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9dbe04a5ea62f8de169cc45069a8ea1834c6bd4bbfefbf86c8a742b992ca18c8 | 2026-01-01T00:00:00-05:00 | Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities | arXiv:2504.06313v4 Announce Type: replace Abstract: This paper investigates how popular text-to-image (T2I) models, DALL-E 3 and Gemini 3 Pro Preview, depict people from 206 nationalities when prompted to generate images of individuals engaging in common everyday activities. Five scenarios were developed, and 2,060 ima... | https://arxiv.org/abs/2504.06313 | Academic Papers | svg |
bcfd05f4f38c3e2f21647422f4a9b00be437722ae7c4378b4bcb494b80f94712 | 2026-01-01T00:00:00-05:00 | Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration | arXiv:2504.09973v3 Announce Type: replace Abstract: All-in-One Image Restoration (AiOIR), which addresses diverse degradation types with a unified model, presents significant challenges in designing task-aware prompts that effectively guide restoration across multiple degradation scenarios. While adaptive prompt learni... | https://arxiv.org/abs/2504.09973 | Academic Papers | svg |
caf962ddd3c4e8b72b6cdb694691efaefbb3f2af2ccd97925d29d820419c3057 | 2026-01-01T00:00:00-05:00 | xVerify: Efficient Answer Verifier for Reasoning Model Evaluations | arXiv:2504.10481v2 Announce Type: replace Abstract: With the release of OpenAI's o1 model, reasoning models that adopt slow-thinking strategies have become increasingly common. Their outputs often contain complex reasoning, intermediate steps, and self-reflection, making existing evaluation methods and reward models in... | https://arxiv.org/abs/2504.10481 | Academic Papers | svg |
90101cc05da186abf63d72cd045f6ace6c326619a3f1e55dd5ccbd9bd7188454 | 2026-01-01T00:00:00-05:00 | Pre-DPO: Improving Data Utilization in Direct Preference Optimization Using a Guiding Reference Model | arXiv:2504.15843v3 Announce Type: replace Abstract: Direct Preference Optimization (DPO) simplifies reinforcement learning from human feedback (RLHF) for large language models (LLMs) by directly optimizing human preferences without an explicit reward model. We find that during DPO training, the reference model plays th... | https://arxiv.org/abs/2504.15843 | Academic Papers | svg |
f2e846a322b329431d7c13819b659c14bddd512878ff75608470d61b919b5db2 | 2026-01-01T00:00:00-05:00 | ParetoHqD: Fast Offline Multiobjective Alignment of Large Language Models using Pareto High-quality Data | arXiv:2504.16628v3 Announce Type: replace Abstract: Aligning large language models with multiple human expectations and values is crucial for ensuring that they adequately serve a variety of user needs. To this end, offline multiobjective alignment algorithms such as the Rewards-in-Context algorithm have shown strong p... | https://arxiv.org/abs/2504.16628 | Academic Papers | svg |
38ec21bc6fcfb506f522266f0e3bd64c230e0629f18bce0c6373a164ba9cd9de | 2026-01-01T00:00:00-05:00 | Dynamic Approximate Maximum Matching in the Distributed Vertex Partition Model | arXiv:2504.17338v3 Announce Type: replace Abstract: We initiate the study of approximate maximum matching in the vertex partition model, for graphs subject to dynamic changes. We assume that the $n$ vertices of the graph are partitioned among $k$ players, who execute a distributed algorithm and communicate via message ... | https://arxiv.org/abs/2504.17338 | Academic Papers | svg |
ad3e3024b1e1e24e630d1a10f233c2757c4072fdbb6f5d533b7e901286301eaa | 2026-01-01T00:00:00-05:00 | ALF: Advertiser Large Foundation Model for Multi-Modal Advertiser Understanding | arXiv:2504.18785v3 Announce Type: replace Abstract: We present ALF (Advertiser Large Foundation model), a multi-modal transformer architecture for understanding advertiser behavior and intent across text, image, video, and structured data modalities. Through contrastive learning and multi-task optimization, ALF creates... | https://arxiv.org/abs/2504.18785 | Academic Papers | svg |
eb161926a7674ba35b8ffe227ecc6fe79789cdc595c3fedba3d7a407de616270 | 2026-01-01T00:00:00-05:00 | Neurosymbolic Association Rule Mining from Tabular Data | arXiv:2504.19354v5 Announce Type: replace Abstract: Association Rule Mining (ARM) is the task of mining patterns among data features in the form of logical rules, with applications across a myriad of domains. However, high-dimensional datasets often result in an excessive number of rules, increasing execution time and ... | https://arxiv.org/abs/2504.19354 | Academic Papers | svg |
0be55ee733ed088f1b4227627967e59bb23a176dec7aeb5dcf4fae25e4fd37b7 | 2026-01-01T00:00:00-05:00 | Analysis of Errors in Robotic Surgical Skill Acquisition with Video-Based Detection | arXiv:2504.19571v2 Announce Type: replace Abstract: Robot-assisted minimally invasive surgeries offer many advantages but require complex motor tasks that take surgeons years to master. There is currently a lack of knowledge on how surgeons acquire these robotic surgical skills. Toward bridging this gap, a previous stu... | https://arxiv.org/abs/2504.19571 | Academic Papers | svg |
4dee9acde8cafcad91fe3ddc507c7243a31e3a481fdb9d76a7993e6d451ada02 | 2026-01-01T00:00:00-05:00 | Adapting In-Domain Few-Shot Segmentation to New Domains without Source Domain Retraining | arXiv:2504.21414v4 Announce Type: replace Abstract: Cross-domain few-shot segmentation (CD-FSS) aims to segment objects of novel classes in new domains, which is often challenging due to the diverse characteristics of target domains and the limited availability of support data. Most CD-FSS methods redesign and retrain ... | https://arxiv.org/abs/2504.21414 | Academic Papers | svg |
9ba9f6c49547f261c4810f1ed0634f72febbcb8906d419e45a189b73ac17c63c | 2026-01-01T00:00:00-05:00 | Zoomer: Adaptive Image Focus Optimization for Black-box MLLM | arXiv:2505.00742v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) such as GPT-4o, Gemini Pro, and Claude 3.5 have enabled unified reasoning over text and visual inputs, yet they often hallucinate in real world scenarios especially when small objects or fine spatial context are involved. We pi... | https://arxiv.org/abs/2505.00742 | Academic Papers | svg |
c180ab16b11f75cc531916938731c604ae152bf646fec145c6f586b243d4b98e | 2026-01-01T00:00:00-05:00 | Multi-Antenna Users in Cell-Free Massive MIMO: Stream Allocation and Necessity of Downlink Pilots | arXiv:2505.02951v2 Announce Type: replace Abstract: We consider a cell-free massive multiple-input multiple-output (MIMO) system with multiple antennas on the users and access points (APs). In previous works, the downlink spectral efficiency (SE) has been evaluated using the hardening bound that requires no downlink pi... | https://arxiv.org/abs/2505.02951 | Academic Papers | svg |
