refinement framework for neural networks | arXiv:2601.13989v1 Announce Type: new Abstract: Neural networks are predominantly trained using gradient-based methods, yet in many applications their final predictions remain far from the accuracy attainable within the model's expressive capacity. We introduce Linearized Subspace Refinement (LSR), a general and archit... | https://arxiv.org/abs/2601.13989 | Academic Papers | svg |
ed7485ee05829940a23aa3417dbcd66de22312e8b5e5339335bda32123728e85 | 2026-01-21T00:00:00-05:00 | Generating Functions Meet Occupation Measures: Invariant Synthesis for Probabilistic Loops (Extended Version) | arXiv:2601.13991v1 Announce Type: new Abstract: A fundamental computational task in probabilistic programming is to infer a program's output (posterior) distribution from a given initial (prior) distribution. This problem is challenging, especially for expressive languages that feature loops or unbounded recursion. Whi... | https://arxiv.org/abs/2601.13991 | Academic Papers | svg |
4db5e26be3dc4c2bb65324eaf0f259eb13e7ce886934690b60701c654248aee2 | 2026-01-21T00:00:00-05:00 | "The Whole Is Greater Than the Sum of Its Parts": A Compatibility-Aware Multi-Teacher CoT Distillation Framework | arXiv:2601.13992v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning empowers Large Language Models (LLMs) with remarkable capabilities but typically requires prohibitive parameter scales. CoT distillation has emerged as a promising paradigm to transfer reasoning prowess into compact Student Models (SLMs), ... | https://arxiv.org/abs/2601.13992 | Academic Papers | svg |
0c73e90365cf642d4da67edd7ef64f7ca858dee5ce3236548e684599c6e1aec8 | 2026-01-21T00:00:00-05:00 | Capacity and Energy Trade-Offs in FR3 6G Networks Using Real Deployment Data | arXiv:2601.13993v1 Announce Type: new Abstract: This article presents a data-driven system-level analysis of multi-layer 6G networks operating in the upper mid-band (FR3: 7-24 GHz). Unlike most prior studies based on 3rd Generation Partnership Project (3GPP) templates, we leverage real-world deployment and traffic data... | https://arxiv.org/abs/2601.13993 | Academic Papers | svg |
1cebe6f3e7b6da36fec22bd4b15c6ff17ddba82cd8bb6070dfd78de0099bf344 | 2026-01-21T00:00:00-05:00 | torch-sla: Differentiable Sparse Linear Algebra with Adjoint Solvers and Sparse Tensor Parallelism for PyTorch | arXiv:2601.13994v1 Announce Type: new Abstract: Industrial scientific computing predominantly uses sparse matrices to represent unstructured data -- finite element meshes, graphs, point clouds. We present \torchsla{}, an open-source PyTorch library that enables GPU-accelerated, scalable, and differentiable sparse linea... | https://arxiv.org/abs/2601.13994 | Academic Papers | svg |
4bfefbb5fda1fb9d1eda1b42ccf5d37ec6946a33604eb4be9155f92c94460102 | 2026-01-21T00:00:00-05:00 | From Tags to Trees: Structuring Fine-Grained Knowledge for Controllable Data Selection in LLM Instruction Tuning | arXiv:2601.13995v1 Announce Type: new Abstract: Effective and controllable data selection is critical for LLM instruction tuning, especially with massive open-source datasets. Existing approaches primarily rely on instance-level quality scores, or diversity metrics based on embedding clusters or semantic tags. However,... | https://arxiv.org/abs/2601.13995 | Academic Papers | svg |
b3ff2a0411bcf8b6e80f65348f1f22c8aed76358084ccd54219f7d48c209a762 | 2026-01-21T00:00:00-05:00 | Software Testing in the Quantum World | arXiv:2601.13996v1 Announce Type: new Abstract: Quantum computing offers significant speedups for simulating physical, chemical, and biological systems, and for optimization and machine learning. As quantum software grows in complexity, the classical simulation of quantum computers, which has long been essential for qu... | https://arxiv.org/abs/2601.13996 | Academic Papers | svg |
fe8a83de3775dd7a4ea14db09e95396c6a59192dbc7db151d78390b1033de953 | 2026-01-21T00:00:00-05:00 | Group-Invariant Unsupervised Skill Discovery: Symmetry-aware Skill Representations for Generalizable Behavior | arXiv:2601.14000v1 Announce Type: new Abstract: Unsupervised skill discovery aims to acquire behavior primitives that improve exploration and accelerate downstream task learning. However, existing approaches often ignore the geometric symmetries of physical environments, leading to redundant behaviors and sample ineffi... | https://arxiv.org/abs/2601.14000 | Academic Papers | svg |
495aeec312214c0c1fc81d4af2dcc5ba2797c30bb82af2cb9b7d98ae28b23c2c | 2026-01-21T00:00:00-05:00 | Auditory Brain Passage Retrieval: Cross-Sensory EEG Training for Neural Information Retrieval | arXiv:2601.14001v1 Announce Type: new Abstract: Query formulation from internal information needs remains fundamentally challenging across all Information Retrieval paradigms due to cognitive complexity and physical impairments. Brain Passage Retrieval (BPR) addresses this by directly mapping EEG signals to passage rep... | https://arxiv.org/abs/2601.14001 | Academic Papers | svg |
