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4f476332bf8eb3a3d02f719ffe135f371c47f1ecf198485baf88b2e631c13326 | 2026-01-07T00:00:00-05:00 | Hypothesize-Then-Verify: Speculative Root Cause Analysis for Microservices with Pathwise Parallelism | arXiv:2601.02736v1 Announce Type: new Abstract: Microservice systems have become the backbone of cloud-native enterprise applications due to their resource elasticity, loosely coupled architecture, and lightweight deployment. Yet, the intrinsic complexity and dynamic runtime interactions of such systems inevitably give... | https://arxiv.org/abs/2601.02736 | Academic Papers | svg |
4c46521590974be651474bf9134fbecee690899d1e288f9338dd9e74ac116013 | 2026-01-07T00:00:00-05:00 | Unveiling and Bridging the Functional Perception Gap in MLLMs: Atomic Visual Alignment and Hierarchical Evaluation via PET-Bench | arXiv:2601.02737v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in tasks such as abnormality detection and report generation for anatomical modalities, their capability in functional imaging remains largely unexplored. In this work, we identify and... | https://arxiv.org/abs/2601.02737 | Academic Papers | svg |
ff62f9520fec14f7275550a0920662ab7c822e8a6f9965c6dbf8597aedc5c543 | 2026-01-07T00:00:00-05:00 | Optimizing Control-Friendly Trajectories with Self-Supervised Residual Learning | arXiv:2601.02738v1 Announce Type: new Abstract: Real-world physics can only be analytically modeled with a certain level of precision for modern intricate robotic systems. As a result, tracking aggressive trajectories accurately could be challenging due to the existence of residual physics during controller synthesis. ... | https://arxiv.org/abs/2601.02738 | Academic Papers | svg |
d74eb4b35cd70bdb805913c9ac5a94c025c86708a5f9a460ae863036a28ee3bd | 2026-01-07T00:00:00-05:00 | Mitigating Prompt-Induced Hallucinations in Large Language Models via Structured Reasoning | arXiv:2601.02739v1 Announce Type: new Abstract: To address hallucination issues in large language models (LLMs), this paper proposes a method for mitigating prompt-induced hallucinations. Building on a knowledge distillation chain-style model, we introduce a code module to guide knowledge-graph exploration and incorpor... | https://arxiv.org/abs/2601.02739 | Academic Papers | svg |
3b845772539aab88cff64b8b3fc926702b50dd89d1dd8b1f60919060b83af04f | 2026-01-07T00:00:00-05:00 | Language Hierarchization Provides the Optimal Solution to Human Working Memory Limits | arXiv:2601.02740v1 Announce Type: new Abstract: Language is a uniquely human trait, conveying information efficiently by organizing word sequences in sentences into hierarchical structures. A central question persists: Why is human language hierarchical? In this study, we show that hierarchization optimally solves the ... | https://arxiv.org/abs/2601.02740 | Academic Papers | svg |
77374317ecc3c5c576ee1d36fcd4e0fbf71d85f4d0ee6e734d795163f69c49cb | 2026-01-07T00:00:00-05:00 | SYNAPSE: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation | arXiv:2601.02744v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel at generalized reasoning, standard retrieval-augmented approaches fail to address the disconnected nature of long-term agentic memory. To bridge this gap, we introduce Synapse (Synergistic Associative Processing Semantic Encoding),... | https://arxiv.org/abs/2601.02744 | Academic Papers | svg |
38088c095435d131b4b4fa49fbaba812d210477ab08f59252e088a90e6b5a609 | 2026-01-07T00:00:00-05:00 | D$^3$R-DETR: DETR with Dual-Domain Density Refinement for Tiny Object Detection in Aerial Images | arXiv:2601.02747v1 Announce Type: new Abstract: Detecting tiny objects plays a vital role in remote sensing intelligent interpretation, as these objects often carry critical information for downstream applications. However, due to the extremely limited pixel information and significant variations in object density, mai... | https://arxiv.org/abs/2601.02747 | Academic Papers | svg |
2ebe0811a23b4703d51bfedcd13218282d10cff9d27463d70fb1190a2ab5ca87 | 2026-01-07T00:00:00-05:00 | The Path Ahead for Agentic AI: Challenges and Opportunities | arXiv:2601.02749v1 Announce Type: new Abstract: The evolution of Large Language Models (LLMs) from passive text generators to autonomous, goal-driven systems represents a fundamental shift in artificial intelligence. This chapter examines the emergence of agentic AI systems that integrate planning, memory, tool use, an... | https://arxiv.org/abs/2601.02749 | Academic Papers | svg |
7b7823269f3d2cc2b7fd4e57b777cdf6ef4cd7625255e375935c182603dda0fb | 2026-01-07T00:00:00-05:00 | Ahead of the Spread: Agent-Driven Virtual Propagation for Early Fake News Detection | arXiv:2601.02750v1 Announce Type: new Abstract: Early detection of fake news is critical for mitigating its rapid dissemination on social media, which can severely undermine public trust and social stability. Recent advancements show that incorporating propagation dynamics can significantly enhance detection performanc... | https://arxiv.org/abs/2601.02750 | Academic Papers | svg |
509665b3a0f963041c719b9d83d48d0d982b3a73d1ef96a3d13028b719f52b42 | 2026-01-07T00:00:00-05:00 | Window-based Membership Inference Attacks Against Fine-tuned Large Language Models | arXiv:2601.02751v1 Announce Type: new Abstract: Most membership inference attacks (MIAs) against Large Language Models (LLMs) rely on global signals, like average loss, to identify training data. This approach, however, dilutes the subtle, localized signals of memorization, reducing attack effectiveness. We challenge t... | https://arxiv.org/abs/2601.02751 | Academic Papers | svg |
a155c429c66310868e675a4120e48eefbe55fcc1ba69ca368fc6bda57c99fdb1 | 2026-01-07T00:00:00-05:00 | EComStage: Stage-wise and Orientation-specific Benchmarking for Large Language Models in E-commerce | arXiv:2601.02752v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents are increasingly deployed in e-commerce applications to assist customer services in tasks such as product inquiries, recommendations, and order management. Existing benchmarks primarily evaluate whether these agents successfully com... | https://arxiv.org/abs/2601.02752 | Academic Papers | svg |
940e2c095c6b5b10f761def6bb015ae6071deb2a1dc4c9f29225d6540a7dc64d | 2026-01-07T00:00:00-05:00 | Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies | arXiv:2601.02754v1 Announce Type: new Abstract: With the rapid development of e-commerce, auto-bidding has become a key asset in optimizing advertising performance under diverse advertiser environments. The current approaches focus on reinforcement learning (RL) and generative models. These efforts imitate offline hist... | https://arxiv.org/abs/2601.02754 | Academic Papers | svg |