16880e5e6bd1d35b4695c951908fee637e9d01312009415746dcb9c3188a7e3b | 2026-01-01T00:00:00-05:00 | An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation | arXiv:2505.03452v3 Announce Type: replace Abstract: Optimizing Retrieval-Augmented Generation (RAG) configurations for specific tasks is a complex and resource-intensive challenge. Motivated by this challenge, frameworks for RAG hyper-parameter optimization (HPO) have recently emerged, yet their effectiveness has not b... | https://arxiv.org/abs/2505.03452 | Academic Papers | svg |
4ffcdca28a20571339050f1e60e3ecbf828f99ef1743c4404fef5cbad0de14d1 | 2026-01-01T00:00:00-05:00 | Selfish, Local and Online Scheduling via Vector Fitting | arXiv:2505.10082v3 Announce Type: replace Abstract: We provide a dual fitting technique on a semidefinite program yielding simple proofs of tight bounds for the robust price of anarchy of several congestion and scheduling games under the sum of weighted completion times objective. The same approach also allows to bound... | https://arxiv.org/abs/2505.10082 | Academic Papers | svg |
61d83fd871977297e1b26817ddd45def92754e94fff17f432779d1f2112a8f04 | 2026-01-01T00:00:00-05:00 | Neural Field Equations with random data | arXiv:2505.16343v3 Announce Type: replace Abstract: We study neural field equations, which are prototypical models of large-scale cortical activity, subject to random data. We view this spatially-extended, nonlocal evolution equation as a Cauchy problem on abstract Banach spaces, with randomness in the synaptic kernel,... | https://arxiv.org/abs/2505.16343 | Academic Papers | svg |
ca1941452e77d198c8a8bda49f7dafd66444580c26c1d61c89693b30cd743c9e | 2026-01-01T00:00:00-05:00 | MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding | arXiv:2505.20298v2 Announce Type: replace Abstract: Manga, or Japanese comics, is a richly multimodal narrative form that blends images and text in complex ways. Teaching large multimodal models (LMMs) to understand such narratives at a human-like level could help manga creators reflect on and refine their stories. To ... | https://arxiv.org/abs/2505.20298 | Academic Papers | svg |
fb43425a4dfdf80d1fb20a2233efe2ce4ec02a9624f39b9edabfe967bc3d343a | 2026-01-01T00:00:00-05:00 | OSVI-WM: One-Shot Visual Imitation for Unseen Tasks using World-Model-Guided Trajectory Generation | arXiv:2505.20425v2 Announce Type: replace Abstract: Visual imitation learning enables robotic agents to acquire skills by observing expert demonstration videos. In the one-shot setting, the agent generates a policy after observing a single expert demonstration without additional fine-tuning. Existing approaches typical... | https://arxiv.org/abs/2505.20425 | Academic Papers | svg |
3bcba872a4dc355e8e6520e1def0d3857510344f14478fab85adb5755316775e | 2026-01-01T00:00:00-05:00 | Do LLMs Understand Collaborative Signals? Diagnosis and Repair | arXiv:2505.20730v3 Announce Type: replace Abstract: Collaborative information from user-item interactions is a fundamental source of signal in successful recommender systems. Recently, researchers have attempted to incorporate this knowledge into large language model-based recommender approaches (LLMRec) to enhance the... | https://arxiv.org/abs/2505.20730 | Academic Papers | svg |
b52118f735babf47b3dd33f4f4ac388f521a80015a30451d74e685cb2281ea6f | 2026-01-01T00:00:00-05:00 | GoMatching++: Parameter- and Data-Efficient Arbitrary-Shaped Video Text Spotting and Benchmarking | arXiv:2505.22228v2 Announce Type: replace Abstract: Video text spotting (VTS) extends image text spotting (ITS) by adding text tracking, significantly increasing task complexity. Despite progress in VTS, existing methods still fall short of the performance seen in ITS. This paper identifies a key limitation in current ... | https://arxiv.org/abs/2505.22228 | Academic Papers | svg |
4575413b51425b3c0b11c825b9c72a9fd5ad0355ec63a0dc6bc211f0ab05d8e2 | 2026-01-01T00:00:00-05:00 | Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs | arXiv:2505.24830v3 Announce Type: replace Abstract: Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and inaccurate citations, which pose a challenge to their clinical adoption and regulatory compliance. Current methods, such as Retrieval Augmented Generation, partially a... | https://arxiv.org/abs/2505.24830 | Academic Papers | svg |
b3a5e4b5d806bcae3a9c947c419af603f5eae68a11aa04651ed90bd56d95392d | 2026-01-01T00:00:00-05:00 | TalkingHeadBench: A Multi-Modal Benchmark & Analysis of Talking-Head DeepFake Detection | arXiv:2505.24866v2 Announce Type: replace Abstract: The rapid advancement of talking-head deepfake generation fueled by advanced generative models has elevated the realism of synthetic videos to a level that poses substantial risks in domains such as media, politics, and finance. However, current benchmarks for deepfak... | https://arxiv.org/abs/2505.24866 | Academic Papers | svg |
1a484339f20fe8f5b707dc6c2c0f5ec0f84d076e7a8f85df68148f82fe8e47a7 | 2026-01-01T00:00:00-05:00 | Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task? | arXiv:2506.01482v2 Announce Type: replace Abstract: Stage lighting is a vital component in live music performances, shaping an engaging experience for both musicians and audiences. In recent years, Automatic Stage Lighting Control (ASLC) has attracted growing interest due to the high costs of hiring or training profess... | https://arxiv.org/abs/2506.01482 | Academic Papers | svg |
237122b69e6437e36a1d499082396963fe894de98307afc90aade6bc5c2197eb | 2026-01-01T00:00:00-05:00 | Controllable Human-centric Keyframe Interpolation with Generative Prior | arXiv:2506.03119v2 Announce Type: replace Abstract: Existing interpolation methods use pre-trained video diffusion priors to generate intermediate frames between sparsely sampled keyframes. In the absence of 3D geometric guidance, these methods struggle to produce plausible results for complex, articulated human motion... | https://arxiv.org/abs/2506.03119 | Academic Papers | svg |
cfd1443a4d77249013010834827878d35a920bebb17d73d530224761d5255031 | 2026-01-01T00:00:00-05:00 | Not All Tokens Are Meant to Be Forgotten | arXiv:2506.03142v2 Announce Type: replace Abstract: Large Language Models (LLMs), pre-trained on massive text corpora, exhibit remarkable human-level language understanding, reasoning, and decision-making abilities. However, they tend to memorize unwanted information, such as private or copyrighted content, raising sig... | https://arxiv.org/abs/2506.03142 | Academic Papers | svg |
03c4f86d740075082f71c3cfb19fa43bdd26a54cbe5b1f1bd49580bea113d311 | 2026-01-01T00:00:00-05:00 | Contextual Integrity in LLMs via Reasoning and Reinforcement Learning | arXiv:2506.04245v4 Announce Type: replace Abstract: As the era of autonomous agents making decisions on behalf of users unfolds, ensuring contextual integrity (CI) -- what is the appropriate information to share while carrying out a certain task -- becomes a central question to the field. We posit that CI demands a for... | https://arxiv.org/abs/2506.04245 | Academic Papers | svg |
