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795105cf7e30e4a7c888dfed247490e5860077957c62924f253bb7a36053b9bc | 2026-01-01T00:00:00-05:00 | Bayesian Subspace Identification in the MIMO Case | arXiv:2512.24435v1 Announce Type: new Abstract: This report investigates the extension of the Bayesian Subspace System Identification method proposed in our previous work to the Multiple-Input Multiple-Output (MIMO) case. We derive new equivariant priors and posterior distributions specifically suited for the MIMO fram... | https://arxiv.org/abs/2512.24435 | Academic Papers | svg |
df51774ee9dc0f2062a275bd279c81329523768c1fa482f7043cf5f9dde1d963 | 2026-01-01T00:00:00-05:00 | Exploring Compositionality in Vision Transformers using Wavelet Representations | arXiv:2512.24438v1 Announce Type: new Abstract: While insights into the workings of the transformer model have largely emerged by analysing their behaviour on language tasks, this work investigates the representations learnt by the Vision Transformer (ViT) encoder through the lens of compositionality. We introduce a fr... | https://arxiv.org/abs/2512.24438 | Academic Papers | svg |
a2d0fbeba3670d2c755ca3367afcfe63701b7e9afd2b554116f7e21043bb6e4a | 2026-01-01T00:00:00-05:00 | Sparse classification with positive-confidence data in high dimensions | arXiv:2512.24443v1 Announce Type: new Abstract: High-dimensional learning problems, where the number of features exceeds the sample size, often require sparse regularization for effective prediction and variable selection. While established for fully supervised data, these techniques remain underexplored in weak-superv... | https://arxiv.org/abs/2512.24443 | Academic Papers | svg |
819d85bd4dd72f4e1f6b1ab34ae7d67a2f5580c3ca920acdbd53aefa96c36dec | 2026-01-01T00:00:00-05:00 | Adaptive Learning Guided by Bias-Noise-Alignment Diagnostics | arXiv:2512.24445v1 Announce Type: new Abstract: Learning systems deployed in nonstationary and safety-critical environments often suffer from instability, slow convergence, or brittle adaptation when learning dynamics evolve over time. While modern optimization, reinforcement learning, and meta-learning methods adapt t... | https://arxiv.org/abs/2512.24445 | Academic Papers | svg |
65f26b5ed715e3bc5f282c6f1a43d70e423152daf78018285dbb295964470c52 | 2026-01-01T00:00:00-05:00 | Generative forecasting with joint probability models | arXiv:2512.24446v1 Announce Type: new Abstract: Chaotic dynamical systems exhibit strong sensitivity to initial conditions and often contain unresolved multiscale processes, making deterministic forecasting fundamentally limited. Generative models offer an appealing alternative by learning distributions over plausible ... | https://arxiv.org/abs/2512.24446 | Academic Papers | svg |
4de9354bf20d6cc354ac20942ba5855f04452032fdc96194a3ab1d040b970411 | 2026-01-01T00:00:00-05:00 | PackKV: Reducing KV Cache Memory Footprint through LLM-Aware Lossy Compression | arXiv:2512.24449v1 Announce Type: new Abstract: Transformer-based large language models (LLMs) have demonstrated remarkable potential across a wide range of practical applications. However, long-context inference remains a significant challenge due to the substantial memory requirements of the key-value (KV) cache, whi... | https://arxiv.org/abs/2512.24449 | Academic Papers | svg |
dd6494572f2985ab5b54f68f7e1f0a9f41278fce9008e809e21b86f752324c20 | 2026-01-01T00:00:00-05:00 | Privacy-Preserving Semantic Communications via Multi-Task Learning and Adversarial Perturbations | arXiv:2512.24452v1 Announce Type: new Abstract: Semantic communications conveys task-relevant meaning rather than focusing solely on message reconstruction, improving bandwidth efficiency and robustness for next-generation wireless systems. However, learned semantic representations can still leak sensitive information ... | https://arxiv.org/abs/2512.24452 | Academic Papers | svg |
613248a3379c1833908439b595b55fa1f0bb914c790465ebca4d5c0c4a1d22ba | 2026-01-01T00:00:00-05:00 | Multipliers for forced Lurye systems with slope-restricted nonlinearities | arXiv:2512.24453v1 Announce Type: new Abstract: Dynamic multipliers can be used to guarantee the stability of Lurye systems with slope-restricted nonlinearities, but give no guarantee that the closed-loop system has finite incremental gain. We show that multipliers guarantee the closed-loop power gain to be bounded and... | https://arxiv.org/abs/2512.24453 | Academic Papers | svg |
216132b927fe60fe0f6f61b1d6a5e82d3744744d52892fae42ba988abce5bc4a | 2026-01-01T00:00:00-05:00 | Fast high-order spectral solvers for PDEs on triangulated surfaces with applications to deforming surfaces | arXiv:2512.24456v1 Announce Type: new Abstract: In this paper, we extend the classical quadrilateral based hierarchical Poincar\'e-Steklov (HPS) framework to triangulated geometries. Traditionally, the HPS method takes as input an unstructured, high-order quadrilateral mesh and relies on tensor-product spectral discret... | https://arxiv.org/abs/2512.24456 | Academic Papers | svg |
a41d081020a22a5cd84334eda3df309270c6767cc8bda7b70ffa9908b80f44d2 | 2026-01-01T00:00:00-05:00 | Document Data Matching for Blockchain-Supported Real Estate | arXiv:2512.24457v1 Announce Type: new Abstract: The real estate sector remains highly dependent on manual document handling and verification, making processes inefficient and prone to fraud. This work presents a system that integrates optical character recognition (OCR), natural language processing (NLP), and verifiabl... | https://arxiv.org/abs/2512.24457 | Academic Papers | svg |
c24b9ffabb60003dd58ba33e85b7e9d36cbb9beb4a6142ae3b1eced212d94136 | 2026-01-01T00:00:00-05:00 | Cleaning English Abstracts of Scientific Publications | arXiv:2512.24459v1 Announce Type: new Abstract: Scientific abstracts are often used as proxies for the content and thematic focus of research publications. However, a significant share of published abstracts contains extraneous information-such as publisher copyright statements, section headings, author notes, registra... | https://arxiv.org/abs/2512.24459 | Academic Papers | svg |
82f1c4997bcbceae0e568947d36fb4553cd52567c35aa8e969dc4f9a69fe4471 | 2026-01-01T00:00:00-05:00 | IELTS Writing Revision Platform with Automated Essay Scoring and Adaptive Feedback | arXiv:2512.24460v1 Announce Type: new Abstract: This paper presents the design, development, and evaluation of a proposed revision platform assisting candidates for the International English Language Testing System (IELTS) writing exam. Traditional IELTS preparation methods lack personalised feedback, catered to the IE... | https://arxiv.org/abs/2512.24460 | Academic Papers | svg |
d2bcdcf2db1def989fc64678117b09ad00e4a81176a6c70a8726da799cc860bc | 2026-01-01T00:00:00-05:00 | Align While Search: Belief-Guided Exploratory Inference for World-Grounded Embodied Agents | arXiv:2512.24461v1 Announce Type: new Abstract: In this paper, we propose a test-time adaptive agent that performs exploratory inference through posterior-guided belief refinement without relying on gradient-based updates or additional training for LLM agent operating under partial observability. Our agent maintains an... | https://arxiv.org/abs/2512.24461 | Academic Papers | svg |
