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ddd458190a4299939b992115330bc571edff52bdd4c476ae6532067d2d9d873a | 2026-01-21T00:00:00-05:00 | Conversational Context Classification: A Representation Engineering Approach | arXiv:2601.12286v1 Announce Type: new Abstract: The increasing prevalence of Large Language Models (LLMs) demands effective safeguards for their operation, particularly concerning their tendency to generate out-of-context responses. A key challenge is accurately detecting when LLMs stray from expected conversational no... | https://arxiv.org/abs/2601.12286 | Academic Papers | svg |
90cc193d62c6188227a736729044a4258c784361fe50bd74b0834f33d275b381 | 2026-01-21T00:00:00-05:00 | TimeGMM: Single-Pass Probabilistic Forecasting via Adaptive Gaussian Mixture Models with Reversible Normalization | arXiv:2601.12288v1 Announce Type: new Abstract: Probabilistic time series forecasting is crucial for quantifying future uncertainty, with significant applications in fields such as energy and finance. However, existing methods often rely on computationally expensive sampling or restrictive parametric assumptions to cha... | https://arxiv.org/abs/2601.12288 | Academic Papers | svg |
855481160c9b67611f3af0d81864fafe764bfa8a1ecfc3192d57a2d194eb901b | 2026-01-21T00:00:00-05:00 | ParaMETA: Towards Learning Disentangled Paralinguistic Speaking Styles Representations from Speech | arXiv:2601.12289v1 Announce Type: new Abstract: Learning representative embeddings for different types of speaking styles, such as emotion, age, and gender, is critical for both recognition tasks (e.g., cognitive computing and human-computer interaction) and generative tasks (e.g., style-controllable speech generation)... | https://arxiv.org/abs/2601.12289 | Academic Papers | svg |
9a0e25720980d414fcab93c09f796996d926e27359146596262f4959e08580fa | 2026-01-21T00:00:00-05:00 | Re-educating Educated Ones: A Case Study on Chakma Language Revitalization in Chittagong Hill Tracts | arXiv:2601.12290v1 Announce Type: new Abstract: Indigenous languages face significant cultural oppression from official state languages, particularly in the Global South. We investigate the Bangladeshi Chakma language revitalization movement, a community grappling with language liquidity and amalgamation into the domin... | https://arxiv.org/abs/2601.12290 | Academic Papers | svg |
229d2c93e0bc2ac4543209ef3ac4dd02e42a381bd9d2094e774e77b4a8ec5983 | 2026-01-21T00:00:00-05:00 | OpenNavMap: Structure-Free Topometric Mapping via Large-Scale Collaborative Localization | arXiv:2601.12291v1 Announce Type: new Abstract: Scalable and maintainable map representations are fundamental to enabling large-scale visual navigation and facilitating the deployment of robots in real-world environments. While collaborative localization across multi-session mapping enhances efficiency, traditional str... | https://arxiv.org/abs/2601.12291 | Academic Papers | svg |
76f44c152584e8d83290e903e1b64e327be1d12ae310a8706d8519b416bc1366 | 2026-01-21T00:00:00-05:00 | ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-using Agents | arXiv:2601.12294v1 Announce Type: new Abstract: Reward-guided search methods have demonstrated strong potential in enhancing tool-using agents by effectively guiding sampling and exploration over complex action spaces. As a core design, those search methods utilize process reward models (PRMs) to provide step-level rew... | https://arxiv.org/abs/2601.12294 | Academic Papers | svg |
10cd75b922fa901319faa10ee73d241eb49f52c04c6939e3edb86223c361da61 | 2026-01-21T00:00:00-05:00 | Distribution Shift Is Key to Learning Invariant Prediction | arXiv:2601.12296v1 Announce Type: new Abstract: An interesting phenomenon arises: Empirical Risk Minimization (ERM) sometimes outperforms methods specifically designed for out-of-distribution tasks. This motivates an investigation into the reasons behind such behavior beyond algorithmic design. In this study, we find t... | https://arxiv.org/abs/2601.12296 | Academic Papers | svg |