7df25b025ee0d7e25067dadba778244750cf453cc081624afe766e503fc8bc7d | 2026-01-07T00:00:00-05:00 | LLM Agent Framework for Intelligent Change Analysis in Urban Environment using Remote Sensing Imagery | arXiv:2601.02757v1 Announce Type: new Abstract: Existing change detection methods often lack the versatility to handle diverse real-world queries and the intelligence for comprehensive analysis. This paper presents a general agent framework, integrating Large Language Models (LLM) with vision foundation models to form ... | https://arxiv.org/abs/2601.02757 | Academic Papers | svg |
df1f4c87d697fa8be0d4dbfb14122f3b71be05c8b2f845345780ef9d0d23deca | 2026-01-07T00:00:00-05:00 | Towards Zero-Shot Point Cloud Registration Across Diverse Scales, Scenes, and Sensor Setups | arXiv:2601.02759v1 Announce Type: new Abstract: Some deep learning-based point cloud registration methods struggle with zero-shot generalization, often requiring dataset-specific hyperparameter tuning or retraining for new environments. We identify three critical limitations: (a) fixed user-defined parameters (e.g., vo... | https://arxiv.org/abs/2601.02759 | Academic Papers | svg |
826b317699d3f7d495eb52a0eec6906c03821b3c5c7807795a0b7c82a9a7c737 | 2026-01-07T00:00:00-05:00 | AnyDepth: Depth Estimation Made Easy | arXiv:2601.02760v1 Announce Type: new Abstract: Monocular depth estimation aims to recover the depth information of 3D scenes from 2D images. Recent work has made significant progress, but its reliance on large-scale datasets and complex decoders has limited its efficiency and generalization ability. In this paper, we ... | https://arxiv.org/abs/2601.02760 | Academic Papers | svg |
e7bc5315d36c8a4e3a9f3d736d1084766b1ea2e7229e96054b53c831bdd6e9cb | 2026-01-07T00:00:00-05:00 | Unified Meta-Representation and Feedback Calibration for General Disturbance Estimation | arXiv:2601.02762v1 Announce Type: new Abstract: Precise control in modern robotic applications is always an open issue due to unknown time-varying disturbances. Existing meta-learning-based approaches require a shared representation of environmental structures, which lack flexibility for realistic non-structural distur... | https://arxiv.org/abs/2601.02762 | Academic Papers | svg |
9a2b9e9c3330c08dae29fe54a5ecd6216b98e1a2c8a5a5e18faff12483c0535c | 2026-01-07T00:00:00-05:00 | ClearAIR: A Human-Visual-Perception-Inspired All-in-One Image Restoration | arXiv:2601.02763v1 Announce Type: new Abstract: All-in-One Image Restoration (AiOIR) has advanced significantly, offering promising solutions for complex real-world degradations. However, most existing approaches rely heavily on degradation-specific representations, often resulting in oversmoothing and artifacts. To ad... | https://arxiv.org/abs/2601.02763 | Academic Papers | svg |
aacd31eb02f4776f87e4f1808c021cc9fd6d7b9744d2fda606f1701406bb377e | 2026-01-07T00:00:00-05:00 | Netflix Artwork Personalization via LLM Post-training | arXiv:2601.02764v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated success in various applications of user recommendation and personalization across e-commerce and entertainment. On many entertainment platforms such as Netflix, users typically interact with a wide range of titles, each repre... | https://arxiv.org/abs/2601.02764 | Academic Papers | svg |
3e7b898069419fada0d010a531f23a5c30cabc6527ca6b7b93d74938f8cb9f43 | 2026-01-07T00:00:00-05:00 | Advancing Assistive Robotics: Multi-Modal Navigation and Biophysical Monitoring for Next-Generation Wheelchairs | arXiv:2601.02766v1 Announce Type: new Abstract: Assistive electric-powered wheelchairs (EPWs) have become essential mobility aids for people with disabilities such as amyotrophic lateral sclerosis (ALS), post-stroke hemiplegia, and dementia-related mobility impairment. This work presents a novel multi-modal EPW control... | https://arxiv.org/abs/2601.02766 | Academic Papers | svg |
d4d9550f3d84f12bd566902890ab75c0d41cd7eeb2c5bf8655a39c0b3e428131 | 2026-01-07T00:00:00-05:00 | AbductiveMLLM: Boosting Visual Abductive Reasoning Within MLLMs | arXiv:2601.02771v1 Announce Type: new Abstract: Visual abductive reasoning (VAR) is a challenging task that requires AI systems to infer the most likely explanation for incomplete visual observations. While recent MLLMs develop strong general-purpose multimodal reasoning capabilities, they fall short in abductive infer... | https://arxiv.org/abs/2601.02771 | Academic Papers | svg |
42987956c897956835e3c68a115a658cdce2264db34d444363cb1b2608856924 | 2026-01-07T00:00:00-05:00 | From Slaves to Synths? Superintelligence and the Evolution of Legal Personality | arXiv:2601.02773v1 Announce Type: new Abstract: This essay examines the evolving concept of legal personality through the lens of recent developments in artificial intelligence and the possible emergence of superintelligence. Legal systems have long been open to extending personhood to non-human entities, most prominen... | https://arxiv.org/abs/2601.02773 | Academic Papers | svg |
90d3c661523c642f8a50f174e50c5e0213a589fbc6dcffdf842194d4acc9ff1f | 2026-01-07T00:00:00-05:00 | Experience and Adaptation in AI-mediated Hiring Systems: A Combined Analysis of Online Discourse and Interface Design | arXiv:2601.02775v1 Announce Type: new Abstract: Automated interviewing tools are now widely adopted to manage recruitment at scale, often replacing early human screening with algorithmic assessments. While these systems are promoted as efficient and consistent, they also generate new forms of uncertainty for applicants... | https://arxiv.org/abs/2601.02775 | Academic Papers | svg |
0447ab3ca643c8c0529e2d39c55467d36487a9e266502c1100f215f1048aae8a | 2026-01-07T00:00:00-05:00 | UniSRCodec: Unified and Low-Bitrate Single Codebook Codec with Sub-Band Reconstruction | arXiv:2601.02776v1 Announce Type: new Abstract: Neural Audio Codecs (NACs) can reduce transmission overhead by performing compact compression and reconstruction, which also aim to bridge the gap between continuous and discrete signals. Existing NACs can be divided into two categories: multi-codebook and single-codebook... | https://arxiv.org/abs/2601.02776 | Academic Papers | svg |
9d46adcbdf8c9941e30ea5d628ec7a5741961894fa13981dee2c2ce338987da0 | 2026-01-07T00:00:00-05:00 | M-SEVIQ: A Multi-band Stereo Event Visual-Inertial Quadruped-based Dataset for Perception under Rapid Motion and Challenging Illumination | arXiv:2601.02777v1 Announce Type: new Abstract: Agile locomotion in legged robots poses significant challenges for visual perception. Traditional frame-based cameras often fail in these scenarios for producing blurred images, particularly under low-light conditions. In contrast, event cameras capture changes in brightn... | https://arxiv.org/abs/2601.02777 | Academic Papers | svg |
3facaa4d5c2383bfcb3a4105cc45e0119043e32cbf97d4429a75a011662e4185 | 2026-01-07T00:00:00-05:00 | Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation | arXiv:2601.02778v1 Announce Type: new Abstract: Human-like dexterous hands with multiple fingers offer human-level manipulation capabilities, but training control policies that can directly deploy on real hardware remains difficult due to contact-rich physics and imperfect actuation. We close this gap with a practical ... | https://arxiv.org/abs/2601.02778 | Academic Papers | svg |