daa228bdb249f4842caf3266d1b2f619616b92657c9e60eeed8f95c59c207487 | 2026-01-01T00:00:00-05:00 | "Game Changer" or "Overenthusiastic Drunk Acquaintance"? Generative AI Use by Blind and Low Vision Software Professionals in the Workplace | arXiv:2512.24462v1 Announce Type: new Abstract: The software development workplace poses numerous technical and collaborative accessibility challenges for blind and low vision software professionals (BLVSPs). Though Generative AI (GenAI) is increasingly adopted within the software development industry and has been a ra... | https://arxiv.org/abs/2512.24462 | Academic Papers | svg |
df7a5eeb2f55508113daf543bc05ba0037f132cd23172ad5fafcf0abc09ff5f2 | 2026-01-01T00:00:00-05:00 | Spectral and Spatial Graph Learning for Multispectral Solar Image Compression | arXiv:2512.24463v1 Announce Type: new Abstract: High-fidelity compression of multispectral solar imagery remains challenging for space missions, where limited bandwidth must be balanced against preserving fine spectral and spatial details. We present a learned image compression framework tailored to solar observations,... | https://arxiv.org/abs/2512.24463 | Academic Papers | svg |
fd81498d9259ff34966f1bc315d2ffa9ecabf904403ecd9c452294b73d1413a6 | 2026-01-01T00:00:00-05:00 | On the Difficulty of Measuring Divisiveness of Proposals under Ranked Preferences | arXiv:2512.24467v1 Announce Type: new Abstract: Given the stated preferences of several people over a number of proposals regarding public policy initiatives, some of those proposals might be judged to be more ``divisive'' than others. When designing online participatory platforms to support digital democracy initiativ... | https://arxiv.org/abs/2512.24467 | Academic Papers | svg |
7b5dc3b003e856ed7cbbbf5d8cada288ee03ee25cfb20c87c539aa9e3dca69fc | 2026-01-01T00:00:00-05:00 | Infinite families of graphs and stable completion of arbitrary matrices, Part I | arXiv:2512.24468v1 Announce Type: new Abstract: We study deterministic constructions of graphs for which the unique completion of low rank matrices is generically possible regardless of the values of the entries. We relate the completability to the presence of some patterns (particular unions of self-avoiding walks) in... | https://arxiv.org/abs/2512.24468 | Academic Papers | svg |
b85aa54a98a63e0ff65117f4c69408e1a4f0a9eb589bb6d9bcd808b4676499b0 | 2026-01-01T00:00:00-05:00 | Foundation models on the bridge: Semantic hazard detection and safety maneuvers for maritime autonomy with vision-language models | arXiv:2512.24470v1 Announce Type: new Abstract: The draft IMO MASS Code requires autonomous and remotely supervised maritime vessels to detect departures from their operational design domain, enter a predefined fallback that notifies the operator, permit immediate human override, and avoid changing the voyage plan with... | https://arxiv.org/abs/2512.24470 | Academic Papers | svg |
7f7758dff4156a9b038d93ba229b590086d1e0cf523aec628c681a95f7960d32 | 2026-01-01T00:00:00-05:00 | F2IDiff: Real-world Image Super-resolution using Feature to Image Diffusion Foundation Model | arXiv:2512.24473v1 Announce Type: new Abstract: With the advent of Generative AI, Single Image Super-Resolution (SISR) quality has seen substantial improvement, as the strong priors learned by Text-2-Image Diffusion (T2IDiff) Foundation Models (FM) can bridge the gap between High-Resolution (HR) and Low-Resolution (LR)... | https://arxiv.org/abs/2512.24473 | Academic Papers | svg |
3a4c5032776330f12a1577655ea36d1c8fa94dde79cd42e4725bcdd078a58a5e | 2026-01-01T00:00:00-05:00 | HOLOGRAPH: Active Causal Discovery via Sheaf-Theoretic Alignment of Large Language Model Priors | arXiv:2512.24478v1 Announce Type: new Abstract: Causal discovery from observational data remains fundamentally limited by identifiability constraints. Recent work has explored leveraging Large Language Models (LLMs) as sources of prior causal knowledge, but existing approaches rely on heuristic integration that lacks t... | https://arxiv.org/abs/2512.24478 | Academic Papers | svg |
4a409a65f1c821c567dbbdbf9d7e6b6d0e0e102c1862d1b39431364883ca5b91 | 2026-01-01T00:00:00-05:00 | Design of Linear Residual Generators for Combined Fault Detection and Estimation in Nonlinear Systems | arXiv:2512.24484v1 Announce Type: new Abstract: A systematic method for the design of linear residual generators for combined fault detection and estimation in nonlinear systems is developed. The proposed residual generator is a linear functional observer built for an extended system that incorporates the fault dynamic... | https://arxiv.org/abs/2512.24484 | Academic Papers | svg |
450babaaa8c6a82713019df0f4da90de90273f7557cc1bedbc8a9e508c4a4b0f | 2026-01-01T00:00:00-05:00 | Networked Markets, Fragmented Data: Adaptive Graph Learning for Customer Risk Analytics and Policy Design | arXiv:2512.24487v1 Announce Type: new Abstract: Financial institutions face escalating challenges in identifying high-risk customer behaviors within massive transaction networks, where fraudulent activities exploit market fragmentation and institutional boundaries. We address three fundamental problems in customer risk... | https://arxiv.org/abs/2512.24487 | Academic Papers | svg |
2478c195db16c1755bc085938f61cd0f0f107f4db8f4449028790d3a733c7149 | 2026-01-01T00:00:00-05:00 | Energy-Aware Bayesian Control Barrier Functions for Physics-Informed Gaussian Process Dynamics | arXiv:2512.24493v1 Announce Type: new Abstract: We study safe control for dynamical systems whose continuous-time dynamics are learned with Gaussian processes (GPs), focusing on mechanical and port-Hamiltonian systems where safety is naturally expressed via energy constraints. The availability of a GP Hamiltonian poste... | https://arxiv.org/abs/2512.24493 | Academic Papers | svg |
222dbc7ebb72bae0743d71ddd093210f3a8ef69795d99554f9a9547973990544 | 2026-01-01T00:00:00-05:00 | What Drives Success in Physical Planning with Joint-Embedding Predictive World Models? | arXiv:2512.24497v1 Announce Type: new Abstract: A long-standing challenge in AI is to develop agents capable of solving a wide range of physical tasks and generalizing to new, unseen tasks and environments. A popular recent approach involves training a world model from state-action trajectories and subsequently use it ... | https://arxiv.org/abs/2512.24497 | Academic Papers | svg |
bd628a2aaa0d4ebbae4dff7388ee3ee1514377e4cf154a2ae7a5e14623194720 | 2026-01-01T00:00:00-05:00 | Open Horn Type Theory | arXiv:2512.24498v1 Announce Type: new Abstract: We introduce Open Horn Type Theory (OHTT), an extension of dependent type theory with two primitive judgment forms: coherence and gap, subject to a mutual exclusion law. Unlike classical or intuitionistic negation, gap is not defined via implication but is a primitive wit... | https://arxiv.org/abs/2512.24498 | Academic Papers | svg |