45b1949d9e732f7beb9c120121c1824c348de7cf7a7bacdc12c3d82d6c6d25c2 | 2026-01-01T00:00:00-05:00 | BiTrajDiff: Bidirectional Trajectory Generation with Diffusion Models for Offline Reinforcement Learning | arXiv:2506.05762v3 Announce Type: replace Abstract: Recent advances in offline Reinforcement Learning (RL) have proven that effective policy learning can benefit from imposing conservative constraints on pre-collected datasets. However, such static datasets often exhibit distribution bias, resulting in limited generali... | https://arxiv.org/abs/2506.05762 | Academic Papers | svg |
cf6ade97d3910509ad072f5d2dc17230dd7e87175aedfd545bee8e6abdcb9a59 | 2026-01-01T00:00:00-05:00 | Guiding Cross-Modal Representations with MLLM Priors via Preference Alignment | arXiv:2506.06970v3 Announce Type: replace Abstract: Despite Contrastive Language-Image Pretraining (CLIP)'s remarkable capability to retrieve content across modalities, a substantial modality gap persists in its feature space. Intriguingly, we discover that off-the-shelf MLLMs (Multimodal Large Language Models) demonst... | https://arxiv.org/abs/2506.06970 | Academic Papers | svg |
48ce4ee948180f203def92d7594fab438db061406a659ad0500e58853a38129c | 2026-01-01T00:00:00-05:00 | Reproducibility in the Control of Autonomous Mobility-on-Demand Systems | arXiv:2506.07345v2 Announce Type: replace Abstract: Autonomous Mobility-on-Demand (AMoD) systems, powered by advances in robotics, control, and Machine Learning (ML), offer a promising paradigm for future urban transportation. AMoD offers fast and personalized travel services by leveraging centralized control of autono... | https://arxiv.org/abs/2506.07345 | Academic Papers | svg |
2c7f977a17e3dcd159eb6806d4dbe4f496d83830c9b5aebbd6212030b3af5792 | 2026-01-01T00:00:00-05:00 | Toward Robust Legal Text Formalization into Defeasible Deontic Logic using LLMs | arXiv:2506.08899v3 Announce Type: replace Abstract: We present a comprehensive approach to the automated formalization of legal texts using large language models (LLMs), targeting their transformation into Defeasible Deontic Logic (DDL). Our method employs a structured pipeline that segments complex normative language ... | https://arxiv.org/abs/2506.08899 | Academic Papers | svg |
8c53cebfa09094384fc1088077d2bea60ef3d4d5206e463c578a33b2a20c9aba | 2026-01-01T00:00:00-05:00 | Rapid prediction of cardiac activation in the left ventricle with geometric deep learning: a step towards cardiac resynchronization therapy planning | arXiv:2506.08987v3 Announce Type: replace Abstract: Cardiac resynchronization therapy (CRT) is a common intervention for patients with dyssynchronous heart failure, yet approximately one-third of recipients fail to respond, partly due to suboptimal lead placement. Identifying optimal pacing sites remains challenging, l... | https://arxiv.org/abs/2506.08987 | Academic Papers | svg |
c4a85e35e86fc43d6e248252f09781ffee6557362d4d6043c37e7187b34531ee | 2026-01-01T00:00:00-05:00 | A Geometric Multigrid Preconditioner for Discontinuous Galerkin Shifted Boundary Method | arXiv:2506.12899v2 Announce Type: replace Abstract: This paper introduces a geometric multigrid preconditioner for the Shifted Boundary Method (SBM) designed to solve PDEs on complex geometries. While SBM simplifies mesh generation by using a non-conforming background grid, it often results in non-symmetric and potenti... | https://arxiv.org/abs/2506.12899 | Academic Papers | svg |
130535fc0fc5b3e1b9553a721bb1dce8758a03faef165ddc199fb85ed837929c | 2026-01-01T00:00:00-05:00 | ChartBlender: An Interactive System for Authoring and Synchronizing Visualization Charts in Video | arXiv:2506.13129v2 Announce Type: replace Abstract: Embedding data visualizations in video can enhance the communication of complex information. However, this process is often labor-intensive, requiring designers to adjust visualizations frame by frame manually. In this work, we present ChartBlender, a novel system tha... | https://arxiv.org/abs/2506.13129 | Academic Papers | svg |
25237066474128641ff9af8a023ed1743dfa217ef4682fa6b30e9a60f42a1c15 | 2026-01-01T00:00:00-05:00 | A Survey on LLM-Assisted Clinical Trial Recruitment | arXiv:2506.15301v3 Announce Type: replace Abstract: Recent advances in LLMs have greatly improved general-domain NLP tasks. Yet, their adoption in critical domains, such as clinical trial recruitment, remains limited. As trials are designed in natural language and patient data is represented as both structured and unst... | https://arxiv.org/abs/2506.15301 | Academic Papers | svg |
39b9d68c4b9935201487a8938cc09a2d5420745e28878c0b22bd032651c650d5 | 2026-01-01T00:00:00-05:00 | Robust Robotic Exploration and Mapping Using Generative Occupancy Map Synthesis | arXiv:2506.20049v2 Announce Type: replace Abstract: We present a novel approach for enhancing robotic exploration by using generative occupancy mapping. We implement SceneSense, a diffusion model designed and trained for predicting 3D occupancy maps given partial observations. Our proposed approach probabilistically fu... | https://arxiv.org/abs/2506.20049 | Academic Papers | svg |
4d22b841d0080cab989b7436fef67a934b83ebeb73225cdea5778949b5b12af3 | 2026-01-01T00:00:00-05:00 | OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions | arXiv:2506.23361v3 Announce Type: replace Abstract: Existing feedforward subject-driven video customization methods mainly study single-subject scenarios due to the difficulty of constructing multi-subject training data pairs. Another challenging problem that how to use the signals such as depth, mask, camera, and text... | https://arxiv.org/abs/2506.23361 | Academic Papers | svg |
6710791ae1bcc3e354086fdfae7993b77673ccefe927c572dc0b4f317f50381b | 2026-01-01T00:00:00-05:00 | Passage-traversing optimal path planning with sampling-based algorithms | arXiv:2506.23614v2 Announce Type: replace Abstract: This paper introduces a new paradigm of optimal path planning, i.e., passage-traversing optimal path planning (PTOPP), that optimizes paths' traversed passages for specified optimization objectives. In particular, PTOPP is utilized to find the path with optimal access... | https://arxiv.org/abs/2506.23614 | Academic Papers | svg |
a67e1890ecc0ea77b210908418cdae060e0b9099d5de247e7c3e8f3492696c2b | 2026-01-01T00:00:00-05:00 | Learning from Random Subspace Exploration: Generalized Test-Time Augmentation with Self-supervised Distillation | arXiv:2507.01347v2 Announce Type: replace Abstract: We introduce Generalized Test-Time Augmentation (GTTA), a highly effective method for improving the performance of a trained model, which unlike other existing Test-Time Augmentation approaches from the literature is general enough to be used off-the-shelf for many vi... | https://arxiv.org/abs/2507.01347 | Academic Papers | svg |
9f671bf2d95d234fcd262155034e10353540fb2320ae908c43c4a09e3113a370 | 2026-01-01T00:00:00-05:00 | MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining | arXiv:2507.01785v2 Announce Type: replace Abstract: Data quality is a critical driver of large language model performance, yet existing model-based selection methods focus almost exclusively on English. We introduce MuRating, a scalable framework that transfers high-quality English data-quality signals into a single ra... | https://arxiv.org/abs/2507.01785 | Academic Papers | svg |