d1655f80b21f041d848eddfcdf49c7a32c799824935642bfc63c8ed6eea1b222 | 2026-01-21T00:00:00-05:00 | CD-PIM: A High-Bandwidth and Compute-Efficient LPDDR5-Based PIM for Low-Batch LLM Acceleration on Edge-Device | arXiv:2601.12298v1 Announce Type: new Abstract: Edge deployment of low-batch large language models (LLMs) faces critical memory bandwidth bottlenecks when executing memory-intensive general matrix-vector multiplications (GEMV) operations. While digital processing-in-memory (PIM) architectures promise to accelerate GEMV... | https://arxiv.org/abs/2601.12298 | Academic Papers | svg |
9817e007a699a8cfff51083f3c899a878b6e39a57f28a249851edd944999b02e | 2026-01-21T00:00:00-05:00 | "What If My Face Gets Scanned Without Consent": Understanding Older Adults' Experiences with Biometric Payment | arXiv:2601.12300v1 Announce Type: new Abstract: Biometric payment, i.e., biometric authentication implemented in digital payment systems, can reduce memory demands and streamline payment for older adults. However, older adults' perceptions and practices regarding biometric payment remain underexplored. We conducted sem... | https://arxiv.org/abs/2601.12300 | Academic Papers | svg |
df634c1c3e24d9ac11b17783925809ec6c0b4915324cb1e22d06eabd53d8cdce | 2026-01-21T00:00:00-05:00 | Facet-Aware Multi-Head Mixture-of-Experts Model with Text-Enhanced Pre-training for Sequential Recommendation | arXiv:2601.12301v1 Announce Type: new Abstract: Sequential recommendation (SR) systems excel at capturing users' dynamic preferences by leveraging their interaction histories. Most existing SR systems assign a single embedding vector to each item to represent its features, adopting various models to combine these embed... | https://arxiv.org/abs/2601.12301 | Academic Papers | svg |
f14e8e553fece5da78824c55de68507b64b0764376201f122fd563e50900a751 | 2026-01-21T00:00:00-05:00 | On the Minimum Length of Functional Batch Codes with Small Recovery Sets | arXiv:2601.12302v1 Announce Type: new Abstract: Batch codes are of potential use for load balancing and private information retrieval in distributed data storage systems. Recently, a special case of batch codes, termed functional batch codes, was proposed in the literature. In functional batch codes, users can query li... | https://arxiv.org/abs/2601.12302 | Academic Papers | svg |
3397b46b01a62cc5d43d0e9f5db561b0286dd1f01ca5b210becd44325cfb38ee | 2026-01-21T00:00:00-05:00 | Concepts from Representations: Post-hoc Concept Bottleneck Models via Sparse Decomposition of Visual Representations | arXiv:2601.12303v1 Announce Type: new Abstract: Deep learning has achieved remarkable success in image recognition, yet their inherent opacity poses challenges for deployment in critical domains. Concept-based interpretations aim to address this by explaining model reasoning through human-understandable concepts. Howev... | https://arxiv.org/abs/2601.12303 | Academic Papers | svg |
17dee31685b8c422564dcd5ec8b01ed94d0b3a11f857deaebf6231ab8910be21 | 2026-01-21T00:00:00-05:00 | A Two-Stage Globally-Diverse Adversarial Attack for Vision-Language Pre-training Models | arXiv:2601.12304v1 Announce Type: new Abstract: Vision-language pre-training (VLP) models are vulnerable to adversarial examples, particularly in black-box scenarios. Existing multimodal attacks often suffer from limited perturbation diversity and unstable multi-stage pipelines. To address these challenges, we propose ... | https://arxiv.org/abs/2601.12304 | Academic Papers | svg |