63521164cf74e1d7dfa1eea800bad0354f6fa671c17d3825a77751f73bc21544 | 2026-01-01T00:00:00-05:00 | Training-Free Color-Aware Adversarial Diffusion Sanitization for Diffusion Stegomalware Defense at Security Gateways | arXiv:2512.24499v1 Announce Type: new Abstract: The rapid expansion of generative AI has normalized large-scale synthetic media creation, enabling new forms of covert communication. Recent generative steganography methods, particularly those based on diffusion models, can embed high-capacity payloads without fine-tunin... | https://arxiv.org/abs/2512.24499 | Academic Papers | svg |
c5a4a039c357fb5e206e3d4202030ec656ba23a07a5289c7ada34c78a1717ee6 | 2026-01-01T00:00:00-05:00 | Can Small Training Runs Reliably Guide Data Curation? Rethinking Proxy-Model Practice | arXiv:2512.24503v1 Announce Type: new Abstract: Data teams at frontier AI companies routinely train small proxy models to make critical decisions about pretraining data recipes for full-scale training runs. However, the community has a limited understanding of whether and when conclusions drawn from small-scale experim... | https://arxiv.org/abs/2512.24503 | Academic Papers | svg |
e22a7330ffe1143f381e7b084323b692de74b77bcd26d6716b915514f7a94a75 | 2026-01-01T00:00:00-05:00 | Thinking on Maps: How Foundation Model Agents Explore, Remember, and Reason Map Environments | arXiv:2512.24504v1 Announce Type: new Abstract: Map environments provide a fundamental medium for representing spatial structure. Understanding how foundation model (FM) agents understand and act in such environments is therefore critical for enabling reliable map-based reasoning and applications. However, most existin... | https://arxiv.org/abs/2512.24504 | Academic Papers | svg |
814737511e9228e5aa79544452e411fcbb5a8c011d0e2c741a24ae1ccb597c99 | 2026-01-01T00:00:00-05:00 | Evaluating the Reasoning Abilities of LLMs on Underrepresented Mathematics Competition Problems | arXiv:2512.24505v1 Announce Type: new Abstract: Understanding the limitations of Large Language Models, or LLMs, in mathematical reasoning has been the focus of several recent studies. However, the majority of these studies use the same datasets for benchmarking, which limits the generalizability of their findings and ... | https://arxiv.org/abs/2512.24505 | Academic Papers | svg |
6a8d72f85261bbe86e2270cad7bf3279024915b08a789d027c0a1a2d86f064f0 | 2026-01-01T00:00:00-05:00 | Generalising E-prop to Deep Networks | arXiv:2512.24506v1 Announce Type: new Abstract: Recurrent networks are typically trained with backpropagation through time (BPTT). However, BPTT requires storing the history of all states in the network and then replaying them sequentially backwards in time. This computation appears extremely implausible for the brain ... | https://arxiv.org/abs/2512.24506 | Academic Papers | svg |
da0f397b9f9eb5e3a41faefd3ca68ed7c861140ea136a41d03de518973d927c3 | 2026-01-01T00:00:00-05:00 | Understanding LLM Checkpoint/Restore I/O Strategies and Patterns | arXiv:2512.24511v1 Announce Type: new Abstract: As LLMs and foundation models scale, checkpoint/restore has become a critical pattern for training and inference. With 3D parallelism (tensor, pipeline, data), checkpointing involves many processes, each managing numerous tensors of varying shapes and sizes, that must be ... | https://arxiv.org/abs/2512.24511 | Academic Papers | svg |
bf68d64356fadf0dc2814d4a17b7e6529107b26aa1ef49a5aa767fe7c9f23ae2 | 2026-01-01T00:00:00-05:00 | From Static to Dynamic: Evaluating the Perceptual Impact of Dynamic Elements in Urban Scenes Using Generative Inpainting | arXiv:2512.24513v1 Announce Type: new Abstract: Understanding urban perception from street view imagery has become a central topic in urban analytics and human centered urban design. However, most existing studies treat urban scenes as static and largely ignore the role of dynamic elements such as pedestrians and vehic... | https://arxiv.org/abs/2512.24513 | Academic Papers | svg |
4eabb87866704503ab9c3b7dc1e9fd193f0d087bd00abac2dd044ceb1feef1fa | 2026-01-01T00:00:00-05:00 | Paragraph Segmentation Revisited: Towards a Standard Task for Structuring Speech | arXiv:2512.24517v1 Announce Type: new Abstract: Automatic speech transcripts are often delivered as unstructured word streams that impede readability and repurposing. We recast paragraph segmentation as the missing structuring step and fill three gaps at the intersection of speech processing and text segmentation. Firs... | https://arxiv.org/abs/2512.24517 | Academic Papers | svg |
cd1273543e1b603a1ec2847542c994c881fc3ef284f2bbdbc103fd85dc3a2f14 | 2026-01-01T00:00:00-05:00 | Using Large Language Models To Translate Machine Results To Human Results | arXiv:2512.24518v1 Announce Type: new Abstract: Artificial intelligence (AI) has transformed medical imaging, with computer vision (CV) systems achieving state-of-the-art performance in classification and detection tasks. However, these systems typically output structured predictions, leaving radiologists responsible f... | https://arxiv.org/abs/2512.24518 | Academic Papers | svg |
6c329a9ee0f9d6c0314341fc552d8e631ef25ddaf47d1de6922f6b9388d2915c | 2026-01-01T00:00:00-05:00 | Analyzing Airline Alliances through Multi-Attribute Graph Partitioning to Maximize Competition and Market Penetration Capability | arXiv:2512.24519v1 Announce Type: new Abstract: The air transportation market is highly competitive and dynamic. Airlines often form alliances to expand their network reach, improve operational efficiency, and enhance customer experience. However, the impact of these alliances on market competition and operational effi... | https://arxiv.org/abs/2512.24519 | Academic Papers | svg |
161c0fbff1d743420828bdacc32d3ea2cf8eca5a4ae209856e0964e5b2a521b2 | 2026-01-01T00:00:00-05:00 | Exponential Convergence of Deep Composite Polynomial Approximation for Cusp-Type Functions | arXiv:2512.24523v1 Announce Type: new Abstract: We investigate deep composite polynomial approximations of continuous but non-differentiable functions with algebraic cusp singularities. The functions in focus consist of finitely many cusp terms of the form $|x-a_j|^{\alpha_j}$ with rational exponents $\alpha_j\in(0,1)$... | https://arxiv.org/abs/2512.24523 | Academic Papers | svg |
87d43b53161810cf95bedcb0cef3a113ff7219552a89a3ddbb37bb79f604459e | 2026-01-01T00:00:00-05:00 | A Magnified View into Heterogeneous-ISA Thread Migration Performance without State Transformation | arXiv:2512.24530v1 Announce Type: new Abstract: Heterogeneous-ISA processor designs have attracted considerable research interest. However, unlike their homogeneous-ISA counterparts, explicit software support for bridging ISA heterogeneity is required. The lack of a compilation toolchain ready to support heterogeneous-... | https://arxiv.org/abs/2512.24530 | Academic Papers | svg |