3724234dcbac4b014ee2644909707488fb3bfc7ba73bfc421cddd77a3d332199 | 2026-01-21T00:00:00-05:00 | Consensus Stability of Community Notes on X | arXiv:2601.14002v1 Announce Type: new Abstract: Community-based fact-checking systems, such as Community Notes on X (formerly Twitter), aim to mitigate online misinformation by surfacing annotations judged helpful by contributors with diverse viewpoints. While prior work has shown that the platform's bridging-based alg... | https://arxiv.org/abs/2601.14002 | Academic Papers | svg |
d36197c2e5fa2daabf7234ea11b404b9615ea056d39c0510676e3f5c8c341dfe | 2026-01-21T00:00:00-05:00 | Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models | arXiv:2601.14004v1 Announce Type: new Abstract: Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However, existing reviews primarily treat MI as an observational science, summarizing analytical insights while lacking a systematic ... | https://arxiv.org/abs/2601.14004 | Academic Papers | svg |
f34d228f0ecec0e76e1190e9d4be98ca7fb34725335a91cf8cfe8de8cbc20bf0 | 2026-01-21T00:00:00-05:00 | BACH-V: Bridging Abstract and Concrete Human-Values in Large Language Models | arXiv:2601.14007v1 Announce Type: new Abstract: Do large language models (LLMs) genuinely understand abstract concepts, or merely manipulate them as statistical patterns? We introduce an abstraction-grounding framework that decomposes conceptual understanding into three capacities: interpretation of abstract concepts (... | https://arxiv.org/abs/2601.14007 | Academic Papers | svg |
d7230a154967f7045af662456ed86330f9e74bc9bb143f909ca6b58e4a6561a6 | 2026-01-21T00:00:00-05:00 | MANATEE: A DevOps Platform for xApp Lifecycle Management and Testing in Open RAN | arXiv:2601.14009v1 Announce Type: new Abstract: The shift to disaggregated 5G architectures introduces unprecedented flexibility but also significant complexity in Beyond 5G Radio Access Networks (RANs). Open RAN enables programmability through xApps, yet deploying and validating these applications is critical given th... | https://arxiv.org/abs/2601.14009 | Academic Papers | svg |
0af984cdd81f328e4f2af76f8d949d59c8b758839d401214ad27e9c46d86ef4d | 2026-01-21T00:00:00-05:00 | Numerical solution of Smoluchowski coagulation equation combined with Ostwald ripening | arXiv:2601.14011v1 Announce Type: new Abstract: The processes of simultaneous coagulation and Ostwald ripening of particles in the concluding stage of phase transformation are considered. We solve the integro-differential system of Smoluchowski-type kinetic and mass balance equations using a computationally efficient n... | https://arxiv.org/abs/2601.14011 | Academic Papers | svg |
430471469e4370897d84c5b27f54e42ac6e9f8eb446cc38189bd6e2ad2ed7dfe | 2026-01-21T00:00:00-05:00 | BallotRank: A Condorcet Completion Method for Graphs | arXiv:2601.14015v1 Announce Type: new Abstract: We introduce BallotRank, a ranked preference aggregation method derived from a modified PageRank algorithm. It is a Condorcet-consistent method without damping, and empirical examination of nearly 2,000 ranked choice elections and over 20,000 internet polls confirms that ... | https://arxiv.org/abs/2601.14015 | Academic Papers | svg |
4e7f1c19b011ce8a24573e61652296ed27a25e66483e2f15997b8ba7123c6298 | 2026-01-21T00:00:00-05:00 | A Security Framework for Chemical Functions | arXiv:2601.14019v1 Announce Type: new Abstract: In this paper, we introduce chemical functions, a unified framework that models chemical systems as noisy challenge--response primitives, and formalize the associated chemical function infrastructure. Building on the theory of physical functions, we rigorously define robu... | https://arxiv.org/abs/2601.14019 | Academic Papers | svg |
b7844a76bbcec49f6e155003abe7c7e128b0ccae21c6e4bdc9db411f138d8e14 | 2026-01-21T00:00:00-05:00 | OAMAC: Origin-Aware Mandatory Access Control for Practical Post-Compromise Attack Surface Reduction | arXiv:2601.14021v1 Announce Type: new Abstract: Modern operating systems provide powerful mandatory access control mechanisms, yet they largely reason about who executes code rather than how execution originates. As a result, processes launched remotely, locally, or by background services are often treated equivalently... | https://arxiv.org/abs/2601.14021 | Academic Papers | svg |
2e2029f81d420ed9ea6cb6f5eaab432e9c2c7f883358e6f6aa6f2abd5d5fd854 | 2026-01-21T00:00:00-05:00 | Credible CO2 Comparisons: A Machine Learning Approach to Vehicle Powertrain Assessment | arXiv:2601.14022v1 Announce Type: new Abstract: Decarbonizing road transport requires consistent and transparent methods for comparing CO2 emissions across vehicle technologies. This paper proposes a machine learning-based framework for like-for-like operational assessment of internal combustion engine vehicles (ICEVs)... | https://arxiv.org/abs/2601.14022 | Academic Papers | svg |