28ae9c3af0340f3a0d76ac840ba65c9fc3dabf82250f6b2753477aff6ad864e0 | 2026-01-07T00:00:00-05:00 | Hierarchical Preemptive Holistic Collaborative Systems for Embodied Multi-Agent Systems: Framework, Hybrid Stability, and Scalability Analysis | arXiv:2601.02779v1 Announce Type: new Abstract: The coordination of Embodied Multi-Agent Systems in constrained physical environments requires a rigorous balance between safety, scalability, and efficiency. Traditional decentralized approaches, e.g., reactive collision avoidance, are prone to local minima or reciprocal... | https://arxiv.org/abs/2601.02779 | Academic Papers | svg |
0e9b302e1bd4a556047119075c8876ecc04adf00726833eb22f7fd18f9c03d6b | 2026-01-07T00:00:00-05:00 | MiMo-V2-Flash Technical Report | arXiv:2601.02780v1 Announce Type: new Abstract: We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-V2-Flash adopts a hybrid attention architecture that interleaves Sliding Window Attention (S... | https://arxiv.org/abs/2601.02780 | Academic Papers | svg |
2299ac0ca321c5d1a3aad14c6cd4a1ee2e0d28a21a9b09fb02f2efd3b2496654 | 2026-01-07T00:00:00-05:00 | EarthVL: A Progressive Earth Vision-Language Understanding and Generation Framework | arXiv:2601.02783v1 Announce Type: new Abstract: Earth vision has achieved milestones in geospatial object recognition but lacks exploration in object-relational reasoning, limiting comprehensive scene understanding. To address this, a progressive Earth vision-language understanding and generation framework is proposed,... | https://arxiv.org/abs/2601.02783 | Academic Papers | svg |
5097ef8d8b613c25fc179516e71deefb6849514953c22bc7321d11f234a4e678 | 2026-01-07T00:00:00-05:00 | DreamStyle: A Unified Framework for Video Stylization | arXiv:2601.02785v1 Announce Type: new Abstract: Video stylization, an important downstream task of video generation models, has not yet been thoroughly explored. Its input style conditions typically include text, style image, and stylized first frame. Each condition has a characteristic advantage: text is more flexible... | https://arxiv.org/abs/2601.02785 | Academic Papers | svg |
a6a144b5a7800bf1a7aeb19eaf0d4ad9ee026414e31a1c7a076cfe35347b5844 | 2026-01-07T00:00:00-05:00 | RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reuse | arXiv:2601.02790v1 Announce Type: new Abstract: Accurate radio map (RM) construction is essential to enabling environment-aware and adaptive wireless communication. However, in future 6G scenarios characterized by high-speed network entities and fast-changing environments, it is very challenging to meet real-time requi... | https://arxiv.org/abs/2601.02790 | Academic Papers | svg |
3bd8458471c38d3b75ff772a545c07fd876b8f8587ab09e330a67e4cd34fcffc | 2026-01-07T00:00:00-05:00 | Textile IR: A Bidirectional Intermediate Representation for Physics-Aware Fashion CAD | arXiv:2601.02792v1 Announce Type: new Abstract: We introduce Textile IR, a bidirectional intermediate representation that connects manufacturing-valid CAD, physics-based simulation, and lifecycle assessment for fashion design. Unlike existing siloed tools where pattern software guarantees sewable outputs but understand... | https://arxiv.org/abs/2601.02792 | Academic Papers | svg |
9ffdd7a00697bb3ba0310ba382fbb908a7cc0638cd55a0a8e8260dfb117a72b0 | 2026-01-07T00:00:00-05:00 | StableDPT: Temporal Stable Monocular Video Depth Estimation | arXiv:2601.02793v1 Announce Type: new Abstract: Applying single image Monocular Depth Estimation (MDE) models to video sequences introduces significant temporal instability and flickering artifacts. We propose a novel approach that adapts any state-of-the-art image-based (depth) estimation model for video processing by... | https://arxiv.org/abs/2601.02793 | Academic Papers | svg |
06a7843a45f40644a179540d8098486a49db69fa1f6834da618bbd6bbeabb93b | 2026-01-07T00:00:00-05:00 | Reinforcement Learning for Follow-the-Leader Robotic Endoscopic Navigation via Synthetic Data | arXiv:2601.02798v1 Announce Type: new Abstract: Autonomous navigation is crucial for both medical and industrial endoscopic robots, enabling safe and efficient exploration of narrow tubular environments without continuous human intervention, where avoiding contact with the inner walls has been a longstanding challenge ... | https://arxiv.org/abs/2601.02798 | Academic Papers | svg |
c22f39bc14f97a00715c1f2d142664278ff9c975e6f7f243b779e863467c59a9 | 2026-01-07T00:00:00-05:00 | Stratified Hazard Sampling: Minimal-Variance Event Scheduling for CTMC/DTMC Discrete Diffusion and Flow Models | arXiv:2601.02799v1 Announce Type: new Abstract: CTMC/DTMC-based discrete generative models, including uniform-noise discrete diffusion (e.g., D3PM/CTDD) and discrete flow matching, enable non-autoregressive sequence generation by repeatedly replacing tokens through a time-inhomogeneous Markov process. Inference is typi... | https://arxiv.org/abs/2601.02799 | Academic Papers | svg |
7c0e31cb6aeef290f91649f6138f021c62df28abc303b3e990cb139566f78ea6 | 2026-01-07T00:00:00-05:00 | State-Dependent Fading Gaussian Channel with Common Reconstruction Constraints | arXiv:2601.02802v1 Announce Type: new Abstract: The task of jointly communicating a message and reconstructing a common estimate of the channel state is examined for a fading Gaussian model with additive state interference. The state is an independent and identically distributed Gaussian sequence known noncausally at t... | https://arxiv.org/abs/2601.02802 | Academic Papers | svg |
8d7d114400052336fca124b4bf9d781222e7d33f7bcd5b3b7045e69791884a67 | 2026-01-07T00:00:00-05:00 | Bounded Rewriting Induction for LCSTRSs | arXiv:2601.02803v1 Announce Type: new Abstract: Rewriting Induction (RI) is a method to prove inductive theorems, originating from equational reasoning. By using Logically Constrained Simply-typed Term Rewriting Systems (LCSTRSs) as an intermediate language, rewriting induction becomes a tool for program verification, ... | https://arxiv.org/abs/2601.02803 | Academic Papers | svg |
0b3a72e77c7041e7a1490acbeee984773afe6eef5454e578848573bef55295f7 | 2026-01-07T00:00:00-05:00 | Distributionally Robust Game for Proof-of-Work Blockchain Mining Under Resource Uncertainties | arXiv:2601.02804v1 Announce Type: new Abstract: Blockchain plays a crucial role in ensuring the security and integrity of decentralized systems, with the proof-of-work (PoW) mechanism being fundamental for achieving distributed consensus. As PoW blockchains see broader adoption, an increasingly diverse set of miners wi... | https://arxiv.org/abs/2601.02804 | Academic Papers | svg |
ac2feb5d00c4412bba28762077cc43adab7204debe9600e56764e9eb4685c2b6 | 2026-01-07T00:00:00-05:00 | The perceptual gap between video see-through displays and natural human vision | arXiv:2601.02805v1 Announce Type: new Abstract: Video see-through (VST) technology aims to seamlessly blend virtual and physical worlds by reconstructing reality through cameras. While manufacturers promise perceptual fidelity, it remains unclear how close these systems are to replicating natural human vision across va... | https://arxiv.org/abs/2601.02805 | Academic Papers | svg |