cbbf58cfb53dd9b2bfbd6a402759c84a5d34cd6c192885a82bf6cfa541066fd9 | 2026-01-21T00:00:00-05:00 | Machine Learning as a Service (MLaaS) Dataset Generator Framework for IoT Environments | arXiv:2601.12305v1 Announce Type: new Abstract: We propose a novel MLaaS Dataset Generator (MDG) framework that creates configurable and reproducible datasets for evaluating Machine Learning as a Service (MLaaS) selection and composition. MDG simulates realistic MLaaS behaviour by training and evaluating diverse model ... | https://arxiv.org/abs/2601.12305 | Academic Papers | svg |
f5daac0ade5df2da8119234235e4780ac2b3cb1b6e8471d40befbf324c216bd2 | 2026-01-21T00:00:00-05:00 | Rethinking the Value of Multi-Agent Workflow: A Strong Single Agent Baseline | arXiv:2601.12307v1 Announce Type: new Abstract: Recent advances in LLM-based multi-agent systems (MAS) show that workflows composed of multiple LLM agents with distinct roles, tools, and communication patterns can outperform single-LLM baselines on complex tasks. However, most frameworks are homogeneous, where all agen... | https://arxiv.org/abs/2601.12307 | Academic Papers | svg |
a9142fb4b920d57b03c88e79f2413ad3bf6d928533d14b97da6b5ead01c9d0b7 | 2026-01-21T00:00:00-05:00 | Adaptive Multi-Scale Correlation Meta-Network for Few-Shot Remote Sensing Image Classification | arXiv:2601.12308v1 Announce Type: new Abstract: Few-shot learning in remote sensing remains challenging due to three factors: the scarcity of labeled data, substantial domain shifts, and the multi-scale nature of geospatial objects. To address these issues, we introduce Adaptive Multi-Scale Correlation Meta-Network (AM... | https://arxiv.org/abs/2601.12308 | Academic Papers | svg |
5fe47c09a4dd3dffe9edb305b08a175acd4b3ff90b5938ecfc6c2b09d34af9b6 | 2026-01-21T00:00:00-05:00 | Survival is the Only Reward: Sustainable Self-Training Through Environment-Mediated Selection | arXiv:2601.12310v1 Announce Type: new Abstract: Self-training systems often degenerate due to the lack of an external criterion for judging data quality, leading to reward hacking and semantic drift. This paper provides a proof-of-concept system architecture for stable self-training under sparse external feedback and b... | https://arxiv.org/abs/2601.12310 | Academic Papers | svg |
31422991c48a80420b2e89c7af6ea4e7bbaa0cb43c3c564bd50a30ae98cd273d | 2026-01-21T00:00:00-05:00 | Cross-reality Location Privacy Protection in 6G-enabled Vehicular Metaverses: An LLM-enhanced Hybrid Generative Diffusion Model-based Approach | arXiv:2601.12311v1 Announce Type: new Abstract: The emergence of 6G-enabled vehicular metaverses enables Autonomous Vehicles (AVs) to operate across physical and virtual spaces through space-air-ground-sea integrated networks. The AVs can deploy AI agents powered by large AI models as personalized assistants, on edge s... | https://arxiv.org/abs/2601.12311 | Academic Papers | svg |
060986af4afeaf099db53850feff7dd42d486b8e1477e6f22bfd620b11bb53a8 | 2026-01-21T00:00:00-05:00 | CurConMix+: A Unified Spatio-Temporal Framework for Hierarchical Surgical Workflow Understanding | arXiv:2601.12312v1 Announce Type: new Abstract: Surgical action triplet recognition aims to understand fine-grained surgical behaviors by modeling the interactions among instruments, actions, and anatomical targets. Despite its clinical importance for workflow analysis and skill assessment, progress has been hindered b... | https://arxiv.org/abs/2601.12312 | Academic Papers | svg |
dbea6c8d39fcd06c1d5b9003b0944d441001336f6e7a94fe25f70f0033d02322 | 2026-01-21T00:00:00-05:00 | S^2F-Net:A Robust Spatial-Spectral Fusion Framework for Cross-Model AIGC Detection | arXiv:2601.12313v1 Announce Type: new Abstract: The rapid development of generative models has imposed an urgent demand for detection schemes with strong generalization capabilities. However, existing detection methods generally suffer from overfitting to specific source models, leading to significant performance degra... | https://arxiv.org/abs/2601.12313 | Academic Papers | svg |