6d20ab8682f5163b5bad6fed707f7ad791a237b024b566c81f61523cf4671f61 | 2026-01-21T00:00:00-05:00 | Universal Approximation Theorem for Input-Connected Multilayer Perceptrons | arXiv:2601.14026v1 Announce Type: new Abstract: We introduce the Input-Connected Multilayer Perceptron (IC-MLP), a feedforward neural network architecture in which each hidden neuron receives, in addition to the outputs of the preceding layer, a direct affine connection from the raw input. We first study this architect... | https://arxiv.org/abs/2601.14026 | Academic Papers | svg |
f309401f0caceb8d685081bf216eee43077a17423714a0e324df449fc252a4b0 | 2026-01-21T00:00:00-05:00 | Numina-Lean-Agent: An Open and General Agentic Reasoning System for Formal Mathematics | arXiv:2601.14027v1 Announce Type: new Abstract: Agentic systems have recently become the dominant paradigm for formal theorem proving, achieving strong performance by coordinating multiple models and tools. However, existing approaches often rely on task-specific pipelines and trained formal provers, limiting their fle... | https://arxiv.org/abs/2601.14027 | Academic Papers | svg |
c3a8a612b188f54de79f2de14d441add03e123fe41170ccd585f86f5db047b97 | 2026-01-21T00:00:00-05:00 | Likelihood-Separable Diffusion Inference for Multi-Image MRI Super-Resolution | arXiv:2601.14030v1 Announce Type: new Abstract: Diffusion models are the current state-of-the-art for solving inverse problems in imaging. Their impressive generative capability allows them to approximate sampling from a prior distribution, which alongside a known likelihood function permits posterior sampling without ... | https://arxiv.org/abs/2601.14030 | Academic Papers | svg |
92ecf441092ec644f84f04a21dc3609c1a94bc16deaf14bc33ec584d1c20aec1 | 2026-01-21T00:00:00-05:00 | RM-Distiller: Exploiting Generative LLM for Reward Model Distillation | arXiv:2601.14032v1 Announce Type: new Abstract: Reward models (RMs) play a pivotal role in aligning large language models (LLMs) with human preferences. Due to the difficulty of obtaining high-quality human preference annotations, distilling preferences from generative LLMs has emerged as a standard practice. However, ... | https://arxiv.org/abs/2601.14032 | Academic Papers | svg |
c999a856d4573088960b8b7f5c5d90bdd1858a712c6baad2a3d7dddd94382f89 | 2026-01-21T00:00:00-05:00 | PAC-Private Responses with Adversarial Composition | arXiv:2601.14033v1 Announce Type: new Abstract: Modern machine learning models are increasingly deployed behind APIs. This renders standard weight-privatization methods (e.g. DP-SGD) unnecessarily noisy at the cost of utility. While model weights may vary significantly across training datasets, model responses to speci... | https://arxiv.org/abs/2601.14033 | Academic Papers | svg |
34bb2236d4354d0acc84f98886180749cf0304db93103593b7a8cd7e9a84bc8b | 2026-01-21T00:00:00-05:00 | Analyzing the Availability of E-Mail Addresses for PyPI Libraries | arXiv:2601.14034v1 Announce Type: new Abstract: Open Source Software (OSS) libraries form the backbone of modern software systems, yet their long-term sustainability often depends on maintainers being reachable for support, coordination, and security reporting. In this paper, we empirically analyze the availability of ... | https://arxiv.org/abs/2601.14034 | Academic Papers | svg |
0ce0e9d921431b67e49a0b2f01a367385dddfba3b6ac20f01236df67bab9d11f | 2026-01-21T00:00:00-05:00 | Human detectors are surprisingly powerful reward models | arXiv:2601.14037v1 Announce Type: new Abstract: Video generation models have recently achieved impressive visual fidelity and temporal coherence. Yet, they continue to struggle with complex, non-rigid motions, especially when synthesizing humans performing dynamic actions such as sports, dance, etc. Generated videos of... | https://arxiv.org/abs/2601.14037 | Academic Papers | svg |
8cec323f776561f07079f59edc114b31d8f2ddd062f94db0e93bc364447dec95 | 2026-01-21T00:00:00-05:00 | Correcting and Quantifying Systematic Errors in 3D Box Annotations for Autonomous Driving | arXiv:2601.14038v1 Announce Type: new Abstract: Accurate ground truth annotations are critical to supervised learning and evaluating the performance of autonomous vehicle systems. These vehicles are typically equipped with active sensors, such as LiDAR, which scan the environment in predefined patterns. 3D box annotati... | https://arxiv.org/abs/2601.14038 | Academic Papers | svg |
39590b0c270c223ccd62f5468b3f353af634b0aeea4c3466101b3dfb7060a807 | 2026-01-21T00:00:00-05:00 | Generalizing Abstention for Noise-Robust Learning in Medical Image Segmentation | arXiv:2601.14039v1 Announce Type: new Abstract: Label noise is a critical problem in medical image segmentation, often arising from the inherent difficulty of manual annotation. Models trained on noisy data are prone to overfitting, which degrades their generalization performance. While a number of methods and strategi... | https://arxiv.org/abs/2601.14039 | Academic Papers | svg |