bfa8fc1bbe9741f4fa23d164aeb630ba58c7365445883c4c8422e652be7cea69 | 2026-01-01T00:00:00-05:00 | Correctness of Extended RSA Public Key Cryptosystem | arXiv:2512.24531v1 Announce Type: new Abstract: This paper proposes an alternative approach to formally establishing the correctness of the RSA public key cryptosystem. The methodology presented herein deviates slightly from conventional proofs found in existing literature. Specifically, this study explores the conditi... | https://arxiv.org/abs/2512.24531 | Academic Papers | svg |
99a2bb89f22207297822fa5a658017818693835d795cc8c3a93cc1a2cdb2a3a4 | 2026-01-01T00:00:00-05:00 | From Building Blocks to Planning: Multi-Step Spatial Reasoning in LLMs with Reinforcement Learning | arXiv:2512.24532v1 Announce Type: new Abstract: Spatial reasoning in large language models (LLMs) has gained increasing attention due to applications in navigation and planning. Despite strong general language capabilities, LLMs still struggle with spatial transformations and multi-step planning in structured environme... | https://arxiv.org/abs/2512.24532 | Academic Papers | svg |
c88e82173021f5be9f60c403e9a71eb6fe2ed8e43b60fb51f14c92486507eec4 | 2026-01-01T00:00:00-05:00 | A Graph Neural Network with Auxiliary Task Learning for Missing PMU Data Reconstruction | arXiv:2512.24542v1 Announce Type: new Abstract: In wide-area measurement systems (WAMS), phasor measurement unit (PMU) measurement is prone to data missingness due to hardware failures, communication delays, and cyber-attacks. Existing data-driven methods are limited by inadaptability to concept drift in power systems,... | https://arxiv.org/abs/2512.24542 | Academic Papers | svg |
0b50d5f3da78674c4ab2ad819a6fa0ac56e31eeacd42baf0b36dc6b9c88ebe6c | 2026-01-01T00:00:00-05:00 | More Than Bits: Multi-Envelope Double Binary Factorization for Extreme Quantization | arXiv:2512.24545v1 Announce Type: new Abstract: For extreme low-bit quantization of large language models (LLMs), Double Binary Factorization (DBF) is attractive as it enables efficient inference without sacrificing accuracy. However, the scaling parameters of DBF are too restrictive; after factoring out signs, all ran... | https://arxiv.org/abs/2512.24545 | Academic Papers | svg |
df4bd1e2683c61f2df310a04afcc28c7137fd5b659d27a35726c8afee76fb844 | 2026-01-01T00:00:00-05:00 | Hierarchical Vector-Quantized Latents for Perceptual Low-Resolution Video Compression | arXiv:2512.24547v1 Announce Type: new Abstract: The exponential growth of video traffic has placed increasing demands on bandwidth and storage infrastructure, particularly for content delivery networks (CDNs) and edge devices. While traditional video codecs like H.264 and HEVC achieve high compression ratios, they are ... | https://arxiv.org/abs/2512.24547 | Academic Papers | svg |
c6cd9e252db1ca92c8615c6590e91e3508441a607efc94e9e08b62301d8a8deb | 2026-01-01T00:00:00-05:00 | DISF: Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning with Grasp Pose Alignment to the Object Center of Mass | arXiv:2512.24550v1 Announce Type: new Abstract: In this work, we address the limitation of surface fitting-based grasp planning algorithm, which primarily focuses on geometric alignment between the gripper and object surface while overlooking the stability of contact point distribution, often resulting in unstable gras... | https://arxiv.org/abs/2512.24550 | Academic Papers | svg |
36e869e83d4ea3efd45d713c788bab41c51db64222aad8ee6704466dad3cb079 | 2026-01-01T00:00:00-05:00 | PhyGDPO: Physics-Aware Groupwise Direct Preference Optimization for Physically Consistent Text-to-Video Generation | arXiv:2512.24551v1 Announce Type: new Abstract: Recent advances in text-to-video (T2V) generation have achieved good visual quality, yet synthesizing videos that faithfully follow physical laws remains an open challenge. Existing methods mainly based on graphics or prompt extension struggle to generalize beyond simple ... | https://arxiv.org/abs/2512.24551 | Academic Papers | svg |
56c8283edec61ff9fc9b93870ee3e59abeaf911932c635120f0394ea5bb4f60c | 2026-01-01T00:00:00-05:00 | OCP-LS: An Efficient Algorithm for Visual Localization | arXiv:2512.24552v1 Announce Type: new Abstract: This paper proposes a novel second-order optimization algorithm. It aims to address large-scale optimization problems in deep learning because it incorporates the OCP method and appropriately approximating the diagonal elements of the Hessian matrix. Extensive experiments... | https://arxiv.org/abs/2512.24552 | Academic Papers | svg |
00198359bad89623c0e296d573f7dd733ed56fca54ef76101854109e3a2ca926 | 2026-01-01T00:00:00-05:00 | From Perception to Punchline: Empowering VLM with the Art of In-the-wild Meme | arXiv:2512.24555v1 Announce Type: new Abstract: Generating humorous memes is a challenging multimodal task that moves beyond direct image-to-caption supervision. It requires a nuanced reasoning over visual content, contextual cues, and subjective humor. To bridge this gap between visual perception and humorous punchlin... | https://arxiv.org/abs/2512.24555 | Academic Papers | svg |
076f371ba1ec986d3dff0881f4da0c5c6f439032c4c7cb5b028d0362581d7b4b | 2026-01-01T00:00:00-05:00 | Safe in the Future, Dangerous in the Past: Dissecting Temporal and Linguistic Vulnerabilities in LLMs | arXiv:2512.24556v1 Announce Type: new Abstract: As Large Language Models (LLMs) integrate into critical global infrastructure, the assumption that safety alignment transfers zero-shot from English to other languages remains a dangerous blind spot. This study presents a systematic audit of three state of the art models ... | https://arxiv.org/abs/2512.24556 | Academic Papers | svg |
0f3eda63aef4c32d4deddc803bf346a4d5e7937b0ec9a99495e33a4ae995889c | 2026-01-01T00:00:00-05:00 | Evolutionary Discovery of Sequence Acceleration Methods for Slab Geometry Neutron Transport | arXiv:2512.24559v1 Announce Type: new Abstract: We present a genetic programming approach to automatically discover convergence acceleration methods for discrete ordinates solutions of neutron transport problems in slab geometry. Classical acceleration methods such as Aitken's delta-squared and Wynn epsilon assume spec... | https://arxiv.org/abs/2512.24559 | Academic Papers | svg |
30b3531c8cff7a312e13e08ff0e297b8c5225d407704cdabbf64e6c64a3f0b04 | 2026-01-01T00:00:00-05:00 | Localized Calibrated Uncertainty in Code Language Models | arXiv:2512.24560v1 Announce Type: new Abstract: Large Language models (LLMs) can generate complicated source code from natural language prompts. However, LLMs can generate output that deviates from what the user wants, requiring supervision and editing. To support this process, we offer techniques to localize where gen... | https://arxiv.org/abs/2512.24560 | Academic Papers | svg |
f5e2b519c2dd29d9453edb23aee4bca3896be951018a91300cb98fbd3a2e5c32 | 2026-01-01T00:00:00-05:00 | RGBT-Ground Benchmark: Visual Grounding Beyond RGB in Complex Real-World Scenarios | arXiv:2512.24561v1 Announce Type: new Abstract: Visual Grounding (VG) aims to localize specific objects in an image according to natural language expressions, serving as a fundamental task in vision-language understanding. However, existing VG benchmarks are mostly derived from datasets collected under clean environmen... | https://arxiv.org/abs/2512.24561 | Academic Papers | svg |