ab1b4c3f5506396894b5357ea8dfb36d70d8dbe7e64c8e87ebbb5030a7757811 | 2026-01-21T00:00:00-05:00 | A Similarity Network for Correlating Musical Structure to Military Strategy | arXiv:2601.12314v1 Announce Type: new Abstract: Music perception, a multi-sensory process based on the synesthesia effect, is an essential component of music aesthetic education. Understanding music structure helps both perception and aesthetic education. Music structure incorporates a range of information, the coordin... | https://arxiv.org/abs/2601.12314 | Academic Papers | svg |
6a317eccfd4ba9ec924d36f77e5f8e0032cf89b5265a94dbf2b133cd957f22a3 | 2026-01-21T00:00:00-05:00 | GazeFormer-MoE: Context-Aware Gaze Estimation via CLIP and MoE Transformer | arXiv:2601.12316v1 Announce Type: new Abstract: We present a semantics modulated, multi scale Transformer for 3D gaze estimation. Our model conditions CLIP global features with learnable prototype banks (illumination, head pose, background, direction), fuses these prototype-enriched global vectors with CLIP patch token... | https://arxiv.org/abs/2601.12316 | Academic Papers | svg |
7a6b4d2a3736f523ded9b51f4dec6ec3fdd3a04d5480ccc38043a1ae9edb48a0 | 2026-01-21T00:00:00-05:00 | Explanova: Automatically Discover Data Insights in N \times M Table via XAI Combined LLM Workflow | arXiv:2601.12317v1 Announce Type: new Abstract: Automation in data analysis has been a long-time pursuit. Current agentic LLM shows a promising solution towards it. Like DeepAnalyze, DataSage, and Datawise. They are all powerful agentic frameworks for automatic fine-grained analysis and are powered by LLM-based agentic... | https://arxiv.org/abs/2601.12317 | Academic Papers | svg |
15ba463a770a267bad56998b79029577f9dd1c0b0922de2fa794dcf07240e310 | 2026-01-21T00:00:00-05:00 | Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligence | arXiv:2601.12318v1 Announce Type: new Abstract: The advancement of Document Intelligence (DI) demands large-scale, high-quality training data, yet manual annotation remains a critical bottleneck. While data generation methods are evolving rapidly, existing surveys are constrained by fragmented focuses on single modalit... | https://arxiv.org/abs/2601.12318 | Academic Papers | svg |
d163393be97e84163eb858ea5a12f4aa6c28657b821400481da0d2ecfad433f7 | 2026-01-21T00:00:00-05:00 | Ordered Local Momentum for Asynchronous Distributed Learning under Arbitrary Delays | arXiv:2601.12322v1 Announce Type: new Abstract: Momentum SGD (MSGD) serves as a foundational optimizer in training deep models due to momentum's key role in accelerating convergence and enhancing generalization. Meanwhile, asynchronous distributed learning is crucial for training large-scale deep models, especially whe... | https://arxiv.org/abs/2601.12322 | Academic Papers | svg |
bdec65bae73c3577c06950e0b55e880b73b71f3b5a5fb26a6cbe6684023e9350 | 2026-01-21T00:00:00-05:00 | MARO: Learning Stronger Reasoning from Social Interaction | arXiv:2601.12323v1 Announce Type: new Abstract: Humans face countless scenarios that require reasoning and judgment in daily life. However, existing large language model training methods primarily allow models to learn from existing textual content or solve predetermined problems, lacking experience in real scenarios i... | https://arxiv.org/abs/2601.12323 | Academic Papers | svg |
7816551508f3d72f0fe7443b985bae0eacbd9a68c0b67dd5d52b91a7450e76e3 | 2026-01-21T00:00:00-05:00 | Experiencer, Helper, or Observer: Online Fraud Intervention for Older Adults Through Role-based Simulation | arXiv:2601.12324v1 Announce Type: new Abstract: Online fraud is a critical global threat that disproportionately targets older adults. Prior anti-fraud education for older adults has largely relied on static, traditional instruction that limits engagement and real-world transfer, whereas role-based simulation offers re... | https://arxiv.org/abs/2601.12324 | Academic Papers | svg |