96309752f7aca2486e5077fe2cb84d9c67c925a7c6f5db98e2594f93bbf9cdbc | 2026-01-21T00:00:00-05:00 | Top 10 Open Challenges Steering the Future of Diffusion Language Model and Its Variants | arXiv:2601.14041v1 Announce Type: new Abstract: The paradigm of Large Language Models (LLMs) is currently defined by auto-regressive (AR) architectures, which generate text through a sequential ``brick-by-brick'' process. Despite their success, AR models are inherently constrained by a causal bottleneck that limits glo... | https://arxiv.org/abs/2601.14041 | Academic Papers | svg |
7314ac098894300203756db339e27ed69cc5c4ade3906535b498780f678c1860 | 2026-01-21T00:00:00-05:00 | Federated Balanced Learning | arXiv:2601.14042v1 Announce Type: new Abstract: Federated learning is a paradigm of joint learning in which clients collaborate by sharing model parameters instead of data. However, in the non-iid setting, the global model experiences client drift, which can seriously affect the final performance of the model. Previous... | https://arxiv.org/abs/2601.14042 | Academic Papers | svg |
a05f2b6bb6a19e8ffd474e53c7466ea38adeb9124135ec79e71e1ba013f5528d | 2026-01-21T00:00:00-05:00 | Weather-R1: Logically Consistent Reinforcement Fine-Tuning for Multimodal Reasoning in Meteorology | arXiv:2601.14044v1 Announce Type: new Abstract: While Vision Language Models (VLMs) show advancing reasoning capabilities, their application in meteorology is constrained by a domain gap and a reasoning faithfulness gap. Specifically, mainstream Reinforcement Fine-Tuning (RFT) can induce Self-Contradictory Reasoning (S... | https://arxiv.org/abs/2601.14044 | Academic Papers | svg |
46d65a78f766ef937f2ab0a7f9de128095f0c8733c2e24b9a86c60373c23b8f2 | 2026-01-21T00:00:00-05:00 | PRiSM: Benchmarking Phone Realization in Speech Models | arXiv:2601.14046v1 Announce Type: new Abstract: Phone recognition (PR) serves as the atomic interface for language-agnostic modeling for cross-lingual speech processing and phonetic analysis. Despite prolonged efforts in developing PR systems, current evaluations only measure surface-level transcription accuracy. We in... | https://arxiv.org/abs/2601.14046 | Academic Papers | svg |
f572159a67241e39d019d4330459a4a4e1165eba1981a1d43f67a715c97e129d | 2026-01-21T00:00:00-05:00 | Collective intelligence in science: direct elicitation of diverse information from experts with unknown information structure | arXiv:2601.14047v1 Announce Type: new Abstract: Suppose we need a deep collective analysis of an open scientific problem: there is a complex scientific hypothesis and a large online group of mutually unrelated experts with relevant private information of a diverse and unpredictable nature. This information may be resul... | https://arxiv.org/abs/2601.14047 | Academic Papers | svg |
523a4d4c9325fce095d7e18d1073e927b19aa85b310d3de8bb7c5d8c2a2d1849 | 2026-01-21T00:00:00-05:00 | Understanding Multilingualism in Mixture-of-Experts LLMs: Routing Mechanism, Expert Specialization, and Layerwise Steering | arXiv:2601.14050v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures have shown strong multilingual capabilities, yet the internal mechanisms underlying performance gains and cross-language differences remain insufficiently understood. In this work, we conduct a systematic analysis of MoE models, exam... | https://arxiv.org/abs/2601.14050 | Academic Papers | svg |
bfc6fc53adeaf3ad4a0fc945450f5e9343677874a8478c93a31499a7afd3f3ee | 2026-01-21T00:00:00-05:00 | Kakugo: Distillation of Low-Resource Languages into Small Language Models | arXiv:2601.14051v1 Announce Type: new Abstract: We present Kakugo, a novel and cost-effective pipeline designed to train general-purpose Small Language Models (SLMs) for low-resource languages using only the language name as input. By using a large teacher model to generate synthetic prompts and translate instruction d... | https://arxiv.org/abs/2601.14051 | Academic Papers | svg |
d02b529bf116c842ca27fc24c1f505a6eccd629cc78b376a697a394408232882 | 2026-01-21T00:00:00-05:00 | Vision Also You Need: Navigating Out-of-Distribution Detection with Multimodal Large Language Model | arXiv:2601.14052v1 Announce Type: new Abstract: Out-of-Distribution (OOD) detection is a critical task that has garnered significant attention. The emergence of CLIP has spurred extensive research into zero-shot OOD detection, often employing a training-free approach. Current methods leverage expert knowledge from larg... | https://arxiv.org/abs/2601.14052 | Academic Papers | svg |