9fa4ebac669931451cd7beca4de3e3b3898d26ae83b239db049b1091e186c836 | 2026-01-01T00:00:00-05:00 | HaluNet: Multi-Granular Uncertainty Modeling for Efficient Hallucination Detection in LLM Question Answering | arXiv:2512.24562v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at question answering (QA) but often generate hallucinations, including factual errors or fabricated content. Detecting hallucinations from internal uncertainty signals is attractive due to its scalability and independence from external ... | https://arxiv.org/abs/2512.24562 | Academic Papers | svg |
a4649c4a1154908b8b2c5c40dc0a6ddf630070c0ca4b475d6b288ed9d332aab3 | 2026-01-01T00:00:00-05:00 | CPR: Causal Physiological Representation Learning for Robust ECG Analysis under Distribution Shifts | arXiv:2512.24564v1 Announce Type: new Abstract: Deep learning models for Electrocardiogram (ECG) diagnosis have achieved remarkable accuracy but exhibit fragility against adversarial perturbations, particularly Smooth Adversarial Perturbations (SAP) that mimic biological morphology. Existing defenses face a critical di... | https://arxiv.org/abs/2512.24564 | Academic Papers | svg |
7dd9979ec136b68eca8b0e8f370b3ca9dceba5c645e2cba9a731df183bad930b | 2026-01-01T00:00:00-05:00 | MCPAgentBench: A Real-world Task Benchmark for Evaluating LLM Agent MCP Tool Use | arXiv:2512.24565v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol (MCP) is considered a future trend. Current MCP evaluation sets suffer from issues such as reliance on external MCP services ... | https://arxiv.org/abs/2512.24565 | Academic Papers | svg |
16ef3197c1596b7199f70288dccb330eab59af351a91ad67fef269c7c0de9373 | 2026-01-01T00:00:00-05:00 | Newton-Krylov Methods for Computing Steady States of Particle Timesteppers via Optimal Transport | arXiv:2512.24567v1 Announce Type: new Abstract: Timesteppers constitute a powerful tool in modern computational science and engineering. Although they are typically used to advance the system forward in time, they can also be viewed as nonlinear mappings that implicitly encode steady states and stability information. I... | https://arxiv.org/abs/2512.24567 | Academic Papers | svg |
2ed4eebfc45cc47ca3bc2420d51dc1e783b5f5d1144c5fe1920b2f11631a44bf | 2026-01-01T00:00:00-05:00 | On the Effectiveness of Training Data Optimization for LLM-based Code Generation: An Empirical Study | arXiv:2512.24570v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable progress in code generation, largely driven by the availability of high-quality code datasets for effective training. To further improve data quality, numerous training data optimization techniques have been proposed; ... | https://arxiv.org/abs/2512.24570 | Academic Papers | svg |
1a549207b8d7398a402959820f7c9f94ead122b6eaf355403477e50d92a459e6 | 2026-01-01T00:00:00-05:00 | SynRAG: A Large Language Model Framework for Executable Query Generation in Heterogeneous SIEM System | arXiv:2512.24571v1 Announce Type: new Abstract: Security Information and Event Management (SIEM) systems are essential for large enterprises to monitor their IT infrastructure by ingesting and analyzing millions of logs and events daily. Security Operations Center (SOC) analysts are tasked with monitoring and analyzing... | https://arxiv.org/abs/2512.24571 | Academic Papers | svg |
f78769e7e36ded98955e59b30a5899b26f10c4013883ab381251be8a6b0498ae | 2026-01-01T00:00:00-05:00 | Korean Canonical Legal Benchmark: Toward Knowledge-Independent Evaluation of LLMs' Legal Reasoning Capabilities | arXiv:2512.24572v1 Announce Type: new Abstract: We introduce the Korean Canonical Legal Benchmark (KCL), a benchmark designed to assess language models' legal reasoning capabilities independently of domain-specific knowledge. KCL provides question-level supporting precedents, enabling a more faithful disentanglement of... | https://arxiv.org/abs/2512.24572 | Academic Papers | svg |
a48f035a1eb7de74039b832fa0ffc73ef02d142b7c4f64946c7534fcc75f9234 | 2026-01-01T00:00:00-05:00 | Understanding and Steering the Cognitive Behaviors of Reasoning Models at Test-Time | arXiv:2512.24574v1 Announce Type: new Abstract: Large Language Models (LLMs) often rely on long chain-of-thought (CoT) reasoning to solve complex tasks. While effective, these trajectories are frequently inefficient, leading to high latency from excessive token generation, or unstable reasoning that alternates between ... | https://arxiv.org/abs/2512.24574 | Academic Papers | svg |
a94ddd0cc8aebf9a3eb005e8c0a672fd163047d49ae4c87777a2b55915064b3e | 2026-01-01T00:00:00-05:00 | Improving Few-Shot Change Detection Visual Question Answering via Decision-Ambiguity-guided Reinforcement Fine-Tuning | arXiv:2512.24591v1 Announce Type: new Abstract: Change detection visual question answering (CDVQA) requires answering text queries by reasoning about semantic changes in bi-temporal remote sensing images. A straightforward approach is to boost CDVQA performance with generic vision-language models via supervised fine-tu... | https://arxiv.org/abs/2512.24591 | Academic Papers | svg |
1db635657d8faaa6dff35a46f9d0f7d0317ab4898c109b011d824c3a76521926 | 2026-01-01T00:00:00-05:00 | SliceLens: Fine-Grained and Grounded Error Slice Discovery for Multi-Instance Vision Tasks | arXiv:2512.24592v1 Announce Type: new Abstract: Systematic failures of computer vision models on subsets with coherent visual patterns, known as error slices, pose a critical challenge for robust model evaluation. Existing slice discovery methods are primarily developed for image classification, limiting their applicab... | https://arxiv.org/abs/2512.24592 | Academic Papers | svg |
cb129240a6e43cd306b0ad18178922124e826391bd53385ae95cb11b1b5cd01c | 2026-01-01T00:00:00-05:00 | 3D Semantic Segmentation for Post-Disaster Assessment | arXiv:2512.24593v1 Announce Type: new Abstract: The increasing frequency of natural disasters poses severe threats to human lives and leads to substantial economic losses. While 3D semantic segmentation is crucial for post-disaster assessment, existing deep learning models lack datasets specifically designed for post-d... | https://arxiv.org/abs/2512.24593 | Academic Papers | svg |
4981dd163360edfae935c43e9c3113e639c2744613f290485f0f22485f01f992 | 2026-01-01T00:00:00-05:00 | A Tale of 1001 LoC: Potential Runtime Error-Guided Specification Synthesis for Verifying Large-Scale Programs | arXiv:2512.24594v1 Announce Type: new Abstract: Fully automated verification of large-scale software and hardware systems is arguably the holy grail of formal methods. Large language models (LLMs) have recently demonstrated their potential for enhancing the degree of automation in formal verification by, e.g., generati... | https://arxiv.org/abs/2512.24594 | Academic Papers | svg |