71eb515e332deeb393f3db465e8f282674826a3dc6ae2ea071546d8e894535b7 | 2026-01-01T00:00:00-05:00 | Large Language Model-Driven Closed-Loop UAV Operation with Semantic Observations | arXiv:2507.01930v5 Announce Type: replace Abstract: Recent advances in large Language Models (LLMs) have revolutionized mobile robots, including unmanned aerial vehicles (UAVs), enabling their intelligent operation within Internet of Things (IoT) ecosystems. However, LLMs still face challenges from logical reasoning an... | https://arxiv.org/abs/2507.01930 | Academic Papers | svg |
21485e3297e6cbaa291c48446fd506b588e891292252bb17b84879998958566b | 2026-01-01T00:00:00-05:00 | Dynamic Strategy Adaptation in Multi-Agent Environments with Large Language Models | arXiv:2507.02002v4 Announce Type: replace Abstract: Large language models (LLMs) demonstrate strong reasoning abilities across mathematical, strategic, and linguistic tasks, yet little is known about how well they reason in dynamic, real-time, multi-agent scenarios, such as collaborative environments in which agents co... | https://arxiv.org/abs/2507.02002 | Academic Papers | svg |
0217e3c0bfce7eb36e2af07a00d5b9824464cc95e954c8236c8e7cdd24f83701 | 2026-01-01T00:00:00-05:00 | Probabilistically Tightened Linear Relaxation-based Perturbation Analysis for Neural Network Verification | arXiv:2507.05405v2 Announce Type: replace Abstract: We present $\textbf{P}$robabilistically $\textbf{T}$ightened $\textbf{Li}$near $\textbf{R}$elaxation-based $\textbf{P}$erturbation $\textbf{A}$nalysis ($\texttt{PT-LiRPA}$), a novel framework that combines over-approximation techniques from LiRPA-based approaches with... | https://arxiv.org/abs/2507.05405 | Academic Papers | svg |
6966d7eadd65c8536d274a986f293a45284edf668967718d1c31a9b2798ebd20 | 2026-01-01T00:00:00-05:00 | PERK: Long-Context Reasoning as Parameter-Efficient Test-Time Learning | arXiv:2507.06415v2 Announce Type: replace Abstract: Long-context reasoning requires accurately identifying relevant information in extensive, noisy input contexts. Previous research shows that using test-time learning to encode context directly into model parameters can effectively enable reasoning over noisy informati... | https://arxiv.org/abs/2507.06415 | Academic Papers | svg |
043bb593a8f1a8f4520ce2e282afb95249399c81e3b3cd6c443b667435013e08 | 2026-01-01T00:00:00-05:00 | Mathematical artificial data for operator learning | arXiv:2507.06752v2 Announce Type: replace Abstract: Machine learning has emerged as a transformative tool for solving differential equations (DEs), yet prevailing methodologies remain constrained by dual limitations: data-driven methods demand costly labeled datasets while model-driven techniques face efficiency-accura... | https://arxiv.org/abs/2507.06752 | Academic Papers | svg |
0c3cd49514ea3bece2d0cee93d1d91024b963e2665972fcf3e37375749d14da6 | 2026-01-01T00:00:00-05:00 | One Graph to Track Them All: Dynamic GNNs for Single- and Multi-View Tracking | arXiv:2507.08494v2 Announce Type: replace Abstract: This work presents a unified, fully differentiable model for multi-people tracking that learns to associate detections into trajectories without relying on pre-computed tracklets. The model builds a dynamic spatiotemporal graph that aggregates spatial, contextual, and... | https://arxiv.org/abs/2507.08494 | Academic Papers | svg |
b26ac6ce23551b1c66757c7bc55ed68cafbe7e4c49a9967e959f44b3ed183616 | 2026-01-01T00:00:00-05:00 | Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devices | arXiv:2507.09627v2 Announce Type: replace Abstract: Next-generation wireless technologies such as 6G aim to meet demanding requirements such as ultra-high data rates, low latency, and enhanced connectivity. Extremely Large-Scale MIMO (XL-MIMO) and Reconfigurable Intelligent Surface (RIS) are key enablers, with XL-MIMO ... | https://arxiv.org/abs/2507.09627 | Academic Papers | svg |
035376205c279c212c3919c525f60df19ce4b90e6568a67eef2984154006538e | 2026-01-01T00:00:00-05:00 | Compressed data structures for Heegaard splittings | arXiv:2507.11406v2 Announce Type: replace Abstract: Heegaard splittings provide a natural representation of closed 3-manifolds by gluing two handlebodies along a common surface. These splittings can be equivalently given by two finite sets of meridians lying on the surface, which define a Heegaard diagram. We present a... | https://arxiv.org/abs/2507.11406 | Academic Papers | svg |
960f1f91594c49e107da7f6ce6e71ea8e13edbb0389837f2b20812b451cb9909 | 2026-01-01T00:00:00-05:00 | An Ecosystem for Ontology Interoperability | arXiv:2507.12311v5 Announce Type: replace Abstract: Ontology interoperability is one of the complicated issues that restricts the use of ontologies in knowledge graphs (KGs). Different ontologies with conflicting and overlapping concepts make it difficult to design, develop, and deploy an interoperable ontology for dow... | https://arxiv.org/abs/2507.12311 | Academic Papers | svg |
81b0b91952abacdcd9c5895e33dd720518b49fd9d7ce49216580766f0e82cb15 | 2026-01-01T00:00:00-05:00 | Sampling from Gaussian Processes: A Tutorial and Applications in Global Sensitivity Analysis and Optimization | arXiv:2507.14746v2 Announce Type: replace Abstract: High-fidelity simulations and physical experiments are essential for engineering analysis and design, yet their high cost often makes two critical tasks--global sensitivity analysis (GSA) and optimization--prohibitively expensive. This limitation motivates the common ... | https://arxiv.org/abs/2507.14746 | Academic Papers | svg |
00d34d71d10f318da5726a5816f01bebe7471e220cc94f68f3b46f860540dcec | 2026-01-01T00:00:00-05:00 | One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing Platforms | arXiv:2507.15351v2 Announce Type: replace Abstract: Order dispatch is a critical task in ride-sharing systems with Autonomous Vehicles (AVs), directly influencing efficiency and profits. Recently, Multi-Agent Reinforcement Learning (MARL) has emerged as a promising solution to this problem by decomposing the large stat... | https://arxiv.org/abs/2507.15351 | Academic Papers | svg |
fe6db51ef47e726db4acb4667f8f9330a4ba57049ce8e20df09b06906b566fd7 | 2026-01-01T00:00:00-05:00 | Natural Language Processing for Tigrinya: Current State and Future Directions | arXiv:2507.17974v3 Announce Type: replace Abstract: Despite being spoken by millions of people, Tigrinya remains severely underrepresented in Natural Language Processing (NLP) research. This work presents a comprehensive survey of NLP research for Tigrinya, analyzing over 50 studies from 2011 to 2025. We systematically... | https://arxiv.org/abs/2507.17974 | Academic Papers | svg |