00bdd0c98a4403c65dc9e9ca3844412e4b078ab10fd2f56772b0aae32fef86ff | 2026-01-21T00:00:00-05:00 | LLMOrbit: A Circular Taxonomy of Large Language Models -From Scaling Walls to Agentic AI Systems | arXiv:2601.14053v1 Announce Type: new Abstract: The field of artificial intelligence has undergone a revolution from foundational Transformer architectures to reasoning-capable systems approaching human-level performance. We present LLMOrbit, a comprehensive circular taxonomy navigating the landscape of large language ... | https://arxiv.org/abs/2601.14053 | Academic Papers | svg |
a01aa817457d4428cdd11ffd0ae3a0c039e5069872dc5808b9985c70b4ba1aec | 2026-01-21T00:00:00-05:00 | SecureSplit: Mitigating Backdoor Attacks in Split Learning | arXiv:2601.14054v1 Announce Type: new Abstract: Split Learning (SL) offers a framework for collaborative model training that respects data privacy by allowing participants to share the same dataset while maintaining distinct feature sets. However, SL is susceptible to backdoor attacks, in which malicious clients subtly... | https://arxiv.org/abs/2601.14054 | Academic Papers | svg |
47201dcdf8ae150a2f68d0be2ad82e9879242abbf4a3bb1d689a104a4e7ae184 | 2026-01-21T00:00:00-05:00 | Decoder-Free Supervoxel GNN for Accurate Brain-Tumor Localization in Multi-Modal MRI | arXiv:2601.14055v1 Announce Type: new Abstract: Modern vision backbones for 3D medical imaging typically process dense voxel grids through parameter-heavy encoder-decoder structures, a design that allocates a significant portion of its parameters to spatial reconstruction rather than feature learning. Our approach intr... | https://arxiv.org/abs/2601.14055 | Academic Papers | svg |
2978efb84dd0c2083a3bb76157bf4260b3993d125d216967de00fa0bfb8a88ce | 2026-01-21T00:00:00-05:00 | POCI-Diff: Position Objects Consistently and Interactively with 3D-Layout Guided Diffusion | arXiv:2601.14056v1 Announce Type: new Abstract: We propose a diffusion-based approach for Text-to-Image (T2I) generation with consistent and interactive 3D layout control and editing. While prior methods improve spatial adherence using 2D cues or iterative copy-warp-paste strategies, they often distort object geometry ... | https://arxiv.org/abs/2601.14056 | Academic Papers | svg |
1cc628375b75b5e3be6937884a577ec746e9633ad183a511bcfefec2cf741b84 | 2026-01-21T00:00:00-05:00 | Verifying Floating-Point Programs in Stainless | arXiv:2601.14059v1 Announce Type: new Abstract: We extend the Stainless deductive verifier with floating-point support, providing the first automated verification support for floating-point numbers for a subset of Scala that includes polymorphism, recursion and higher-order functions. We follow the recent approach in t... | https://arxiv.org/abs/2601.14059 | Academic Papers | svg |
d1708d93bf522dc25a2433edadfaa94f2932b6a02222c6fdba908f7bff300d7f | 2026-01-21T00:00:00-05:00 | Fine-Grained Zero-Shot Composed Image Retrieval with Complementary Visual-Semantic Integration | arXiv:2601.14060v1 Announce Type: new Abstract: Zero-shot composed image retrieval (ZS-CIR) is a rapidly growing area with significant practical applications, allowing users to retrieve a target image by providing a reference image and a relative caption describing the desired modifications. Existing ZS-CIR methods oft... | https://arxiv.org/abs/2601.14060 | Academic Papers | svg |
008f05092ca9a73b153928fa14913cc0d244f70fc7ee6fc6b94c6f0e7481693f | 2026-01-21T00:00:00-05:00 | XCR-Bench: A Multi-Task Benchmark for Evaluating Cultural Reasoning in LLMs | arXiv:2601.14063v1 Announce Type: new Abstract: Cross-cultural competence in large language models (LLMs) requires the ability to identify Culture-Specific Items (CSIs) and to adapt them appropriately across cultural contexts. Progress in evaluating this capability has been constrained by the scarcity of high-quality C... | https://arxiv.org/abs/2601.14063 | Academic Papers | svg |
3417b52b6f2fd68b78dd3cb56503909ee671835f8edffc914ff2bda837293461 | 2026-01-21T00:00:00-05:00 | VERIDAH: Solving Enumeration Anomaly Aware Vertebra Labeling across Imaging Sequences | arXiv:2601.14066v1 Announce Type: new Abstract: The human spine commonly consists of seven cervical, twelve thoracic, and five lumbar vertebrae. However, enumeration anomalies may result in individuals having eleven or thirteen thoracic vertebrae and four or six lumbar vertebrae. Although the identification of enumerat... | https://arxiv.org/abs/2601.14066 | Academic Papers | svg |
2906d6d6e410c56be6475819cf34d36c1429a0c47525f6032f0f4316451c8d6c | 2026-01-21T00:00:00-05:00 | Modular Attractor Acceleration in Infinite-State Games (Full Version) | arXiv:2601.14068v1 Announce Type: new Abstract: Infinite-state games provide a framework for the synthesis of reactive systems with unbounded data domains. Solving such games typically relies on computing symbolic fixpoints, particularly symbolic attractors. However, these computations may not terminate, and while rece... | https://arxiv.org/abs/2601.14068 | Academic Papers | svg |