754303222375692c271eb2fc9913544ce310ac2af619cd1cac0e7fa5062b8cfd | 2026-01-21T00:00:00-05:00 | Multi-Sensor Matching with HyperNetworks | arXiv:2601.12325v1 Announce Type: new Abstract: Hypernetworks are models that generate or modulate the weights of another network. They provide a flexible mechanism for injecting context and task conditioning and have proven broadly useful across diverse applications without significant increases in model size. We leve... | https://arxiv.org/abs/2601.12325 | Academic Papers | svg |
aa137892adb5d31270bf42f87e85a43aa750e7c27c334a73bdf7395e1abd9ae6 | 2026-01-21T00:00:00-05:00 | EmoKGEdit: Training-free Affective Injection via Visual Cue Transformation | arXiv:2601.12326v1 Announce Type: new Abstract: Existing image emotion editing methods struggle to disentangle emotional cues from latent content representations, often yielding weak emotional expression and distorted visual structures. To bridge this gap, we propose EmoKGEdit, a novel training-free framework for preci... | https://arxiv.org/abs/2601.12326 | Academic Papers | svg |
3503453e71d479e1ee5758e9fd7a2bb1b23d25ef373e412d9a7f167b35aad1d3 | 2026-01-21T00:00:00-05:00 | The Expert Validation Framework (EVF): Enabling Domain Expert Control in AI Engineering | arXiv:2601.12327v1 Announce Type: new Abstract: Generative AI (GenAI) systems promise to transform knowledge work by automating a range of tasks, yet their deployment in enterprise settings remains hindered by the lack of systematic quality assurance mechanisms. We present an Expert Validation Framework that places dom... | https://arxiv.org/abs/2601.12327 | Academic Papers | svg |
dedad7a9e7ab8f0b56da8682d06676132697d9cd1250c46e6719fba3c236703f | 2026-01-21T00:00:00-05:00 | FlowIID: Single-Step Intrinsic Image Decomposition via Latent Flow Matching | arXiv:2601.12329v1 Announce Type: new Abstract: Intrinsic Image Decomposition (IID) separates an image into albedo and shading components. It is a core step in many real-world applications, such as relighting and material editing. Existing IID models achieve good results, but often use a large number of parameters. Thi... | https://arxiv.org/abs/2601.12329 | Academic Papers | svg |
5316978e70dc6d87f58102500692f22ed679cfb1a27efd8f721c4e68874355b8 | 2026-01-21T00:00:00-05:00 | IceWatch: Forecasting Glacial Lake Outburst Floods (GLOFs) using Multimodal Deep Learning | arXiv:2601.12330v1 Announce Type: new Abstract: Glacial Lake Outburst Floods (GLOFs) pose a serious threat in high mountain regions. They are hazardous to communities, infrastructure, and ecosystems further downstream. The classical methods of GLOF detection and prediction have so far mainly relied on hydrological mode... | https://arxiv.org/abs/2601.12330 | Academic Papers | svg |
6ca5f405cc82c9dc49a3f2c37c3c1268dec4340c3e0f278ddc872846c466be45 | 2026-01-21T00:00:00-05:00 | Efficient Privacy-Preserving Retrieval Augmented Generation with Distance-Preserving Encryption | arXiv:2601.12331v1 Announce Type: new Abstract: RAG has emerged as a key technique for enhancing response quality of LLMs without high computational cost. In traditional architectures, RAG services are provided by a single entity that hosts the dataset within a trusted local environment. However, individuals or small o... | https://arxiv.org/abs/2601.12331 | Academic Papers | svg |
ba019b94e9bbb252745ce204d1d0dd4c9c7f9ddbb705f0d5a37608b6e26c15b0 | 2026-01-21T00:00:00-05:00 | Worst-case Nonlinear Regression with Error Bounds | arXiv:2601.12334v1 Announce Type: new Abstract: This paper proposes an active-learning approach to worst-case nonlinear regression with deterministic error guarantees. Given a known nonlinear function defined over a compact set, we compute a surrogate model, such as a feedforward neural network, by minimizing the maxim... | https://arxiv.org/abs/2601.12334 | Academic Papers | svg |