f7ccb39fbcb0160c051292a27a759b4772fe6fd7e864d6d39a98cf729300f4e7 | 2026-01-01T00:00:00-05:00 | Recursive Language Models | arXiv:2512.24601v1 Announce Type: new Abstract: We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference strategy that treats long prompts as part of an external environment and allows ... | https://arxiv.org/abs/2512.24601 | Academic Papers | svg |
21f3fc5c5b37663117ff3a8e4cfaa66b05bb30072e6dc251286e6d07c4ef76a6 | 2026-01-01T00:00:00-05:00 | Secure Digital Semantic Communications: Fundamentals, Challenges, and Opportunities | arXiv:2512.24602v1 Announce Type: new Abstract: Semantic communication (SemCom) has emerged as a promising paradigm for future wireless networks by prioritizing task-relevant meaning over raw data delivery, thereby reducing communication overhead and improving efficiency. However, shifting from bit-accurate transmissio... | https://arxiv.org/abs/2512.24602 | Academic Papers | svg |
0ea1bb32eed18b10000871d23a0ff830efb472c1afd98070c50f92616a64cc4a | 2026-01-01T00:00:00-05:00 | Collaborative Low-Rank Adaptation for Pre-Trained Vision Transformers | arXiv:2512.24603v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) has achieved remarkable success in fine-tuning pre-trained vision transformers for various downstream tasks. Existing studies mainly focus on exploring more parameter-efficient strategies or more effective representation learning schemes. Howeve... | https://arxiv.org/abs/2512.24603 | Academic Papers | svg |
90deffcc32f85eab3a8bc1e07e3e3dfd3ecdb227c00f6cdc3f31b583d8decc0e | 2026-01-01T00:00:00-05:00 | MoniRefer: A Real-world Large-scale Multi-modal Dataset based on Roadside Infrastructure for 3D Visual Grounding | arXiv:2512.24605v1 Announce Type: new Abstract: 3D visual grounding aims to localize the object in 3D point cloud scenes that semantically corresponds to given natural language sentences. It is very critical for roadside infrastructure system to interpret natural languages and localize relevant target objects in comple... | https://arxiv.org/abs/2512.24605 | Academic Papers | svg |
91fdc08957a453851c2f92228f75185cf49478251cfeacebf19b9b44116cbab4 | 2026-01-01T00:00:00-05:00 | Reinforcement Learning-Augmented LLM Agents for Collaborative Decision Making and Performance Optimization | arXiv:2512.24609v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well in language tasks but often lack collaborative awareness and struggle to optimize global performance in multi-agent settings. We present a reinforcement learning-augmented LLM agent framework that formulates cooperation as a decen... | https://arxiv.org/abs/2512.24609 | Academic Papers | svg |
023df41aea070a7dfa85c457bb6ac1890260cb384b653a0f1f0dfef52bafe9c2 | 2026-01-01T00:00:00-05:00 | Group Deliberation Oriented Multi-Agent Conversational Model for Complex Reasoning | arXiv:2512.24613v1 Announce Type: new Abstract: This paper proposes a group deliberation oriented multi-agent conversational model to address the limitations of single large language models in complex reasoning tasks. The model adopts a three-level role division architecture consisting of generation, verification, and ... | https://arxiv.org/abs/2512.24613 | Academic Papers | svg |
11cf2af73b51acf60d81f2c2a587720a7b0e8971f4ef7510c7c5f36c02022b12 | 2026-01-01T00:00:00-05:00 | Chat-Driven Optimal Management for Virtual Network Services | arXiv:2512.24614v1 Announce Type: new Abstract: This paper proposes a chat-driven network management framework that integrates natural language processing (NLP) with optimization-based virtual network allocation, enabling intuitive and reliable reconfiguration of virtual network services. Conventional intent-based netw... | https://arxiv.org/abs/2512.24614 | Academic Papers | svg |
5f03fc02d8be0cbc5f83d46acfcdf14291c40acd82649ee30e430a57db173f7e | 2026-01-01T00:00:00-05:00 | Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization | arXiv:2512.24615v1 Announce Type: new Abstract: Existing Large Language Model (LLM) agent frameworks face two significant challenges: high configuration costs and static capabilities. Building a high-quality agent often requires extensive manual effort in tool integration and prompt engineering, while deployed agents s... | https://arxiv.org/abs/2512.24615 | Academic Papers | svg |
f89aff2ec8721da0ea45682a4af409e8c1c78a41d87fdae987193a2d1bcce071 | 2026-01-01T00:00:00-05:00 | Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space | arXiv:2512.24617v1 Announce Type: new Abstract: Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity on locally predictable spans while under-allocating computation to semantically critical tra... | https://arxiv.org/abs/2512.24617 | Academic Papers | svg |
83427afe1bbbfdd9071ec49bb3f48f0ed9022062cde665b2e6a1af52c277c28e | 2026-01-01T00:00:00-05:00 | Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models | arXiv:2512.24618v1 Announce Type: new Abstract: We introduce Youtu-LLM, a lightweight yet powerful language model that harmonizes high computational efficiency with native agentic intelligence. Unlike typical small models that rely on distillation, Youtu-LLM (1.96B) is pre-trained from scratch to systematically cultiva... | https://arxiv.org/abs/2512.24618 | Academic Papers | svg |
15e6441918a5b6815f88407e91bfe98a6f5a2d1e4f0f90b6c9da9d4800aecf6e | 2026-01-01T00:00:00-05:00 | Decentralized No-Regret Frequency-Time Scheduling for FMCW Radar Interference Avoidance | arXiv:2512.24619v1 Announce Type: new Abstract: Automotive FMCW radars are indispensable to modern ADAS and autonomous-driving systems, but their increasing density has intensified the risk of mutual interference. Existing mitigation techniques, including reactive receiver-side suppression, proactive waveform design, a... | https://arxiv.org/abs/2512.24619 | Academic Papers | svg |
76ca9a5f61e231f3a23a579fdc6e3a097983de8752abe218902e7a0d2b4d0651 | 2026-01-01T00:00:00-05:00 | LLHA-Net: A Hierarchical Attention Network for Two-View Correspondence Learning | arXiv:2512.24620v1 Announce Type: new Abstract: Establishing the correct correspondence of feature points is a fundamental task in computer vision. However, the presence of numerous outliers among the feature points can significantly affect the matching results, reducing the accuracy and robustness of the process. Furt... | https://arxiv.org/abs/2512.24620 | Academic Papers | svg |
8893ec8a3787f8f8be1528f1f899e1a07524d66d800d3c2544a5f07ede9e0978 | 2026-01-01T00:00:00-05:00 | FireRescue: A UAV-Based Dataset and Enhanced YOLO Model for Object Detection in Fire Rescue Scenes | arXiv:2512.24622v1 Announce Type: new Abstract: Object detection in fire rescue scenarios is importance for command and decision-making in firefighting operations. However, existing research still suffers from two main limitations. First, current work predominantly focuses on environments such as mountainous or forest ... | https://arxiv.org/abs/2512.24622 | Academic Papers | svg |