423fff0679a9d1ff2b3586b71bb42047ab9ed07e0aad807a38d0fe0fd2590b41 | 2026-01-07T00:00:00-05:00 | Topology-aware Pathological Consistency Matching for Weakly-Paired IHC Virtual Staining | arXiv:2601.02806v1 Announce Type: new Abstract: Immunohistochemical (IHC) staining provides crucial molecular characterization of tissue samples and plays an indispensable role in the clinical examination and diagnosis of cancers. However, compared with the commonly used Hematoxylin and Eosin (H&E) staining, IHC st... | https://arxiv.org/abs/2601.02806 | Academic Papers | svg |
7c1869d7603dc1be84365bce02d52c4de3c4f1f9f9e0ab21fd9df3c5c8c3ba4e | 2026-01-07T00:00:00-05:00 | COFFEE: COdesign Framework for Feature Enriched Embeddings in Ads-Ranking Systems | arXiv:2601.02807v1 Announce Type: new Abstract: Diverse and enriched data sources are essential for commercial ads-recommendation models to accurately assess user interest both before and after engagement with content. While extended user-engagement histories can improve the prediction of user interests, it is equally ... | https://arxiv.org/abs/2601.02807 | Academic Papers | svg |
d6e65b5cd9f81839c067e65b57c5165f55fe4ecb3858fdbfe628c7d143590b44 | 2026-01-07T00:00:00-05:00 | HAL: Inducing Human-likeness in LLMs with Alignment | arXiv:2601.02813v1 Announce Type: new Abstract: Conversational human-likeness plays a central role in human-AI interaction, yet it has remained difficult to define, measure, and optimize. As a result, improvements in human-like behavior are largely driven by scale or broad supervised training, rather than targeted alig... | https://arxiv.org/abs/2601.02813 | Academic Papers | svg |
638035b08825a4f42a7168f875d8a080fffca5852a602fde7906d7db775f6c52 | 2026-01-07T00:00:00-05:00 | Causal-Enhanced AI Agents for Medical Research Screening | arXiv:2601.02814v1 Announce Type: new Abstract: Systematic reviews are essential for evidence-based medicine, but reviewing 1.5 million+ annual publications manually is infeasible. Current AI approaches suffer from hallucinations in systematic review tasks, with studies reporting rates ranging from 28--40% for earlier ... | https://arxiv.org/abs/2601.02814 | Academic Papers | svg |
61e47ee5abc03bb3bb20e9e05190208c97a7dcadd4b924d597f0acfbb58c34d5 | 2026-01-07T00:00:00-05:00 | Quantum-enhanced long short-term memory with attention for spatial permeability prediction in oilfield reservoirs | arXiv:2601.02818v1 Announce Type: new Abstract: Spatial prediction of reservoir parameters, especially permeability, is crucial for oil and gas exploration and development. However, the wide range and high variability of permeability prevent existing methods from providing reliable predictions. For the first time in su... | https://arxiv.org/abs/2601.02818 | Academic Papers | svg |
2445bc9ecd1f7233824a27ca34f8914cea6f95d701da1a84aec6778cba54e7a1 | 2026-01-07T00:00:00-05:00 | Punctuation-aware Hybrid Trainable Sparse Attention for Large Language Models | arXiv:2601.02819v1 Announce Type: new Abstract: Attention serves as the fundamental mechanism for long-context modeling in large language models (LLMs), yet dense attention becomes structurally prohibitive for long sequences due to its quadratic complexity. Consequently, sparse attention has received increasing attenti... | https://arxiv.org/abs/2601.02819 | Academic Papers | svg |
60891527917e469d0c53c9160441812fc3f5f4f9a4118b2c66b3677724359de0 | 2026-01-07T00:00:00-05:00 | DeepFP: Deep-Unfolded Fractional Programming for MIMO Beamforming | arXiv:2601.02822v1 Announce Type: new Abstract: This work proposes a mixed learning-based and optimization-based approach to the weighted-sum-rates beamforming problem in a multiple-input multiple-output (MIMO) wireless network. The conventional methods, i.e., the fractional programming (FP) method and the weighted min... | https://arxiv.org/abs/2601.02822 | Academic Papers | svg |
2782dd088ab8acd2c0c28b35ff8488d9183906bc3732f1217a37b56a0338d4bb | 2026-01-07T00:00:00-05:00 | Case Count Metric for Comparative Analysis of Entity Resolution Results | arXiv:2601.02824v1 Announce Type: new Abstract: This paper describes a new process and software system, the Case Count Metric System (CCMS), for systematically comparing and analyzing the outcomes of two different ER clustering processes acting on the same dataset when the true linking (labeling) is not known. The CCMS... | https://arxiv.org/abs/2601.02824 | Academic Papers | svg |
c9a7fcc79091c280fa8d085f689de95c58c03c4a6eff8359bee8aab267a107ca | 2026-01-07T00:00:00-05:00 | SketchThinker-R1: Towards Efficient Sketch-Style Reasoning in Large Multimodal Models | arXiv:2601.02825v1 Announce Type: new Abstract: Despite the empirical success of extensive, step-by-step reasoning in large multimodal models, long reasoning processes inevitably incur substantial computational overhead, i.e., in terms of higher token costs and increased response time, which undermines inference effici... | https://arxiv.org/abs/2601.02825 | Academic Papers | svg |
173ba6ce11c8080ded742bea1686a1ee6d7698bb529b35887a585394a550da76 | 2026-01-07T00:00:00-05:00 | Resolution deficits drive simulator sickness and compromise reading performance in virtual environments | arXiv:2601.02829v1 Announce Type: new Abstract: Extended reality (XR) is evolving into a general-purpose computing platform, yet its adoption for productivity is hindered by visual fatigue and simulator sickness. While these symptoms are often attributed to latency or motion conflicts, the precise impact of textual cla... | https://arxiv.org/abs/2601.02829 | Academic Papers | svg |
41fc9f3d4a0db05a613333cafa27ae3ac649010d3ccd07a25da6e5fee39d3e3f | 2026-01-07T00:00:00-05:00 | The performances of the Chinese and U.S. Large Language Models on the Topic of Chinese Culture | arXiv:2601.02830v1 Announce Type: new Abstract: Cultural backgrounds shape individuals' perspectives and approaches to problem-solving. Since the emergence of GPT-1 in 2018, large language models (LLMs) have undergone rapid development. To date, the world's ten leading LLM developers are primarily based in China and th... | https://arxiv.org/abs/2601.02830 | Academic Papers | svg |
30e9cd4b2f216b7a21808f705f2a5626f9d14bf33afa3c115e2ef36f02a7bd11 | 2026-01-07T00:00:00-05:00 | DGA-Net: Enhancing SAM with Depth Prompting and Graph-Anchor Guidance for Camouflaged Object Detection | arXiv:2601.02831v1 Announce Type: new Abstract: To fully exploit depth cues in Camouflaged Object Detection (COD), we present DGA-Net, a specialized framework that adapts the Segment Anything Model (SAM) via a novel ``depth prompting" paradigm. Distinguished from existing approaches that primarily rely on sparse prompt... | https://arxiv.org/abs/2601.02831 | Academic Papers | svg |