c6a17357be95c9b0b4649fd0ca2557b0c522bb816af0a6d8f009814578317f1a | 2026-01-21T00:00:00-05:00 | Turbo-GoDec: Exploiting the Cluster Sparsity Prior for Hyperspectral Anomaly Detection | arXiv:2601.12337v1 Announce Type: new Abstract: As a key task in hyperspectral image processing, hyperspectral anomaly detection has garnered significant attention and undergone extensive research. Existing methods primarily relt on two prior assumption: low-rank background and sparse anomaly, along with additional spa... | https://arxiv.org/abs/2601.12337 | Academic Papers | svg |
e33bbd6510bfbfff7926ab1c2727d405117c3e26d3031f9bb1aa7a77e3c3cedb | 2026-01-21T00:00:00-05:00 | Actionable Advice from Reviews via Mixture of LoRA Experts: A Two-LLM Pipeline for Issue Extraction and Business Recommendations | arXiv:2601.12338v1 Announce Type: new Abstract: Customer reviews contain detailed, domain specific signals about service failures and user expectations, but converting this unstructured feedback into actionable business decisions remains difficult. We study review-to-action generation: producing concrete, implementable... | https://arxiv.org/abs/2601.12338 | Academic Papers | svg |
713fcb815cccb94fc9383c823225199ae4538b6ec4eff559fb0b20a48e44b942 | 2026-01-21T00:00:00-05:00 | Time-Continuous Modeling for Temporal Affective Pattern Recognition in LLMs | arXiv:2601.12341v1 Announce Type: new Abstract: This paper introduces a dataset and conceptual framework for LLMs to mimic real world emotional dynamics through time and in-context learning leveraging physics-informed neural network, opening a possibility for interpretable dialogue modeling. | https://arxiv.org/abs/2601.12341 | Academic Papers | svg |
1f20bf250c47cbc4222d5ddc3a66be4b0b3f51286c921ebe2160760447a649e0 | 2026-01-21T00:00:00-05:00 | MMDeepResearch-Bench: A Benchmark for Multimodal Deep Research Agents | arXiv:2601.12346v1 Announce Type: new Abstract: Deep Research Agents (DRAs) generate citation-rich reports via multi-step search and synthesis, yet existing benchmarks mainly target text-only settings or short-form multimodal QA, missing end-to-end multimodal evidence use. We introduce MMDeepResearch-Bench (MMDR-Bench)... | https://arxiv.org/abs/2601.12346 | Academic Papers | svg |
895dd2612f8623fe006e6f289c156e5b0f5e1bfa6e03725981c995c6f9b7cf6d | 2026-01-21T00:00:00-05:00 | RIPPLE++: An Incremental Framework for Efficient GNN Inference on Evolving Graphs | arXiv:2601.12347v1 Announce Type: new Abstract: Real-world graphs are dynamic, with frequent updates to their structure and features due to evolving vertex and edge properties. These continual changes pose significant challenges for efficient inference in graph neural networks (GNNs). Existing vertex-wise and layer-wis... | https://arxiv.org/abs/2601.12347 | Academic Papers | svg |
6d12365fe4eb57c9b1ee12b549639f0ff159370f651c5e3b6f5389e42b39e647 | 2026-01-21T00:00:00-05:00 | Generative AI Agents for Controllable and Protected Content Creation | arXiv:2601.12348v1 Announce Type: new Abstract: The proliferation of generative AI has transformed creative workflows, yet current systems face critical challenges in controllability and content protection. We propose a novel multi-agent framework that addresses both limitations through specialized agent roles and inte... | https://arxiv.org/abs/2601.12348 | Academic Papers | svg |
d18a0bea5e86623e55db9b25b06bcb2aad76599bc8f5c5866685c804116328b5 | 2026-01-21T00:00:00-05:00 | Zero-Permission Manipulation: Can We Trust Large Multimodal Model Powered GUI Agents? | arXiv:2601.12349v1 Announce Type: new Abstract: Large multimodal model powered GUI agents are emerging as high-privilege operators on mobile platforms, entrusted with perceiving screen content and injecting inputs. However, their design operates under the implicit assumption of Visual Atomicity: that the UI state remai... | https://arxiv.org/abs/2601.12349 | Academic Papers | svg |