28ff3530d62b75f2a1f22c51ef3b952e1191ed1b0c6fe87e89d87f672ecc6d04 | 2026-01-01T00:00:00-05:00 | GestureHYDRA: Semantic Co-speech Gesture Synthesis via Hybrid Modality Diffusion Transformer and Cascaded-Synchronized Retrieval-Augmented Generation | arXiv:2507.22731v2 Announce Type: replace Abstract: While increasing attention has been paid to co-speech gesture synthesis, most previous works neglect to investigate hand gestures with explicit and essential semantics. In this paper, we study co-speech gesture generation with an emphasis on specific hand gesture acti... | https://arxiv.org/abs/2507.22731 | Academic Papers | svg |
883ae0146d9306e13d32db4dd27da7ec3f0f59e819a6aeb0239f4fa17b17b374 | 2026-01-01T00:00:00-05:00 | Learning Network Dismantling Without Handcrafted Inputs | arXiv:2508.00706v2 Announce Type: replace Abstract: The application of message-passing Graph Neural Networks has been a breakthrough for important network science problems. However, the competitive performance often relies on using handcrafted structural features as inputs, which increases computational cost and introd... | https://arxiv.org/abs/2508.00706 | Academic Papers | svg |
6d29ce2f3cd271fed08e6bc3a64898c14fb818d62a16ceb85baabe0d5ced88fa | 2026-01-01T00:00:00-05:00 | Multi-step retrieval and reasoning improves radiology question answering with large language models | arXiv:2508.00743v4 Announce Type: replace Abstract: Clinical decision-making in radiology increasingly benefits from artificial intelligence (AI), particularly through large language models (LLMs). However, traditional retrieval-augmented generation (RAG) systems for radiology question answering (QA) typically rely on ... | https://arxiv.org/abs/2508.00743 | Academic Papers | svg |
639e16e34a8fff18e94879e4f0c90c04f53fcae7629c36cbae81ec320c6d1d2e | 2026-01-01T00:00:00-05:00 | SplatSSC: Decoupled Depth-Guided Gaussian Splatting for Semantic Scene Completion | arXiv:2508.02261v3 Announce Type: replace Abstract: Monocular 3D Semantic Scene Completion (SSC) is a challenging yet promising task that aims to infer dense geometric and semantic descriptions of a scene from a single image. While recent object-centric paradigms significantly improve efficiency by leveraging flexible ... | https://arxiv.org/abs/2508.02261 | Academic Papers | svg |
37320b475bf74a9b1c895b458f9c025a3a06f210d33273423894cb5d379c5106 | 2026-01-01T00:00:00-05:00 | BadBlocks: Lightweight and Stealthy Backdoor Threat in Text-to-Image Diffusion Models | arXiv:2508.03221v4 Announce Type: replace Abstract: Diffusion models have recently achieved remarkable success in image generation, yet growing evidence shows their vulnerability to backdoor attacks, where adversaries implant covert triggers to manipulate outputs. While existing defenses can detect many such attacks vi... | https://arxiv.org/abs/2508.03221 | Academic Papers | svg |
97528cd59bb316c52fdee4883a0036a9ad17bb81ed02065e6a6d693284155fd7 | 2026-01-01T00:00:00-05:00 | evTransFER: A Transfer Learning Framework for Event-based Facial Expression Recognition | arXiv:2508.03609v2 Announce Type: replace Abstract: Event-based cameras are bio-inspired sensors that asynchronously capture pixel intensity changes with microsecond latency, high temporal resolution, and high dynamic range, providing information on the spatiotemporal dynamics of a scene. We propose evTransFER, a trans... | https://arxiv.org/abs/2508.03609 | Academic Papers | svg |
7de00af585deef6c98b69cc597c64ba987e3ab499eb8524a5ea7a7cd7ab90fcf | 2026-01-01T00:00:00-05:00 | Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning | arXiv:2508.04329v4 Announce Type: replace Abstract: Supervised fine-tuning (SFT) plays a critical role for pretrained large language models (LLMs), notably enhancing their capacity to acquire domain-specific knowledge while preserving or potentially augmenting their general-purpose capabilities. However, the efficacy o... | https://arxiv.org/abs/2508.04329 | Academic Papers | svg |
cf3489a31712d466589f4aa5a98496bd5a9a29f166cfffaff7a0e80c905e5358 | 2026-01-01T00:00:00-05:00 | ITDR: An Instruction Tuning Dataset for Enhancing Large Language Models in Recommendations | arXiv:2508.05667v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated outstanding performance in natural language processing tasks. However, in the field of recommender systems, due to the inherent structural discrepancy between user behavior data and natural language, LLMs struggle to effe... | https://arxiv.org/abs/2508.05667 | Academic Papers | svg |
e3e2cd9a6c96904aeabd18eb0c5f083eedb9247627250b81f4d6f413a365313d | 2026-01-01T00:00:00-05:00 | MCITlib: Multimodal Continual Instruction Tuning Library and Benchmark | arXiv:2508.07307v3 Announce Type: replace Abstract: Continual learning enables AI systems to acquire new knowledge while retaining previously learned information. While traditional unimodal methods have made progress, the rise of Multimodal Large Language Models (MLLMs) brings new challenges in Multimodal Continual Lea... | https://arxiv.org/abs/2508.07307 | Academic Papers | svg |
0a56cca35b52a48cc23eddab37981b7da589198382ce970dd4a2b0b5ef739a43 | 2026-01-01T00:00:00-05:00 | Online Convex Optimization with Heavy Tails: Old Algorithms, New Regrets, and Applications | arXiv:2508.07473v2 Announce Type: replace Abstract: In Online Convex Optimization (OCO), when the stochastic gradient has a finite variance, many algorithms provably work and guarantee a sublinear regret. However, limited results are known if the gradient estimate has a heavy tail, i.e., the stochastic gradient only ad... | https://arxiv.org/abs/2508.07473 | Academic Papers | svg |
44c9143ca924c632fa1527307d749287c8eaa01b215c46abff39721caf523efe | 2026-01-01T00:00:00-05:00 | Generalising Traffic Forecasting to Regions without Traffic Observations | arXiv:2508.08947v2 Announce Type: replace Abstract: Traffic forecasting is essential for intelligent transportation systems. Accurate forecasting relies on continuous observations collected by traffic sensors. However, due to high deployment and maintenance costs, not all regions are equipped with such sensors. This pa... | https://arxiv.org/abs/2508.08947 | Academic Papers | svg |
cc5b340254726b6f1dd7abb27fad755958e3824748f875ee83352cca5080e069 | 2026-01-01T00:00:00-05:00 | CLF-RL: Control Lyapunov Function Guided Reinforcement Learning | arXiv:2508.09354v2 Announce Type: replace Abstract: Reinforcement learning (RL) has shown promise in generating robust locomotion policies for bipedal robots, but often suffers from tedious reward design and sensitivity to poorly shaped objectives. In this work, we propose a structured reward shaping framework that lev... | https://arxiv.org/abs/2508.09354 | Academic Papers | svg |
229dfe9ccfb53eda4b42c36c8d90389725352437e96987afd5a64a7e1d697548 | 2026-01-01T00:00:00-05:00 | RAJ-PGA: Reasoning-Activated Jailbreak and Principle-Guided Alignment Framework for Large Reasoning Models | arXiv:2508.12897v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) face a distinct safety vulnerability: their internal reasoning chains may generate harmful content even when the final output appears benign. To address this overlooked risk, we first propose a novel attack paradigm, Reasoning-Activated J... | https://arxiv.org/abs/2508.12897 | Academic Papers | svg |