39391bdfb105ced62d9da368e5c288a8194e11d162f9b2d2b4437a120b0b3110 | 2026-01-07T00:00:00-05:00 | A Practical 73/50 Approximation for Contiguous Monotone Moldable Job Scheduling | arXiv:2601.02836v1 Announce Type: new Abstract: In moldable job scheduling, we are provided $m$ identical machines and $n$ jobs that can be executed on a variable number of machines. The execution time of each job depends on the number of machines assigned to execute that job. For the specific problem of monotone molda... | https://arxiv.org/abs/2601.02836 | Academic Papers | svg |
791739db4c1bae75f4de5e6d5002f33edde2195041c990099f82f36d80257d75 | 2026-01-07T00:00:00-05:00 | Breaking Self-Attention Failure: Rethinking Query Initialization for Infrared Small Target Detection | arXiv:2601.02837v1 Announce Type: new Abstract: Infrared small target detection (IRSTD) faces significant challenges due to the low signal-to-noise ratio (SNR), small target size, and complex cluttered backgrounds. Although recent DETR-based detectors benefit from global context modeling, they exhibit notable performan... | https://arxiv.org/abs/2601.02837 | Academic Papers | svg |
849fcdafc17e4903bcc798d25996b17cff49ec47635ab93c0c306ffbbffe9a18 | 2026-01-07T00:00:00-05:00 | TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents | arXiv:2601.02845v1 Announce Type: new Abstract: Long-horizon conversational agents have to manage ever-growing interaction histories that quickly exceed the finite context windows of large language models (LLMs). Existing memory frameworks provide limited support for temporally structured information across hierarchica... | https://arxiv.org/abs/2601.02845 | Academic Papers | svg |
551d8bd57d3bad7e2555c2a907a9ecb2f3c3c43791f11ed55be06d5a410cce6c | 2026-01-07T00:00:00-05:00 | Stability and error estimates of a linear and partitioned finite element method approximating nonlinear fluid-structure interactions | arXiv:2601.02847v1 Announce Type: new Abstract: We propose and analyze a linear and partitioned finite element method for fluid-shell interactions under the arbitrary Lagrangian-Eulerian (ALE) framework. We adopt the P1-bubble/P1/P1 elements for the fluid velocity, pressure, and structure velocity, respectively. We sho... | https://arxiv.org/abs/2601.02847 | Academic Papers | svg |
f2d15cbe4308dcc6fd39bd0db4d5fa0bf4fc320128be7f3b483522cddd0910bf | 2026-01-07T00:00:00-05:00 | Modeling ICD-10 Morbidity and Multidimensional Poverty as a Spatial Network: Evidence from Thailand | arXiv:2601.02848v1 Announce Type: new Abstract: Health and poverty in Thailand exhibit pronounced geographic structuring, yet the extent to which they operate as interconnected regional systems remains insufficiently understood. This study analyzes ICD-10 chapter-level morbidity and multidimensional poverty as outcomes... | https://arxiv.org/abs/2601.02848 | Academic Papers | svg |
ce5b28a32e0444f0c94d9f0d440ab0fb1899f375cdaf44bbcfc8f4a2e4241be3 | 2026-01-07T00:00:00-05:00 | Sample-Efficient Neurosymbolic Deep Reinforcement Learning | arXiv:2601.02850v1 Announce Type: new Abstract: Reinforcement Learning (RL) is a well-established framework for sequential decision-making in complex environments. However, state-of-the-art Deep RL (DRL) algorithms typically require large training datasets and often struggle to generalize beyond small-scale training sc... | https://arxiv.org/abs/2601.02850 | Academic Papers | svg |
1eb33d3c8785513a5c56ffea82be99c86eff440a152112d32d0258b6955fe4c7 | 2026-01-07T00:00:00-05:00 | M3MAD-Bench: Are Multi-Agent Debates Really Effective Across Domains and Modalities? | arXiv:2601.02854v1 Announce Type: new Abstract: As an agent-level reasoning and coordination paradigm, Multi-Agent Debate (MAD) orchestrates multiple agents through structured debate to improve answer quality and support complex reasoning. However, existing research on MAD suffers from two fundamental limitations: eval... | https://arxiv.org/abs/2601.02854 | Academic Papers | svg |
09405d302a798cdf59dde2317c16cd8f0328b28f4f4584ca6d7f4688ea599dd4 | 2026-01-07T00:00:00-05:00 | Context-aware Privacy Bounds for Linear Queries | arXiv:2601.02855v1 Announce Type: new Abstract: Linear queries, as the basis of broad analysis tasks, are often released through privacy mechanisms based on differential privacy (DP), the most popular framework for privacy protection. However, DP adopts a context-free definition that operates independently of the data-... | https://arxiv.org/abs/2601.02855 | Academic Papers | svg |
d4acec1d3fd73bb73d17ea61a20ad6e8713f7f7a8af9052770d7e17c8de0bca8 | 2026-01-07T00:00:00-05:00 | Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learning | arXiv:2601.02856v1 Announce Type: new Abstract: Precise day-ahead forecasts for electricity prices are crucial to ensure efficient portfolio management, support strategic decision-making for power plant operations, enable efficient battery storage optimization, and facilitate demand response planning. However, developi... | https://arxiv.org/abs/2601.02856 | Academic Papers | svg |
821c560b4860a6a783ac29f1668826227511b1297bb14995659dfde58f02c872 | 2026-01-07T00:00:00-05:00 | Soft Responsive Materials Enhance Humanoid Safety | arXiv:2601.02857v1 Announce Type: new Abstract: Humanoid robots are envisioned as general-purpose platforms in human-centered environments, yet their deployment is limited by vulnerability to falls and the risks posed by rigid metal-plastic structures to people and surroundings. We introduce a soft-rigid co-design fram... | https://arxiv.org/abs/2601.02857 | Academic Papers | svg |
e65d1b8d4df1f992a3e17baa3431f34476c69b113890af93a7bc15768f5deeaa | 2026-01-07T00:00:00-05:00 | To Generate or Discriminate? Methodological Considerations for Measuring Cultural Alignment in LLMs | arXiv:2601.02858v1 Announce Type: new Abstract: Socio-demographic prompting (SDP) - prompting Large Language Models (LLMs) using demographic proxies to generate culturally aligned outputs - often shows LLM responses as stereotypical and biased. While effective in assessing LLMs' cultural competency, SDP is prone to con... | https://arxiv.org/abs/2601.02858 | Academic Papers | svg |
ec41f3362dc220414a05e2389997f7c7afb30afbfa5692f37d05198cf0bfdc26 | 2026-01-07T00:00:00-05:00 | Training Language Models with homotokens Leads to Delayed Overfitting | arXiv:2601.02867v1 Announce Type: new Abstract: Subword tokenization introduces a computational layer in language models where many distinct token sequences decode to the same surface form and preserve meaning, yet induce different internal computations. Despite this non-uniqueness, language models are typically traine... | https://arxiv.org/abs/2601.02867 | Academic Papers | svg |