ce023a68f51944ef45db20d44ddf47571313b63f2a8303ca0ba89874e85afb0f | 2026-01-21T00:00:00-05:00 | Analyzing Collection Strategies: A Computational Perspective on the Coupon Collector Problem | arXiv:2601.12351v1 Announce Type: new Abstract: The Coupon Collector Problem (CCP) is a well-known combinatorial problem that seeks to estimate the number of random draws required to complete a collection of $n$ distinct coupon types. Various generalizations of this problem have been applied in numerous engineering dom... | https://arxiv.org/abs/2601.12351 | Academic Papers | svg |
fd988eb7020f425ea937d8f798cdc62ad7b90ac0d3f647e33842e46a50feb7d1 | 2026-01-21T00:00:00-05:00 | From Shallow Waters to Mariana Trench: A Survey of Bio-inspired Underwater Soft Robots | arXiv:2601.12353v1 Announce Type: new Abstract: Sample Exploring the ocean environment holds profound significance in areas such as resource exploration and ecological protection. Underwater robots struggle with extreme water pressure and often cause noise and damage to the underwater ecosystem, while bio-inspired soft... | https://arxiv.org/abs/2601.12353 | Academic Papers | svg |
6fced5f97bfc20eff1c17ead1184d69ab3220ef92cefdfe005069e122f6739bf | 2026-01-21T00:00:00-05:00 | LB-MCTS: Synergizing Large Language Models and Bayesian Optimization for Efficient CASH | arXiv:2601.12355v1 Announce Type: new Abstract: To lower the expertise barrier in machine learning, the AutoML community has focused on the CASH problem, a fundamental challenge that automates the process of algorithm selection and hyperparameter tuning. While traditional methods like Bayesian Optimization (BO) struggl... | https://arxiv.org/abs/2601.12355 | Academic Papers | svg |
651b53d356b47baa023009a14f9c8c3692572ef54cef8a0d1b7c2f9e48f6e3e3 | 2026-01-21T00:00:00-05:00 | SimpleMatch: A Simple and Strong Baseline for Semantic Correspondence | arXiv:2601.12357v1 Announce Type: new Abstract: Recent advances in semantic correspondence have been largely driven by the use of pre-trained large-scale models. However, a limitation of these approaches is their dependence on high-resolution input images to achieve optimal performance, which results in considerable co... | https://arxiv.org/abs/2601.12357 | Academic Papers | svg |
61bf0e5fc130c05e5b5bee58466e0b667d38f131f93a9383b118c1bf59ad3e81 | 2026-01-21T00:00:00-05:00 | From Prompts to Pavement: LMMs-based Agentic Behavior-Tree Generation Framework for Autonomous Vehicles | arXiv:2601.12358v1 Announce Type: new Abstract: Autonomous vehicles (AVs) require adaptive behavior planners to navigate unpredictable, real-world environments safely. Traditional behavior trees (BTs) offer structured decision logic but are inherently static and demand labor-intensive manual tuning, limiting their appl... | https://arxiv.org/abs/2601.12358 | Academic Papers | svg |
b348a2fd892780723ae607f2376ca58d8296cb03d1c244eb71720bfd95f2cfa8 | 2026-01-21T00:00:00-05:00 | Zero-Shot Embedding Drift Detection: A Lightweight Defense Against Prompt Injections in LLMs | arXiv:2601.12359v1 Announce Type: new Abstract: Prompt injection attacks have become an increasing vulnerability for LLM applications, where adversarial prompts exploit indirect input channels such as emails or user-generated content to circumvent alignment safeguards and induce harmful or unintended outputs. Despite a... | https://arxiv.org/abs/2601.12359 | Academic Papers | svg |