a98fda5948bd399d20fd89425d3bc62cfd8b75de875b969754403ece9eb0beed | 2026-01-01T00:00:00-05:00 | Holistic Evaluation of Multimodal LLMs on Spatial Intelligence | arXiv:2508.13142v5 Announce Type: replace Abstract: Multimodal models have achieved remarkable progress in recent years. Nevertheless, they continue to exhibit notable limitations in spatial understanding and reasoning, the very capability that anchors artificial general intelligence in the physical world. With the rec... | https://arxiv.org/abs/2508.13142 | Academic Papers | svg |
98cd00bc9ab96d6033373d43b2e6c718a7d316770377d1e23a01bd4c44cfeaef | 2026-01-01T00:00:00-05:00 | Mamba2 Meets Silence: Robust Vocal Source Separation for Sparse Regions | arXiv:2508.14556v2 Announce Type: replace Abstract: We introduce a new music source separation model tailored for accurate vocal isolation. Unlike Transformer-based approaches, which often fail to capture intermittently occurring vocals, our model leverages Mamba2, a recent state space model, to better capture long-ran... | https://arxiv.org/abs/2508.14556 | Academic Papers | svg |
1a031f147baad7a8a996f9a827403e558a15a6c2dec41c3cb3de4aad311031ed | 2026-01-01T00:00:00-05:00 | MedQARo: A Large-Scale Benchmark for Evaluating Large Language Models on Medical Question Answering in Romanian | arXiv:2508.16390v3 Announce Type: replace Abstract: Question answering (QA) is an actively studied topic, being a core natural language processing (NLP) task that needs to be addressed before achieving Artificial General Intelligence (AGI). However, the lack of QA datasets in specific domains and languages hinders the ... | https://arxiv.org/abs/2508.16390 | Academic Papers | svg |
2e33b89de9d1c93f67e56254da9d2ea90537e427770e8d4b4321e0ad5bc00272 | 2026-01-01T00:00:00-05:00 | CrystalDiT: A Diffusion Transformer for Crystal Generation | arXiv:2508.16614v3 Announce Type: replace Abstract: We present CrystalDiT, a diffusion transformer for crystal structure generation that achieves state-of-the-art performance by challenging the trend of architectural complexity. Instead of intricate, multi-stream designs, CrystalDiT employs a unified transformer that i... | https://arxiv.org/abs/2508.16614 | Academic Papers | svg |
c6115d5068d65648f663390007ff4e92f65a6ce9df0daedee54980f9223450e9 | 2026-01-01T00:00:00-05:00 | STRelay: A Universal Spatio-Temporal Relaying Framework for Location Prediction over Human Trajectory Data | arXiv:2508.16620v2 Announce Type: replace Abstract: Next location prediction is a critical task in human mobility modeling, enabling applications like travel planning and urban mobility management. Existing methods mainly rely on historical spatiotemporal trajectory data to train sequence models that directly forecast ... | https://arxiv.org/abs/2508.16620 | Academic Papers | svg |
db18c2eaff95b52e43d5814a4e09d00af582adb92f98ce8cf8ec5b582bbae3d8 | 2026-01-01T00:00:00-05:00 | RAST: A Retrieval Augmented Spatio-Temporal Framework for Traffic Prediction | arXiv:2508.16623v2 Announce Type: replace Abstract: Traffic prediction is a cornerstone of modern intelligent transportation systems and a critical task in spatio-temporal forecasting. Although advanced Spatio-temporal Graph Neural Networks (STGNNs) and pre-trained models have achieved significant progress in traffic p... | https://arxiv.org/abs/2508.16623 | Academic Papers | svg |
e0c2f3b4a89a4d0cdf7f24cec1e2138f5bd29cfb2f68017295a22139dacba392 | 2026-01-01T00:00:00-05:00 | Natural Image Classification via Quasi-Cyclic Graph Ensembles and Random-Bond Ising Models at the Nishimori Temperature | arXiv:2508.18717v2 Announce Type: replace Abstract: Modern multi-class image classification relies on high-dimensional CNN feature vectors, which are computationally expensive and obscure the underlying data geometry. Conventional graph-based classifiers degrade on natural multi-class images because typical graphs fail... | https://arxiv.org/abs/2508.18717 | Academic Papers | svg |
284124fde3d8af27675bbb8696c5c8fb9873597863ce8e95b10e59f308b2717a | 2026-01-01T00:00:00-05:00 | CVBench: Benchmarking Cross-Video Synergies for Complex Multimodal Reasoning | arXiv:2508.19542v3 Announce Type: replace Abstract: While multimodal large language models (MLLMs) exhibit strong performance on single-video tasks (e.g., video question answering), their capability for spatiotemporal pattern reasoning across multiple videos remains a critical gap in pattern recognition research. Howev... | https://arxiv.org/abs/2508.19542 | Academic Papers | svg |
6d0498aeec78d825931a9388adfb9f902007e91f58d66c7e630c6b690f66f266 | 2026-01-01T00:00:00-05:00 | Towards Operational Validation of LLM-Agent Social Simulations: A Replicated Study of a Reddit-like Technology Forum | arXiv:2508.21740v2 Announce Type: replace Abstract: Large Language Models (LLMs) enable generative social simulations that can capture culturally informed, norm-guided interaction on online social platforms. We build a technology community simulation modeled on Voat, a Reddit-like alt-right news aggregator and discussi... | https://arxiv.org/abs/2508.21740 | Academic Papers | svg |
234cbf658716c77ec97b994914de5b99774e354abf34064fc4fc09937e9672ff | 2026-01-01T00:00:00-05:00 | Aligned Anchor Groups Guided Line Segment Detector | arXiv:2509.00786v2 Announce Type: replace Abstract: This paper introduces a novel line segment detector, the Aligned Anchor Groups guided Line Segment Detector (AAGLSD), designed to detect line segments from images with high precision and completeness. The algorithm employs a hierarchical approach to extract candidate ... | https://arxiv.org/abs/2509.00786 | Academic Papers | svg |
329f131c08d574db2af8776b95e5319d731c557d813e4bcb11fd33db5d37bbca | 2026-01-01T00:00:00-05:00 | Bidirectional Sparse Attention for Faster Video Diffusion Training | arXiv:2509.01085v4 Announce Type: replace Abstract: Video diffusion Transformer (DiT) models excel in generative quality but hit major computational bottlenecks when producing high-resolution, long-duration videos. The quadratic complexity of full attention leads to prohibitively high training and inference costs. Full... | https://arxiv.org/abs/2509.01085 | Academic Papers | svg |
e394cf39f1cfb4cc38b9f67a28af4a83470d227e88ef0d3341da79079c756f90 | 2026-01-01T00:00:00-05:00 | Towards Data-Driven Metrics for Social Robot Navigation Benchmarking | arXiv:2509.01251v2 Announce Type: replace Abstract: This paper presents a joint effort towards the development of a data-driven Social Robot Navigation metric to facilitate benchmarking and policy optimization for ground robots. We compiled a dataset with 4427 trajectories -- 182 real and 4245 simulated -- and presente... | https://arxiv.org/abs/2509.01251 | Academic Papers | svg |