4ca609a08bd632f183ee152c9eb50de79f577d8b2ba64a3f95681d19c1d1996d | 2026-01-07T00:00:00-05:00 | CodeMEM: AST-Guided Adaptive Memory for Repository-Level Iterative Code Generation | arXiv:2601.02868v1 Announce Type: new Abstract: Large language models (LLMs) substantially enhance developer productivity in repository-level code generation through interactive collaboration. However, as interactions progress, repository context must be continuously preserved and updated to integrate newly validated i... | https://arxiv.org/abs/2601.02868 | Academic Papers | svg |
89e908fb59a261d47119b30c19bcdcefb5791f3e45ce8647290e9f9393df77fc | 2026-01-07T00:00:00-05:00 | Quantum-Enhanced Neural Contextual Bandit Algorithms | arXiv:2601.02870v1 Announce Type: new Abstract: Stochastic contextual bandits are fundamental for sequential decision-making but pose significant challenges for existing neural network-based algorithms, particularly when scaling to quantum neural networks (QNNs) due to issues such as massive over-parameterization, comp... | https://arxiv.org/abs/2601.02870 | Academic Papers | svg |
d7da1b4d7681e9e2b9c96f3dabdb3e1fb1a2bd2a918b491cb2c8407a1d331efd | 2026-01-07T00:00:00-05:00 | SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection | arXiv:2601.02871v1 Announce Type: new Abstract: Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-media contacts for private-channel conversion. Although supervised fine-tuning and reinforcemen... | https://arxiv.org/abs/2601.02871 | Academic Papers | svg |
78e1f8e6534f5dd029751b73f9737e4b9c884a739b44749434f881d2a24e6cd1 | 2026-01-07T00:00:00-05:00 | LongBench Pro: A More Realistic and Comprehensive Bilingual Long-Context Evaluation Benchmark | arXiv:2601.02872v1 Announce Type: new Abstract: The rapid expansion of context length in large language models (LLMs) has outpaced existing evaluation benchmarks. Current long-context benchmarks often trade off scalability and realism: synthetic tasks underrepresent real-world complexity, while fully manual annotation ... | https://arxiv.org/abs/2601.02872 | Academic Papers | svg |
6c580178dc5965b74b040b970ceca3c773fea1c6d1daf397d393aae0feff0fb8 | 2026-01-07T00:00:00-05:00 | Warm-Starting Collision-Free Model Predictive Control With Object-Centric Diffusion | arXiv:2601.02873v1 Announce Type: new Abstract: Acting in cluttered environments requires predicting and avoiding collisions while still achieving precise control. Conventional optimization-based controllers can enforce physical constraints, but they struggle to produce feasible solutions quickly when many obstacles ar... | https://arxiv.org/abs/2601.02873 | Academic Papers | svg |
5f7f4c3c914fbb98ddf753f939596917ddcfbdb87b722a78584cf266e15f7552 | 2026-01-07T00:00:00-05:00 | Revisiting Data Compression with Language Modeling | arXiv:2601.02875v1 Announce Type: new Abstract: In this report, we investigate the potential use of large language models (LLM's) in the task of data compression. Previous works have demonstrated promising results in applying LLM's towards compressing not only text, but also a wide range of multi-modal data. Despite th... | https://arxiv.org/abs/2601.02875 | Academic Papers | svg |
cd9ff4597ccf29e37f724ef9ee58902aa119fbdff589cd4ecc182cdf61cfa5ad | 2026-01-07T00:00:00-05:00 | ReTreVal: Reasoning Tree with Validation - A Hybrid Framework for Enhanced LLM Multi-Step Reasoning | arXiv:2601.02880v1 Announce Type: new Abstract: Multi-step reasoning remains a key challenge for Large Language Models (LLMs), particularly in complex domains such as mathematics and creative writing. While recent approaches including ReAct, Reflexion, and Self-Refine improve reasoning through iterative refinement and ... | https://arxiv.org/abs/2601.02880 | Academic Papers | svg |
51c3e701a8d4e3f84e3a5e4c204e10001bd2ae7a9cb87c64cf7f04b0dbd77fb1 | 2026-01-07T00:00:00-05:00 | Towards Agnostic and Holistic Universal Image Segmentation with Bit Diffusion | arXiv:2601.02881v1 Announce Type: new Abstract: This paper introduces a diffusion-based framework for universal image segmentation, making agnostic segmentation possible without depending on mask-based frameworks and instead predicting the full segmentation in a holistic manner. We present several key adaptations to di... | https://arxiv.org/abs/2601.02881 | Academic Papers | svg |
5729bf8c44865fdde85761fc74b0a08da77838c49add17671d79bf9940b8119b | 2026-01-07T00:00:00-05:00 | Domain Generalization for Time Series: Enhancing Drilling Regression Models for Stick-Slip Index Prediction | arXiv:2601.02884v1 Announce Type: new Abstract: This paper provides a comprehensive comparison of domain generalization techniques applied to time series data within a drilling context, focusing on the prediction of a continuous Stick-Slip Index (SSI), a critical metric for assessing torsional downhole vibrations at th... | https://arxiv.org/abs/2601.02884 | Academic Papers | svg |
7dcde5f0d383d5670ce99ad23f01e4f4bf50c836637db9f09de84bb3d9f4ec21 | 2026-01-07T00:00:00-05:00 | A Mathematical Formalization of Self-Determining Agency | arXiv:2601.02885v1 Announce Type: new Abstract: Defining agency is an extremely important challenge for cognitive science and artificial intelligence. Physics generally describes mechanical happenings, but there remains an unbridgeable gap between them and the acts of agents. To discuss the morality and responsibility ... | https://arxiv.org/abs/2601.02885 | Academic Papers | svg |
25e80f8ac3daeebd350a3711edaa2ce38866769823864e3118a176cd6913ebcc | 2026-01-07T00:00:00-05:00 | RPIQ: Residual-Projected Multi-Collaboration Closed-Loop and Single Instance Quantization for Visually Impaired Assistance | arXiv:2601.02888v1 Announce Type: new Abstract: Visually impaired users face significant challenges in daily information access and real-time environmental perception, and there is an urgent need for intelligent assistive systems with accurate recognition capabilities. Although large-scale models provide effective solu... | https://arxiv.org/abs/2601.02888 | Academic Papers | svg |
2f6ac85b4c86d0729888702f1a00b256cd5e26907069becff05dcc1ed5240c4f | 2026-01-07T00:00:00-05:00 | Transparent Semantic Change Detection with Dependency-Based Profiles | arXiv:2601.02891v1 Announce Type: new Abstract: Most modern computational approaches to lexical semantic change detection (LSC) rely on embedding-based distributional word representations with neural networks. Despite the strong performance on LSC benchmarks, they are often opaque. We investigate an alternative method ... | https://arxiv.org/abs/2601.02891 | Academic Papers | svg |
2bd1f643bf841e6e37f83e2492bbd26870d105b978be2f78b727da484c44f14c | 2026-01-07T00:00:00-05:00 | Bridging Mechanistic Interpretability and Prompt Engineering with Gradient Ascent for Interpretable Persona Control | arXiv:2601.02896v1 Announce Type: new Abstract: Controlling emergent behavioral personas (e.g., sycophancy, hallucination) in Large Language Models (LLMs) is critical for AI safety, yet remains a persistent challenge. Existing solutions face a dilemma: manual prompt engineering is intuitive but unscalable and imprecise... | https://arxiv.org/abs/2601.02896 | Academic Papers | svg |