536e0772c1a4bcd5720aa9f7ae7c4919f5d6a3843317af639f446fea0597359a | 2026-01-01T00:00:00-05:00 | AutoFed: Manual-Free Federated Traffic Prediction via Personalized Prompt | arXiv:2512.24625v1 Announce Type: new Abstract: Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle fleet management. However, due to significant privacy concerns surrounding traffic data, most existing methods rely on local training,... | https://arxiv.org/abs/2512.24625 | Academic Papers | svg |
3587acb903becad477da857d76597456724d37175e8ccb5202eec4624fb7c5ff | 2026-01-01T00:00:00-05:00 | AI-Driven Acoustic Voice Biomarker-Based Hierarchical Classification of Benign Laryngeal Voice Disorders from Sustained Vowels | arXiv:2512.24628v1 Announce Type: new Abstract: Benign laryngeal voice disorders affect nearly one in five individuals and often manifest as dysphonia, while also serving as non-invasive indicators of broader physiological dysfunction. We introduce a clinically inspired hierarchical machine learning framework for autom... | https://arxiv.org/abs/2512.24628 | Academic Papers | svg |
5945cdf76390b8207348d24e0f0b7777d2f3e3973ab5252ab4adc81a86d21870 | 2026-01-01T00:00:00-05:00 | How Do Agentic AI Systems Address Performance Optimizations? A BERTopic-Based Analysis of Pull Requests | arXiv:2512.24630v1 Announce Type: new Abstract: LLM-based software engineering is influencing modern software development. In addition to correctness, prior studies have also examined the performance of software artifacts generated by AI agents. However, it is unclear how exactly the agentic AI systems address performa... | https://arxiv.org/abs/2512.24630 | Academic Papers | svg |
e0c4279efaf1ffcdbcfb9c8e18bd895c442cc9914f8581de9385ba8df2894e48 | 2026-01-01T00:00:00-05:00 | ReflecToMeet: An AI-Assisted Reflection Based System to Enhance Collaborative Preparedness | arXiv:2512.24632v1 Announce Type: new Abstract: In collaborative settings, difficulties in sustaining a consistent pace and engagement often lead to task drift, reducing preparedness and overall effectiveness between meetings. To address this challenge, we conducted a formative study and developed ReflecToMeet, an AI a... | https://arxiv.org/abs/2512.24632 | Academic Papers | svg |
b3fc1a7f5176ff72d06243aa0768c24723bfcda1df901ebdd324727dbade3275 | 2026-01-01T00:00:00-05:00 | DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information | arXiv:2512.24635v1 Announce Type: new Abstract: Automated Program Repair (APR) aims to automatically generate correct patches for buggy programs. Recent approaches leveraging large language models (LLMs) have shown promise but face limitations. Most rely solely on static analysis, ignoring runtime behaviors. Some attem... | https://arxiv.org/abs/2512.24635 | Academic Papers | svg |
fed5e6c60b0d687d8b48ff0914e60e75be3f4baa0ba4bd9496bc48283a5da50a | 2026-01-01T00:00:00-05:00 | How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study | arXiv:2512.24636v1 Announce Type: new Abstract: As Software Engineering enters its new era (SE 3.0), AI coding agents increasingly automate software development workflows. However, it remains unclear how exactly these agents recognize and address software energy concerns-an issue growing in importance due to large-scal... | https://arxiv.org/abs/2512.24636 | Academic Papers | svg |
aa66bf438289280857a6d39cb3f8ab834b1ab961480a79ceb8f36bb3b683b324 | 2026-01-01T00:00:00-05:00 | MSched: GPU Multitasking via Proactive Memory Scheduling | arXiv:2512.24637v1 Announce Type: new Abstract: The limited HBM capacity has become the primary bottleneck for hosting an increasing number of larger-scale GPU tasks. While demand paging extends capacity via host DRAM, it incurs up to 78x slowdown due to the massive working sets and poor locality of GPU workloads. We o... | https://arxiv.org/abs/2512.24637 | Academic Papers | svg |
2f450b49b2ec60ef04edf6d16d2e841062e13eae33b7b2c6307fd04f90c93319 | 2026-01-01T00:00:00-05:00 | Resolving State Ambiguity in Robot Manipulation via Adaptive Working Memory Recoding | arXiv:2512.24638v1 Announce Type: new Abstract: State ambiguity is common in robotic manipulation. Identical observations may correspond to multiple valid behavior trajectories. The visuomotor policy must correctly extract the appropriate types and levels of information from the history to identify the current task pha... | https://arxiv.org/abs/2512.24638 | Academic Papers | svg |
231fe03dc42f1c6949fd948a071464bc942bf88ccf5c4b4739c72e62eb9eaad7 | 2026-01-01T00:00:00-05:00 | From Sequential to Spatial: Reordering Autoregression for Efficient Visual Generation | arXiv:2512.24639v1 Announce Type: new Abstract: Inspired by the remarkable success of autoregressive models in language modeling, this paradigm has been widely adopted in visual generation. However, the sequential token-by-token decoding mechanism inherent in traditional autoregressive models leads to low inference eff... | https://arxiv.org/abs/2512.24639 | Academic Papers | svg |
d5343188f3bab2cc89558950cefd148ea0c9e3609dc24a25d94e85705ce01ead | 2026-01-01T00:00:00-05:00 | A Scalable Framework for logP Prediction: From Terabyte-Scale Data Integration to Interpretable Ensemble Modeling | arXiv:2512.24643v1 Announce Type: new Abstract: This study presents a large-scale predictive modeling framework for logP prediction using 426850 bioactive compounds rigorously curated from the intersection of three authoritative chemical databases: PubChem, ChEMBL, and eMolecules. We developed a novel computational inf... | https://arxiv.org/abs/2512.24643 | Academic Papers | svg |
a14553692902a00de3d025eac7cb08fb26e0e0f060e4ca22259bc0e8994802bc | 2026-01-01T00:00:00-05:00 | AudioFab: Building A General and Intelligent Audio Factory through Tool Learning | arXiv:2512.24645v1 Announce Type: new Abstract: Currently, artificial intelligence is profoundly transforming the audio domain; however, numerous advanced algorithms and tools remain fragmented, lacking a unified and efficient framework to unlock their full potential. Existing audio agent frameworks often suffer from c... | https://arxiv.org/abs/2512.24645 | Academic Papers | svg |
ce2a77c70172128b23aee714212359673b6e52c8fdded48326ebacdc4ec68c78 | 2026-01-01T00:00:00-05:00 | Solving the inverse Source Problems for wave equation with final time measurements by a data driven approach | arXiv:2512.24647v1 Announce Type: new Abstract: This paper develops a discrete data-driven approach for solving the inverse source problem of the wave equation with final time measurements. Focusing on the $L^2$-Tikhonov regularization method, we analyze its convergence under two different noise models, using noisy dis... | https://arxiv.org/abs/2512.24647 | Academic Papers | svg |
4bfa46a0975640a0396d06ae475c9364c372255a5ad831c64e4a10104c47df1b | 2026-01-01T00:00:00-05:00 | Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation | arXiv:2512.24651v1 Announce Type: new Abstract: Autonomous mobile robots operating in complex, dynamic environments face the dual challenge of navigating large-scale, structurally diverse spaces with static obstacles while safely interacting with various moving agents. Traditional graph-based planners excel at long-ran... | https://arxiv.org/abs/2512.24651 | Academic Papers | svg |