e61834a7b21d3e657a06d57c215673e7bba46796104651465709e77cde7b2bdd | 2026-01-01T00:00:00-05:00 | In-N-Out: A Parameter-Level API Graph Dataset for Tool Agents | arXiv:2509.01560v3 Announce Type: replace Abstract: Tool agents--LLM-based systems that interact with external APIs--offer a way to execute real-world tasks. However, as tasks become increasingly complex, these agents struggle to identify and call the correct APIs in the proper order. To tackle this problem, we investi... | https://arxiv.org/abs/2509.01560 | Academic Papers | svg |
c8b034fb147ebf3f610f9148a0ce4640da649b72bd3d1cc94d2eca2b3248c58c | 2026-01-01T00:00:00-05:00 | Plan Verification for LLM-Based Embodied Task Completion Agents | arXiv:2509.02761v4 Announce Type: replace Abstract: Large language model (LLM) based task plans and corresponding human demonstrations for embodied AI may be noisy, with unnecessary actions, redundant navigation, and logical errors that reduce policy quality. We propose an iterative verification framework in which a Ju... | https://arxiv.org/abs/2509.02761 | Academic Papers | svg |
255a1955fd4b90098ccb23368064df3ca0bab7d37978783f1452e723f43290ef | 2026-01-01T00:00:00-05:00 | Hybrid dynamical systems modeling of power systems | arXiv:2509.02822v2 Announce Type: replace Abstract: The increasing integration of renewable energy sources has introduced complex dynamic behavior in power systems that challenge the adequacy of traditional continuous-time modeling approaches. These developments call for modeling frameworks that can capture the intrica... | https://arxiv.org/abs/2509.02822 | Academic Papers | svg |
3ff4b024c1ab535e1f62718fa299fe335e0a0194a01ea65a7cc3bca3b5d63b21 | 2026-01-01T00:00:00-05:00 | STSR: High-Fidelity Speech Super-Resolution via Spectral-Transient Context Modeling | arXiv:2509.03913v4 Announce Type: replace Abstract: Speech super-resolution (SR) reconstructs high-fidelity wideband speech from low-resolution inputs-a task that necessitates reconciling global harmonic coherence with local transient sharpness. While diffusion-based generative models yield impressive fidelity, their p... | https://arxiv.org/abs/2509.03913 | Academic Papers | svg |
e46bbd18e9634e447870c8afffae505c1db0b79e2a07c05e2c1f09dbfc7a2537 | 2026-01-01T00:00:00-05:00 | ACE-RL: Adaptive Constraint-Enhanced Reward for Long-form Generation Reinforcement Learning | arXiv:2509.04903v3 Announce Type: replace Abstract: Long-form generation has become a critical and challenging application for Large Language Models (LLMs). Existing studies are limited by their reliance on scarce, high-quality long-form response data and their focus on coarse-grained, general-purpose metrics (e.g., co... | https://arxiv.org/abs/2509.04903 | Academic Papers | svg |
c7d78623771697d67930ea885a8ed0c52df8ea5068b02bbc638d758963c8a1c5 | 2026-01-01T00:00:00-05:00 | Open-sci-ref-0.01: open and reproducible reference baselines for language model and dataset comparison | arXiv:2509.09009v3 Announce Type: replace Abstract: We introduce open-sci-ref, a family of dense transformer models trained as research baselines across multiple model (0.13B to 1.7B parameters) and token scales (up to 1T) on 8 recent open reference datasets. Evaluating the models on various standardized benchmarks, ou... | https://arxiv.org/abs/2509.09009 | Academic Papers | svg |
b73b4c56d71f9aceca97457556cc8b7fb928372ff12901712646a38811d4dfca | 2026-01-01T00:00:00-05:00 | Vital Signs Monitoring with mmWave OFDM JCAS System | arXiv:2509.11767v2 Announce Type: replace Abstract: Wireless techniques for monitoring human vital signs, such as heart and breathing rates, offer a promising solution in the context of joint communication and sensing (JCAS) with applications in medicine, sports, safety, security, and even the military. This paper repo... | https://arxiv.org/abs/2509.11767 | Academic Papers | svg |
c793344dea503512ac1a9e862ca5abaf7fcaf5df475ad0ab931d91a487f9337f | 2026-01-01T00:00:00-05:00 | A Novel Compression Framework for YOLOv8: Achieving Real-Time Aerial Object Detection on Edge Devices via Structured Pruning and Channel-Wise Distillation | arXiv:2509.12918v3 Announce Type: replace Abstract: Efficient deployment of deep learning models for aerial object detection on resource-constrained devices requires significant compression without com-promising performance. In this study, we propose a novel three-stage compression pipeline for the YOLOv8 object detect... | https://arxiv.org/abs/2509.12918 | Academic Papers | svg |
5c8dbcc173a140c9ef4c5136becbaa66f8cb85bcf0ef953b2d24135d86041ef3 | 2026-01-01T00:00:00-05:00 | Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking | arXiv:2509.14603v2 Announce Type: replace Abstract: Split Federated Learning (SFL) has emerged as an efficient alternative to traditional Federated Learning (FL) by reducing client-side computation through model partitioning. However, exchanging of intermediate activations and model updates introduces significant priva... | https://arxiv.org/abs/2509.14603 | Academic Papers | svg |
f10739b5374a4d85933552b210b7c6abef9b08359cd2b274ec0683267237b59e | 2026-01-01T00:00:00-05:00 | Chunk Based Speech Pre-training with High Resolution Finite Scalar Quantization | arXiv:2509.15579v2 Announce Type: replace Abstract: Low latency speech human-machine communication is becoming increasingly necessary as speech technology advances quickly in the last decade. One of the primary factors behind the advancement of speech technology is self-supervised learning. Most self-supervised learnin... | https://arxiv.org/abs/2509.15579 | Academic Papers | svg |
21dbd1631c8c62256027ff7946e4f47011ada88ee0274a972ae46f8e6c88bd64 | 2026-01-01T00:00:00-05:00 | Personalized Enhanced Federated Multi-View Clustering via Heat-Kernel Tensor Decomposition | arXiv:2509.16101v3 Announce Type: replace Abstract: This paper introduces mathematical frameworks that address the challenges of multi-view clustering in federated learning environments. The objective is to integrate optimization techniques based on new objective functions employing heat-kernel coefficients to replace ... | https://arxiv.org/abs/2509.16101 | Academic Papers | svg |
b5f35651fdd5c7cb7cb630982773a839273ea55e401f518a44fb83becbbab65e | 2026-01-01T00:00:00-05:00 | Audio Super-Resolution with Latent Bridge Models | arXiv:2509.17609v3 Announce Type: replace Abstract: Audio super-resolution (SR), i.e., upsampling the low-resolution (LR) waveform to the high-resolution (HR) version, has recently been explored with diffusion and bridge models, while previous methods often suffer from sub-optimal upsampling quality due to their uninfo... | https://arxiv.org/abs/2509.17609 | Academic Papers | svg |
87e8aebd7e7aad6092cb6b828c2dc0c75828f0e7a2161a5901b11ed0f2caf3fb | 2026-01-01T00:00:00-05:00 | SiDiaC: Sinhala Diachronic Corpus | arXiv:2509.17912v2 Announce Type: replace Abstract: SiDiaC, the first comprehensive Sinhala Diachronic Corpus, covers a historical span from the 5th to the 20th century CE. SiDiaC comprises 58k words across 46 literary works, annotated carefully based on the written date, after filtering based on availability, authorsh... | https://arxiv.org/abs/2509.17912 | Academic Papers | svg |