fbc3f2a17e60577a0fb0d5f3108b864952eb01e09b64f0d44fd8313959c51659 | 2026-01-21T00:00:00-05:00 | Unsupervised Video Class-Incremental Learning via Deep Embedded Clustering Management | arXiv:2601.14069v1 Announce Type: new Abstract: Unsupervised video class incremental learning (uVCIL) represents an important learning paradigm for learning video information without forgetting, and without considering any data labels. Prior approaches have focused on supervised class-incremental learning, relying on u... | https://arxiv.org/abs/2601.14069 | Academic Papers | svg |
c470f75d4a45bc35bcadbbc2500f384d476be8437f5d6543118bafc81f85a542 | 2026-01-21T00:00:00-05:00 | On the optimal shape parameter for kernel methods: Sharp direct and inverse statements | arXiv:2601.14070v1 Announce Type: new Abstract: The search for the optimal shape parameter for Radial Basis Function (RBF) kernel approximation has been an outstanding research problem for decades. In this work, we establish a theoretical framework for this problem by leveraging a recently established theory on sharp d... | https://arxiv.org/abs/2601.14070 | Academic Papers | svg |
d4c8ce6dfae2d4266466b868019e0cb06556d3e685358c24ad40f98c4e867fd2 | 2026-01-21T00:00:00-05:00 | Utilizing the Perceived Age to Maximize Freshness in Query-Based Update Systems | arXiv:2601.14075v1 Announce Type: new Abstract: Query-based sampling has become an increasingly popular technique for monitoring Markov sources in pull-based update systems. However, most of the contemporary literature on this assumes an exponential distribution for query delay and often relies on the assumption that t... | https://arxiv.org/abs/2601.14075 | Academic Papers | svg |
c0713323f7554511ba5732702f7850c3a317e4c4db10bfa5b3d6423bddf36eb5 | 2026-01-21T00:00:00-05:00 | From Trees to Tree-Like: Distribution and Synthesis for Asynchronous Automata | arXiv:2601.14078v1 Announce Type: new Abstract: We revisit constructions for distribution and synthesis of Zielonka's asynchronous automata in restricted settings. We show first a simple, quadratic, distribution construction for asynchronous automata, where the process architecture is tree-like. An architecture is tree... | https://arxiv.org/abs/2601.14078 | Academic Papers | svg |
f5bb7c5fa5de32e4eb0d761e2a0399669fc520b8b907cf33634c56228153252d | 2026-01-21T00:00:00-05:00 | VENI: Variational Encoder for Natural Illumination | arXiv:2601.14079v1 Announce Type: new Abstract: Inverse rendering is an ill-posed problem, but priors like illumination priors, can simplify it. Existing work either disregards the spherical and rotation-equivariant nature of illumination environments or does not provide a well-behaved latent space. We propose a rotati... | https://arxiv.org/abs/2601.14079 | Academic Papers | svg |
9f6f08dbf9edb5546b7bb637ff252cbd595ccbdc9d0bc26ea30c3efb828edd14 | 2026-01-21T00:00:00-05:00 | Feature-Aware Test Generation for Deep Learning Models | arXiv:2601.14081v1 Announce Type: new Abstract: As deep learning models are widely used in software systems, test generation plays a crucial role in assessing the quality of such models before deployment. To date, the most advanced test generators rely on generative AI to synthesize inputs; however, these approaches re... | https://arxiv.org/abs/2601.14081 | Academic Papers | svg |
a0bd1cbaa2c97326f9d78744622c55fe500ae44a97588b448f355ed7bdcc19dd | 2026-01-21T00:00:00-05:00 | DermaBench: A Clinician-Annotated Benchmark Dataset for Dermatology Visual Question Answering and Reasoning | arXiv:2601.14084v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly important in medical applications; however, their evaluation in dermatology remains limited by datasets that focus primarily on image-level classification tasks such as lesion recognition. While valuable for recognition, such... | https://arxiv.org/abs/2601.14084 | Academic Papers | svg |
5c4e3833f480637ed1c9b5a5c4babe60a8db39bfde9bfe883136eaa8f52af6dc | 2026-01-21T00:00:00-05:00 | Two-Stream temporal transformer for video action classification | arXiv:2601.14086v1 Announce Type: new Abstract: Motion representation plays an important role in video understanding and has many applications including action recognition, robot and autonomous guidance or others. Lately, transformer networks, through their self-attention mechanism capabilities, have proved their effic... | https://arxiv.org/abs/2601.14086 | Academic Papers | svg |
8c70974cf5e529b87f26cc7897c3d6a25024cfc3e43aaa941692ad5c149069c8 | 2026-01-21T00:00:00-05:00 | '1'-bit Count-based Sorting Unit to Reduce Link Power in DNN Accelerators | arXiv:2601.14087v1 Announce Type: new Abstract: Interconnect power consumption remains a bottleneck in Deep Neural Network (DNN) accelerators. While ordering data based on '1'-bit counts can mitigate this via reduced switching activity, practical hardware sorting implementations remain underexplored. This work proposes... | https://arxiv.org/abs/2601.14087 | Academic Papers | svg |