6a1350036962139db51aae84b8da780af720785feb26216c39ef89c93b9db7cc | 2026-01-01T00:00:00-05:00 | Practical Traceable Over-Threshold Multi-Party Private Set Intersection | arXiv:2512.24652v1 Announce Type: new Abstract: Multi-Party Private Set Intersection (MP-PSI) with threshold enhances the flexibility of MP-PSI by disclosing elements present in at least $t$ participants' sets, rather than requiring elements to appear in all $n$ sets. In scenarios where each participant is responsible ... | https://arxiv.org/abs/2512.24652 | Academic Papers | svg |
5b7ba8523c88fcaaf906dc2c1530438619902db948846acb44b6d4e456361285 | 2026-01-01T00:00:00-05:00 | RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence | arXiv:2512.24653v1 Announce Type: new Abstract: While data-driven imitation learning has revolutionized robotic manipulation, current approaches remain constrained by the scarcity of large-scale, diverse real-world demonstrations. Consequently, the ability of existing models to generalize across long-horizon bimanual t... | https://arxiv.org/abs/2512.24653 | Academic Papers | svg |
819057c8beb51e2a51f0e58698b2d8d52611efe8d830f9db72f18967e4f95347 | 2026-01-01T00:00:00-05:00 | Characterizing Bugs and Quality Attributes in Quantum Software: A Large-Scale Empirical Study | arXiv:2512.24656v1 Announce Type: new Abstract: Quantum Software Engineering (QSE) is essential for ensuring the reliability and maintainability of hybrid quantum-classical systems, yet empirical evidence on how bugs emerge and affect quality in real-world quantum projects remains limited. This study presents the first... | https://arxiv.org/abs/2512.24656 | Academic Papers | svg |
74ccb8112e3db456bf0f4376371f380ba44d3eb5bdc83eb9a1a2fa1240e85d41 | 2026-01-01T00:00:00-05:00 | Antagonistic Bowden-Cable Actuation of a Lightweight Robotic Hand: Toward Dexterous Manipulation for Payload Constrained Humanoids | arXiv:2512.24657v1 Announce Type: new Abstract: Humanoid robots toward human-level dexterity require robotic hands capable of simultaneously providing high grasping force, rapid actuation speeds, multiple degrees of freedom, and lightweight structures within human-like size constraints. Meeting these conflicting requir... | https://arxiv.org/abs/2512.24657 | Academic Papers | svg |
9bcb5eb41f87989bbf4648b80ee35b249909f099e15c518f2e275aaed5889892 | 2026-01-01T00:00:00-05:00 | Taking Advantage of Rational Canonical Form for Faster Ring-LWE based Encrypted Controller with Recursive Multiplication | arXiv:2512.24658v1 Announce Type: new Abstract: This paper aims to provide an efficient implementation of encrypted linear dynamic controllers that perform recursive multiplications on a Ring-Learning With Errors (Ring-LWE) based cryptosystem. By adopting a system-theoretical approach, we significantly reduce both time... | https://arxiv.org/abs/2512.24658 | Academic Papers | svg |
5219d350ee0982a3d5b6cf34995e4fe38911ce45dc77e90a3594a709ea98323d | 2026-01-01T00:00:00-05:00 | Hierarchical Online Optimization Approach for IRS-enabled Low-altitude MEC in Vehicular Networks | arXiv:2512.24659v1 Announce Type: new Abstract: In this paper, we propose an intelligent reflecting surface (IRS)-enabled low-altitude multi-access edge computing (MEC) architecture, where an aerial MEC server cooperates with a terrestrial MEC server to provide computing services, while hybrid IRSs (i.e., building-inst... | https://arxiv.org/abs/2512.24659 | Academic Papers | svg |
10276df3a1b7814b027be292f59fbfa8cf103665dcc7f0d36f11239797837600 | 2026-01-01T00:00:00-05:00 | Do Large Language Models Know What They Are Capable Of? | arXiv:2512.24661v1 Announce Type: new Abstract: We investigate whether large language models (LLMs) can predict whether they will succeed on a given task and whether their predictions improve as they progress through multi-step tasks. We also investigate whether LLMs can learn from in-context experiences to make better... | https://arxiv.org/abs/2512.24661 | Academic Papers | svg |
1914e38e84e39ff633abc40bdcde459ee739d460a6e93a1dd59ef248cc780ba4 | 2026-01-01T00:00:00-05:00 | Renormalization Group Guided Tensor Network Structure Search | arXiv:2512.24663v1 Announce Type: new Abstract: Tensor network structure search (TN-SS) aims to automatically discover optimal network topologies and rank configurations for efficient tensor decomposition in high-dimensional data representation. Despite recent advances, existing TN-SS methods face significant limitatio... | https://arxiv.org/abs/2512.24663 | Academic Papers | svg |
d2ec5961ede3b419a20906e20f5b9a53161a0b0b7942487a9ba9fa0462238d0f | 2026-01-01T00:00:00-05:00 | HeteroHBA: A Generative Structure-Manipulating Backdoor Attack on Heterogeneous Graphs | arXiv:2512.24665v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have achieved strong performance in many real-world applications, yet targeted backdoor poisoning on heterogeneous graphs remains less studied. We consider backdoor attacks for heterogeneous node classification, where an adversa... | https://arxiv.org/abs/2512.24665 | Academic Papers | svg |
cbe6fc938a7ef141f2006c27bb285891ff33f192f9eb62944c4b72319ff1f433 | 2026-01-01T00:00:00-05:00 | Distributed Bilevel Optimization with Dual Pruning for Resource-limited Clients | arXiv:2512.24667v1 Announce Type: new Abstract: With the development of large-scale models, traditional distributed bilevel optimization algorithms cannot be applied directly in low-resource clients. The key reason lies in the excessive computation involved in optimizing both the lower- and upper-level functions. Thus,... | https://arxiv.org/abs/2512.24667 | Academic Papers | svg |
683256203196c442cab1934e3711514c51e248838186327f7b7ad9888e04a106 | 2026-01-01T00:00:00-05:00 | VLA-RAIL: A Real-Time Asynchronous Inference Linker for VLA Models and Robots | arXiv:2512.24673v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have achieved remarkable breakthroughs in robotics, with the action chunk playing a dominant role in these advances. Given the real-time and continuous nature of robotic motion control, the strategies for fusing a queue of successive ac... | https://arxiv.org/abs/2512.24673 | Academic Papers | svg |
549ec5af6d106764c65c264e56f983218d24027bf77535e8b1f0746194e02f92 | 2026-01-01T00:00:00-05:00 | Multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis under unseen working conditions | arXiv:2512.24679v1 Announce Type: new Abstract: Intelligent fault diagnosis has become an indispensable technique for ensuring machinery reliability. However, existing methods suffer significant performance decline in real-world scenarios where models are tested under unseen working conditions, while domain adaptation ... | https://arxiv.org/abs/2512.24679 | Academic Papers | svg |
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