871bef0eb5fe561447e176e264e2472b7762172bd7d6163da6e7a6994c5fdf7f | 2026-01-07T00:00:00-05:00 | Proceedings of the 1st International Workshop on Low Carbon Computing (LOCO 2024) | arXiv:2601.02898v1 Announce Type: new Abstract: This is the proceedings of the 1st International Workshop on Low Carbon Computing (LOCO 2024). | https://arxiv.org/abs/2601.02898 | Academic Papers | svg |
eeb06891388059a90171e0a8b3ceeef5755c7027baa65fdf8ef7c6d94dc5b177 | 2026-01-07T00:00:00-05:00 | SPO-CLAPScore: Enhancing CLAP-based alignment prediction system with Standardize Preference Optimization, for the first XACLE Challenge | arXiv:2601.02900v1 Announce Type: new Abstract: The first XACLE Challenge (x-to-audio alignment challenge) addresses the critical need for automatic evaluation metrics that correlate with human perception of audio-text semantic alignment. In this paper, we describe the "Takano_UTokyo_03" system submitted to XACLE Chall... | https://arxiv.org/abs/2601.02900 | Academic Papers | svg |
1312aa6bb10f20726388d69432fad5c9b568445308fca8d9c8cf0e7f0bf4ceeb | 2026-01-07T00:00:00-05:00 | Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning | arXiv:2601.02902v1 Announce Type: new Abstract: Symbolic logical reasoning is a critical yet underexplored capability of large language models (LLMs), providing reliable and verifiable decision-making in high-stakes domains such as mathematical reasoning and legal judgment. In this study, we present a systematic analys... | https://arxiv.org/abs/2601.02902 | Academic Papers | svg |
ca83a129b0630731407d422849850bcfdceb19f34bac93f386e874b0cab73c04 | 2026-01-07T00:00:00-05:00 | Site-Specific and Frequency-Dependent Channel Characterization and MIMO Performance in FR3 | arXiv:2601.02903v1 Announce Type: new Abstract: Next-generation wireless systems aim to enable on-demand connectivity through dynamic spectrum utilization. Motivated by this vision, this paper investigates the propagation characteristics and MIMO performance of the upper mid-band, spanning approximately 7-24 GHz and un... | https://arxiv.org/abs/2601.02903 | Academic Papers | svg |
8956ff9f82c3d555f468052ca99c1c6a71f17ad1937be30fc1a91ea31fbd5e47 | 2026-01-07T00:00:00-05:00 | LOST-3DSG: Lightweight Open-Vocabulary 3D Scene Graphs with Semantic Tracking in Dynamic Environments | arXiv:2601.02905v1 Announce Type: new Abstract: Tracking objects that move within dynamic environments is a core challenge in robotics. Recent research has advanced this topic significantly; however, many existing approaches remain inefficient due to their reliance on heavy foundation models. To address this limitation... | https://arxiv.org/abs/2601.02905 | Academic Papers | svg |
be8871b640c78f382f9c76c484115f3f7239a2d8598c4057328803ef11bbea94 | 2026-01-07T00:00:00-05:00 | Linear Script Representations in Speech Foundation Models Enable Zero-Shot Transliteration | arXiv:2601.02906v1 Announce Type: new Abstract: Multilingual speech foundation models such as Whisper are trained on web-scale data, where data for each language consists of a myriad of regional varieties. However, different regional varieties often employ different scripts to write the same language, rendering speech ... | https://arxiv.org/abs/2601.02906 | Academic Papers | svg |
0df67ce06b68a6ec6df1f96a3b1fc5e4d4f71fc78bb9e25156b662a32f44baa9 | 2026-01-07T00:00:00-05:00 | Beyond the Black Box: Theory and Mechanism of Large Language Models | arXiv:2601.02907v1 Announce Type: new Abstract: The rapid emergence of Large Language Models (LLMs) has precipitated a profound paradigm shift in Artificial Intelligence, delivering monumental engineering successes that increasingly impact modern society. However, a critical paradox persists within the current field: d... | https://arxiv.org/abs/2601.02907 | Academic Papers | svg |
bd49b679b83cac34c25fd99d710c14f0f0696e98e905e288c1fb39fee5d02f63 | 2026-01-07T00:00:00-05:00 | TA-Prompting: Enhancing Video Large Language Models for Dense Video Captioning via Temporal Anchors | arXiv:2601.02908v1 Announce Type: new Abstract: Dense video captioning aims to interpret and describe all temporally localized events throughout an input video. Recent state-of-the-art methods leverage large language models (LLMs) to provide detailed moment descriptions for video data. However, existing VideoLLMs remai... | https://arxiv.org/abs/2601.02908 | Academic Papers | svg |
eefb547cf3c0a7a8545c6ed81d914cc024e54813cade08f1fd8cae55e56cfadc | 2026-01-07T00:00:00-05:00 | Image, Word and Thought: A More Challenging Language Task for the Iterated Learning Model | arXiv:2601.02911v1 Announce Type: new Abstract: The iterated learning model simulates the transmission of language from generation to generation in order to explore how the constraints imposed by language transmission facilitate the emergence of language structure. Despite each modelled language learner starting from a... | https://arxiv.org/abs/2601.02911 | Academic Papers | svg |
093816e54984c00e2faf3fa52b1e01a2d7900b0df3be2a90d8242ea78438662f | 2026-01-07T00:00:00-05:00 | Vulnerabilities of Audio-Based Biometric Authentication Systems Against Deepfake Speech Synthesis | arXiv:2601.02914v1 Announce Type: new Abstract: As audio deepfakes transition from research artifacts to widely available commercial tools, robust biometric authentication faces pressing security threats in high-stakes industries. This paper presents a systematic empirical evaluation of state-of-the-art speaker authent... | https://arxiv.org/abs/2601.02914 | Academic Papers | svg |
cb3fa13f601dcd524008085f8ec87407d2b834ab9d1e5625d168fbf7cabf200c | 2026-01-07T00:00:00-05:00 | ChemBART: A Pre-trained BART Model Assisting Organic Chemistry Analysis | arXiv:2601.02915v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated transformative potential across diverse fields. While LLMs have been applied to molecular simplified molecular input line entry system (SMILES) in computer-aided synthesis planning (CASP), existing methodol... | https://arxiv.org/abs/2601.02915 | Academic Papers | svg |
b793bc297f68b530f53844b1739ecf3e9218830e7a725514629c372d34f20950 | 2026-01-07T00:00:00-05:00 | RAL2M: Retrieval Augmented Learning-To-Match Against Hallucination in Compliance-Guaranteed Service Systems | arXiv:2601.02917v1 Announce Type: new Abstract: Hallucination is a major concern in LLM-driven service systems, necessitating explicit knowledge grounding for compliance-guaranteed responses. In this paper, we introduce Retrieval-Augmented Learning-to-Match (RAL2M), a novel framework that eliminates generation hallucin... | https://arxiv.org/abs/2601.02917 | Academic Papers | svg |
c5afb1abca9d9da46a5c4d0692a37aa1353d7dc372bebd0d8838e38b6584d1d0 | 2026-01-07T00:00:00-05:00 | Zoom-IQA: Image Quality Assessment with Reliable Region-Aware Reasoning | arXiv:2601.02918v1 Announce Type: new Abstract: Image Quality Assessment (IQA) is a long-standing problem in computer vision. Previous methods typically focus on predicting numerical scores without explanation or provide low-level descriptions lacking precise scores. Recent reasoning-based vision language models (VLMs)... | https://arxiv.org/abs/2601.02918 | Academic Papers | svg |