4496a3df95bf3ed70546f22f350fb35fb465ee01640fc7be9bffd0674d20441f | 2026-01-21T00:00:00-05:00 | Near Optimal Code Construction for the Adversarial Torn Paper Channel With Edit Errors | arXiv:2601.14088v1 Announce Type: new Abstract: Motivated by DNA storage systems and 3D fingerprinting, this work studies the adversarial torn paper channel with edit errors. This channel first applies at most $t_e$ edit errors (i.e., insertions, deletions, and substitutions) to the transmitted word and then breaks it ... | https://arxiv.org/abs/2601.14088 | Academic Papers | svg |
67fda2380976241c9fcbb23859f108f020094d940167da8bcb8755b45436c5da | 2026-01-21T00:00:00-05:00 | Data-Driven Safe Output Regulation of Strict-Feedback Linear Systems with Input Delay | arXiv:2601.14089v1 Announce Type: new Abstract: This paper develops a data-driven safe control framework for linear systems possessing a known strict-feedback structure, but with most plant parameters, external disturbances, and input delay being unknown. By leveraging Koopman operator theory, we utilize Krylov dynamic... | https://arxiv.org/abs/2601.14089 | Academic Papers | svg |
b1663680c090ca606ac3ed62ad462247605135b52da76cf958f21a2b4b6be96f | 2026-01-21T00:00:00-05:00 | Zero-shot adaptable task planning for autonomous construction robots: a comparative study of lightweight single and multi-AI agent systems | arXiv:2601.14091v1 Announce Type: new Abstract: Robots are expected to play a major role in the future construction industry but face challenges due to high costs and difficulty adapting to dynamic tasks. This study explores the potential of foundation models to enhance the adaptability and generalizability of task pla... | https://arxiv.org/abs/2601.14091 | Academic Papers | svg |
7af8383381a4794451ca9d04c8be7a17e2b764d14638ea0606e03541a2b5029e | 2026-01-21T00:00:00-05:00 | Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning | arXiv:2601.14092v1 Announce Type: new Abstract: Due to their adaptability and mobility, Unmanned Aerial Vehicles (UAVs) are becoming increasingly essential for wireless network services, particularly for data harvesting tasks. In this context, Artificial Intelligence (AI)-based approaches have gained significant attent... | https://arxiv.org/abs/2601.14092 | Academic Papers | svg |
f46055e74c200ba41443aa40771f6f588e80c471b0b673e9c70af913eb5e76db | 2026-01-21T00:00:00-05:00 | Remapping and navigation of an embedding space via error minimization: a fundamental organizational principle of cognition in natural and artificial systems | arXiv:2601.14096v1 Announce Type: new Abstract: The emerging field of diverse intelligence seeks an integrated view of problem-solving in agents of very different provenance, composition, and substrates. From subcellular chemical networks to swarms of organisms, and across evolved, engineered, and chimeric systems, it ... | https://arxiv.org/abs/2601.14096 | Academic Papers | svg |
e02fb0775b0baf53439d5b22977ec24d2a490569486db03a20732492730b4975 | 2026-01-21T00:00:00-05:00 | A flexible language model-assisted electronic design automation framework | arXiv:2601.14098v1 Announce Type: new Abstract: Large language models (LLMs) are transforming electronic design automation (EDA) by enhancing design stages such as schematic design, simulation, netlist synthesis, and place-and-route. Existing methods primarily focus these optimisations within isolated open-source EDA t... | https://arxiv.org/abs/2601.14098 | Academic Papers | svg |
51189015b972b9b2814036b7648321a514119af1db6fe584717adb4b1893fc69 | 2026-01-21T00:00:00-05:00 | Causal feature selection framework for stable soft sensor modeling based on time-delayed cross mapping | arXiv:2601.14099v1 Announce Type: new Abstract: Soft sensor modeling plays a crucial role in process monitoring. Causal feature selection can enhance the performance of soft sensor models in industrial applications. However, existing methods ignore two critical characteristics of industrial processes. Firstly, causal r... | https://arxiv.org/abs/2601.14099 | Academic Papers | svg |
bb399b9197ecb5e75144fe15858d0cccdcc5bacc79fef9879a0d2dca737fff1a | 2026-01-21T00:00:00-05:00 | Curriculum-Based Strategies for Efficient Cross-Domain Action Recognition | arXiv:2601.14101v1 Announce Type: new Abstract: Despite significant progress in human action recognition, generalizing to diverse viewpoints remains a challenge. Most existing datasets are captured from ground-level perspectives, and models trained on them often struggle to transfer to drastically different domains suc... | https://arxiv.org/abs/2601.14101 | Academic Papers | svg |