8777685fce2c4cc2a675bcf9f55cd65362619d9973ea8d55561c9ad42e8986a3 | 2026-01-01T00:00:00-05:00 | Secure and Efficient Access Control for Computer-Use Agents via Context Space | arXiv:2509.22256v3 Announce Type: replace Abstract: Large language model (LLM)-based computer-use agents represent a convergence of AI and OS capabilities, enabling natural language to control system- and application-level functions. However, due to LLMs' inherent uncertainty issues, granting agents control over comput... | https://arxiv.org/abs/2509.22256 | Academic Papers | svg |
185e85ffce54c9958955186e839fa44c3cdac3205a1356f45871fd2d7f7df9e8 | 2026-01-01T00:00:00-05:00 | Dynamical feedback control with operator learning for the Vlasov-Poisson system | arXiv:2509.23063v2 Announce Type: replace Abstract: To meet the demands of instantaneous control of instabilities over long time horizons in plasma fusion, we design a dynamic feedback control strategy for the Vlasov-Poisson system by constructing an operator that maps state perturbations to an external control field. ... | https://arxiv.org/abs/2509.23063 | Academic Papers | svg |
b55a6836b6bc99d70a880de8f87539b2acb83be24ebfa7af050eca0c224073b1 | 2026-01-01T00:00:00-05:00 | Towards Comprehensive Interactive Change Understanding in Remote Sensing: A Large-scale Dataset and Dual-granularity Enhanced VLM | arXiv:2509.23105v2 Announce Type: replace Abstract: Remote sensing change understanding (RSCU) is essential for analyzing remote sensing images and understanding how human activities affect the environment. However, existing datasets lack deep understanding and interactions in the diverse change captioning, counting, a... | https://arxiv.org/abs/2509.23105 | Academic Papers | svg |
f95b2f7475ab16d46bd86fc2b30498bdf1242a033fddb252843e53b47a3e4916 | 2026-01-01T00:00:00-05:00 | Unsupervised Online 3D Instance Segmentation with Synthetic Sequences and Dynamic Loss | arXiv:2509.23194v2 Announce Type: replace Abstract: Unsupervised online 3D instance segmentation is a fundamental yet challenging task, as it requires maintaining consistent object identities across LiDAR scans without relying on annotated training data. Existing methods, such as UNIT, have made progress in this direct... | https://arxiv.org/abs/2509.23194 | Academic Papers | svg |
c0936817609dcd098f38cc5661e690a0410738950cf5386094617796c4ba4744 | 2026-01-01T00:00:00-05:00 | Adversarial Reinforcement Learning Framework for ESP Cheater Simulation | arXiv:2509.24274v2 Announce Type: replace Abstract: Extra-Sensory Perception (ESP) cheats, which reveal hidden in-game information such as enemy locations, are difficult to detect because their effects are not directly observable in player behavior. The lack of observable evidence makes it difficult to collect reliably... | https://arxiv.org/abs/2509.24274 | Academic Papers | svg |
50bff6f4f95343fea97bca22d89a2776a19aabbb5773343a852df3025101c970 | 2026-01-01T00:00:00-05:00 | Deep Learning Accelerated Algebraic Multigrid Methods for Polytopal Discretizations of Second-Order Differential Problems | arXiv:2510.01442v2 Announce Type: replace Abstract: Algebraic Multigrid (AMG) methods are state-of-the-art algebraic solvers for partial differential equations. Still, their efficiency depends heavily on the choice of suitable parameters and/or ingredients. Paradigmatic examples include the so-called strong threshold p... | https://arxiv.org/abs/2510.01442 | Academic Papers | svg |
0e6786e9c54367c7ac4973056a73d9588d7ab762b93e1d8a0bcc8853fe419e89 | 2026-01-01T00:00:00-05:00 | Triple-BERT: Do We Really Need MARL for Order Dispatch on Ride-Sharing Platforms? | arXiv:2510.03257v2 Announce Type: replace Abstract: On-demand ride-sharing platforms, such as Uber and Lyft, face the intricate real-time challenge of bundling and matching passengers-each with distinct origins and destinations-to available vehicles, all while navigating significant system uncertainties. Due to the ext... | https://arxiv.org/abs/2510.03257 | Academic Papers | svg |
9e5791e909e86d6091362c3b53022272c48f0dd5a550c08caae7a8d4fc599046 | 2026-01-01T00:00:00-05:00 | Distributed Information Bottleneck Theory for Multi-Modal Task-Aware Semantic Communication | arXiv:2510.04000v3 Announce Type: replace Abstract: Semantic communication shifts the focus from bit-level accuracy to task-relevant semantic delivery, enabling efficient and intelligent communication for next-generation networks. However, existing multi-modal solutions often process all available data modalities indis... | https://arxiv.org/abs/2510.04000 | Academic Papers | svg |
8e634d8d45d6d842e9511b70e47cf55c2080f7d6e1d825a3b084dae8b73fc33a | 2026-01-01T00:00:00-05:00 | LiRA: A Multi-Agent Framework for Reliable and Readable Literature Review Generation | arXiv:2510.05138v3 Announce Type: replace Abstract: The rapid growth of scientific publications has made it increasingly difficult to keep literature reviews comprehensive and up-to-date. Though prior work has focused on automating retrieval and screening, the writing phase of systematic reviews remains largely under-e... | https://arxiv.org/abs/2510.05138 | Academic Papers | svg |
5b5281a8cfda1719dc527916fa7c4a1c7b392880a998170ba1fe20e8060781ba | 2026-01-01T00:00:00-05:00 | Smoother-type a posteriori error estimates for finite element methods | arXiv:2510.07677v2 Announce Type: replace Abstract: This work develops user-friendly a posteriori error estimates of finite element methods, based on smoothers of linear iterative solvers. The proposed method employs simple smoothers, such as Jacobi or Gauss--Seidel iteration, on an auxiliary finer mesh to process the ... | https://arxiv.org/abs/2510.07677 | Academic Papers | svg |
b2ffe23e24a4a6fafd3dfccda7bc0f860419ef0ac9e81f8428bdf002a0e2d016 | 2026-01-01T00:00:00-05:00 | Large Language Model Sourcing: A Survey | arXiv:2510.10161v2 Announce Type: replace Abstract: Due to the black-box nature of large language models (LLMs) and the realism of their generated content, issues such as hallucinations, bias, unfairness, and copyright infringement have become significant. In this context, sourcing information from multiple perspective... | https://arxiv.org/abs/2510.10161 | Academic Papers | svg |
93f5698176db4b451e4671e5712d444470ad3e4853abe464304347413a9ac86e | 2026-01-01T00:00:00-05:00 | Bringing The Consistency Gap: Explicit Structured Memory for Interleaved Image-Text Generation | arXiv:2510.10969v3 Announce Type: replace Abstract: Existing Vision Language Models (VLMs) often struggle to preserve logic, entity identity, and artistic style during extended, interleaved image-text interactions. We identify this limitation as "Multimodal Context Drift", which stems from the inherent tendency of impl... | https://arxiv.org/abs/2510.10969 | Academic Papers | svg |
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