b7b4cc2da2db7b98baff73a38fa95736cd11e260582e2a2f86dd4ab5cfa61015 | 2026-01-07T00:00:00-05:00 | Intersection patterns of set systems on manifolds with slowly growing homological shatter functions | arXiv:2601.02920v1 Announce Type: new Abstract: A theorem of Matou\v{s}ek asserts that for any $k \ge 2$, any set system whose shatter function is $o(n^k)$ enjoys a fractional Helly theorem: in the $k$-wise intersection hypergraph, positive density implies a linear-size clique. Kalai and Meshulam conjectured a generali... | https://arxiv.org/abs/2601.02920 | Academic Papers | svg |
ecafa32a97a6f401cdfd04f32d1d4a148abd72cdc3e534a0659b38a03d1f08d7 | 2026-01-07T00:00:00-05:00 | DCG ReID: Disentangling Collaboration and Guidance Fusion Representations for Multi-modal Vehicle Re-Identification | arXiv:2601.02924v1 Announce Type: new Abstract: Multi-modal vehicle Re-Identification (ReID) aims to leverage complementary information from RGB, Near Infrared (NIR), and Thermal Infrared (TIR) modalities to retrieve the same vehicle. The challenges of multi-modal vehicle ReID arise from the uncertainty of modality qua... | https://arxiv.org/abs/2601.02924 | Academic Papers | svg |
7dc3c82158c119cae7fcd7f67f9bd471c0a90dfd0f4a999a5749f4224d2a236a | 2026-01-07T00:00:00-05:00 | PrismVAU: Prompt-Refined Inference System for Multimodal Video Anomaly Understanding | arXiv:2601.02927v1 Announce Type: new Abstract: Video Anomaly Understanding (VAU) extends traditional Video Anomaly Detection (VAD) by not only localizing anomalies but also describing and reasoning about their context. Existing VAU approaches often rely on fine-tuned multimodal large language models (MLLMs) or externa... | https://arxiv.org/abs/2601.02927 | Academic Papers | svg |
2d2aa4754bb2c0ff0c2b9fd18d6b2677c165d9b494545a1a552ce3340554a781 | 2026-01-07T00:00:00-05:00 | HybridSolarNet: A Lightweight and Explainable EfficientNet-CBAM Architecture for Real-Time Solar Panel Fault Detection | arXiv:2601.02928v1 Announce Type: new Abstract: Manual inspections for solar panel systems are a tedious, costly, and error-prone task, making it desirable for Unmanned Aerial Vehicle (UAV) based monitoring. Though deep learning models have excellent fault detection capabilities, almost all methods either are too large... | https://arxiv.org/abs/2601.02928 | Academic Papers | svg |
d0df55f83f92e7084daf63615f3f323655007bb25ea416701ddb09e2da1e5947 | 2026-01-07T00:00:00-05:00 | Probabilistic Time Slot Leasing in TDMA-Based IoT Networks for Enhanced Channel Utilization | arXiv:2601.02930v1 Announce Type: new Abstract: In large-scale resource-constrained wireless networks, such as those prevalent in the Internet of Things (IoT), efficient communication scheduling remains a critical challenge. Among the various approaches, Time Division Multiple Access (TDMA) protocols have been widely a... | https://arxiv.org/abs/2601.02930 | Academic Papers | svg |
c7bd638baeae48f1f2ec9ecc516023177ef1ad692c15e29ebd8347dab72252e8 | 2026-01-07T00:00:00-05:00 | Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs | arXiv:2601.02931v1 Announce Type: new Abstract: Autoregressive LLMs perform well on relational tasks that require linking entities via relational words (e.g., father/son, friend), but it is unclear whether they learn the logical semantics of such relations (e.g., symmetry and inversion logic) and, if so, whether revers... | https://arxiv.org/abs/2601.02931 | Academic Papers | svg |
715b8eacc88a17aef86b193b0c4d723150424464014e4d25f89c2485de3cea53 | 2026-01-07T00:00:00-05:00 | Pearmut: Human Evaluation of Translation Made Trivial | arXiv:2601.02933v1 Announce Type: new Abstract: Human evaluation is the gold standard for multilingual NLP, but is often skipped in practice and substituted with automatic metrics, because it is notoriously complex and slow to set up with existing tools with substantial engineering and operational overhead. We introduc... | https://arxiv.org/abs/2601.02933 | Academic Papers | svg |
ab2f00c536844f04dfff974b3142c3ff6fa5406cf0583e9fd2f36e49ffca34b2 | 2026-01-07T00:00:00-05:00 | SastBench: A Benchmark for Testing Agentic SAST Triage | arXiv:2601.02941v1 Announce Type: new Abstract: SAST (Static Application Security Testing) tools are among the most widely used techniques in defensive cybersecurity, employed by commercial and non-commercial organizations to identify potential vulnerabilities in software. Despite their great utility, they generate num... | https://arxiv.org/abs/2601.02941 | Academic Papers | svg |
32281212d03ec505dcb560d2a0537f5c8d6ab763f0a11e56145ac5efa8d7c4cd | 2026-01-07T00:00:00-05:00 | MixTTE: Multi-Level Mixture-of-Experts for Scalable and Adaptive Travel Time Estimation | arXiv:2601.02943v1 Announce Type: new Abstract: Accurate Travel Time Estimation (TTE) is critical for ride-hailing platforms, where errors directly impact user experience and operational efficiency. While existing production systems excel at holistic route-level dependency modeling, they struggle to capture city-scale ... | https://arxiv.org/abs/2601.02943 | Academic Papers | svg |
ebeb37b665c4767235050ede395d0f567665614820ba4b580d1f700209892e8c | 2026-01-07T00:00:00-05:00 | VTONQA: A Multi-Dimensional Quality Assessment Dataset for Virtual Try-on | arXiv:2601.02945v1 Announce Type: new Abstract: With the rapid development of e-commerce and digital fashion, image-based virtual try-on (VTON) has attracted increasing attention. However, existing VTON models often suffer from artifacts such as garment distortion and body inconsistency, highlighting the need for relia... | https://arxiv.org/abs/2601.02945 | Academic Papers | svg |
7f8d80382e50dc6e7dffa3a8d59331e3ee02be6ec861656bf8de4e86b7d285c6 | 2026-01-07T00:00:00-05:00 | Quality Degradation Attack in Synthetic Data | arXiv:2601.02947v1 Announce Type: new Abstract: Synthetic Data Generation (SDG) can be used to facilitate privacy-preserving data sharing. However, most existing research focuses on privacy attacks where the adversary is the recipient of the released synthetic data and attempts to infer sensitive information from it. T... | https://arxiv.org/abs/2601.02947 | Academic Papers | svg |
41d16595b92381afbf1d52fb5ecd503052348b52ee22d7e5b02c2bbc9165f3b5 | 2026-01-07T00:00:00-05:00 | Parameter-Robust MPPI for Safe Online Learning of Unknown Parameters | arXiv:2601.02948v1 Announce Type: new Abstract: Robots deployed in dynamic environments must remain safe even when key physical parameters are uncertain or change over time. We propose Parameter-Robust Model Predictive Path Integral (PRMPPI) control, a framework that integrates online parameter learning with probabilis... | https://arxiv.org/abs/2601.02948 | Academic Papers | svg |
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