1a9b91c33ea8c8d64bb1f8206d50014126e5580aade2050972a85b9695912802 | 2026-01-21T00:00:00-05:00 | Interp3D: Correspondence-aware Interpolation for Generative Textured 3D Morphing | arXiv:2601.14103v1 Announce Type: new Abstract: Textured 3D morphing seeks to generate smooth and plausible transitions between two 3D assets, preserving both structural coherence and fine-grained appearance. This ability is crucial not only for advancing 3D generation research but also for practical applications in an... | https://arxiv.org/abs/2601.14103 | Academic Papers | svg |
ca46f23097f400cbfca780b4c25ca6c7e9a4e4d45f8ba6f804933ad9c79b84c1 | 2026-01-21T00:00:00-05:00 | Diffusion-Guided Backdoor Attacks in Real-World Reinforcement Learning | arXiv:2601.14104v1 Announce Type: new Abstract: Backdoor attacks embed hidden malicious behaviors in reinforcement learning (RL) policies and activate them using triggers at test time. Most existing attacks are validated only in simulation, while their effectiveness in real-world robotic systems remains unclear. In phy... | https://arxiv.org/abs/2601.14104 | Academic Papers | svg |
f3d6c460bff79c93db431dde4cab73b05d3e7ee1fd9814194b62ca92bc4fdd1e | 2026-01-21T00:00:00-05:00 | Truth with a Twist: The Rhetoric of Persuasion in Professional vs. Community-Authored Fact-Checks | arXiv:2601.14105v1 Announce Type: new Abstract: This study presents the first large-scale comparison of persuasion techniques present in crowd- versus professionally-written debunks. Using extensive datasets from Community Notes (CNs), EUvsDisinfo, and the Database of Known Fakes (DBKF), we quantify the prevalence and ... | https://arxiv.org/abs/2601.14105 | Academic Papers | svg |
4f54ef52377d21f6e57221ec548270ec18ecf41a39f51f3fa664723493cabfe2 | 2026-01-21T00:00:00-05:00 | Communication Technologies for Intelligent Transportation Systems: From Railways to UAVs and Beyond | arXiv:2601.14106v1 Announce Type: new Abstract: This white paper aims to comprehensively analyze and consolidate the state of the art in communication technologies supporting modern and future Information and Communication Technology (ICT). Its primary objective is to establish a common understanding of how communicati... | https://arxiv.org/abs/2601.14106 | Academic Papers | svg |
918a36310b9d74beea1de97ddd86ca044a8b37cf544389a6febd48dd240f4c71 | 2026-01-21T00:00:00-05:00 | AttackMate: Realistic Emulation and Automation of Cyber Attack Scenarios Across the Kill Chain | arXiv:2601.14108v1 Announce Type: new Abstract: Adversary emulation tools facilitate scripting and automated execution of cyber attack chains, thereby reducing costs and manual expert effort required for security testing, cyber exercises, and intrusion detection research. However, due to the fact that existing tools ty... | https://arxiv.org/abs/2601.14108 | Academic Papers | svg |
009942a658b1151765788cdcb4fc0bb4b757bf2ef74d77a4be20d6978c8a410c | 2026-01-21T00:00:00-05:00 | TLSQL: Table Learning Structured Query Language | arXiv:2601.14109v1 Announce Type: new Abstract: Table learning, which lies at the intersection of machine learning and modern database systems, has recently attracted growing attention. However, existing frameworks typically require explicit data export and extensive feature engineering, creating a high barrier for dat... | https://arxiv.org/abs/2601.14109 | Academic Papers | svg |
1185a04780975e8cf62b3a54e86eee9823890828b7354a29110588c3b2650de7 | 2026-01-21T00:00:00-05:00 | PMCE: Probabilistic Multi-Granularity Semantics with Caption-Guided Enhancement for Few-Shot Learning | arXiv:2601.14111v1 Announce Type: new Abstract: Few-shot learning aims to identify novel categories from only a handful of labeled samples, where prototypes estimated from scarce data are often biased and generalize poorly. Semantic-based methods alleviate this by introducing coarse class-level information, but they ar... | https://arxiv.org/abs/2601.14111 | Academic Papers | svg |
5cb0cd9698ff9a02c8e0c67c607e984f4a42b511e24fca6195933a193f7153ae | 2026-01-21T00:00:00-05:00 | Learning to Explain: Supervised Token Attribution from Transformer Attention Patterns | arXiv:2601.14112v1 Announce Type: new Abstract: Explainable AI (XAI) has become critical as transformer-based models are deployed in high-stakes applications including healthcare, legal systems, and financial services, where opacity hinders trust and accountability. Transformers self-attention mechanisms have proven va... | https://arxiv.org/abs/2601.14112 | Academic Papers | svg |
55bcb43542cc65798d118e503b42f5f775ac39b6845fc912042f7a905aa8e058 | 2026-01-21T00:00:00-05:00 | Partial Reductions for Kleene Algebra with Linear Hypotheses | arXiv:2601.14114v1 Announce Type: new Abstract: Kleene algebra (KA) is an important tool for reasoning about general program equivalences, with a decidable and complete equational theory. However, KA cannot always prove equivalences between specific programs. For this purpose, one adds hypotheses to KA that encode prog... | https://arxiv.org/abs/2601.14114 | Academic Papers | svg |
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