545e6075d8e16754c1996f9d52353b3ea64d1f6224d2c30105ee7cac7f3dabf1 | 2026-01-21T00:00:00-05:00 | Discovering 100+ Compiler Defects in 72 Hours via LLM-Driven Semantic Logic Recomposition | arXiv:2601.12360v1 Announce Type: new Abstract: Compilers constitute the foundational root-of-trust in software supply chains; however, their immense complexity inevitably conceals critical defects. Recent research has attempted to leverage historical bugs to design new mutation operators or fine-tune models to increas... | https://arxiv.org/abs/2601.12360 | Academic Papers | svg |
da2a35db6f5745350bb795efa06a12a0c2b6ae53790a57f22aee8b4bc12c78b4 | 2026-01-21T00:00:00-05:00 | Complexity of Model Checking Second-Order Hyperproperties on Finite Structures | arXiv:2601.12361v1 Announce Type: new Abstract: We study the model checking problem of Hyper2LTL over finite structures. Hyper2LTL is a second-order hyperlogic, that extends the well-studied logic HyperLTL by adding quantification over sets of traces, to express complex hyperproperties such as epistemic and asynchronou... | https://arxiv.org/abs/2601.12361 | Academic Papers | svg |
dcd4c6bb69c4f68f421e07f9970358ec184cf7a24f74e8417526e8f63055f25b | 2026-01-21T00:00:00-05:00 | Machine Learning-Based Framework for Real Time Detection and Early Prediction of Control Valve Stiction in Industrial Control Systems | arXiv:2601.12362v1 Announce Type: new Abstract: Control valve stiction, a friction that prevents smooth valve movement, is a common fault in industrial process systems that causes instability, equipment wear, and higher maintenance costs. Many plants still operate with conventional valves that lack real time monitoring... | https://arxiv.org/abs/2601.12362 | Academic Papers | svg |
cbe79dc6f6421b6a0c552a3fea9d6528707234c4a25ca0097185312a58c9ba3d | 2026-01-21T00:00:00-05:00 | DepthCropSeg++: Scaling a Crop Segmentation Foundation Model With Depth-Labeled Data | arXiv:2601.12366v1 Announce Type: new Abstract: DepthCropSeg++: a foundation model for crop segmentation, capable of segmenting different crop species under open in-field environment. Crop segmentation is a fundamental task for modern agriculture, which closely relates to many downstream tasks such as plant phenotyping... | https://arxiv.org/abs/2601.12366 | Academic Papers | svg |
556d252c20af6514db4e59d75efb13e5f25eb4f9a87f7edb711c910f03982f7f | 2026-01-21T00:00:00-05:00 | User-to-Vehicle Interaction in Smart Mobility: The GO-DRiVeS Autonomous Ride-Sharing Application | arXiv:2601.12367v1 Announce Type: new Abstract: This paper introduces the GO-DRiVeS application, an on demand ride sharing and requesting mobile application tailored specifically to save long walks and challenges which are time consuming and tiring especially during hot days or when carrying heavy items, faced by unive... | https://arxiv.org/abs/2601.12367 | Academic Papers | svg |
3729f37aa48973906ac9efeee6357c887de0365efb320aef734f9dce884d2765 | 2026-01-21T00:00:00-05:00 | Can Deep Research Agents Find and Organize? Evaluating the Synthesis Gap with Expert Taxonomies | arXiv:2601.12369v1 Announce Type: new Abstract: Deep Research Agents are increasingly used for automated survey generation. However, whether they can write surveys like human experts remains unclear. Existing benchmarks focus on fluency or citation accuracy, but none evaluates the core capabilities: retrieving essentia... | https://arxiv.org/abs/2601.12369 | Academic Papers | svg |
a889e7130e92f4b2675472757d103cfeb4938798106aafb70ee38707eecdd6fd | 2026-01-21T00:00:00-05:00 | CD-TWINSAFE: A ROS-enabled Digital Twin for Scene Understanding and Safety Emerging V2I Technology | arXiv:2601.12373v1 Announce Type: new Abstract: In this paper, the CD-TWINSAFE is introduced, a V2I-based digital twin for Autonomous Vehicles. The proposed architecture is composed of two stacks running simultaneously, an on-board driving stack that includes a stereo camera for scene understanding, and a digital twin ... | https://arxiv.org/abs/2601.12373 | Academic Papers | svg |
9856b32e60ce05011eefeb5ee518901757cc7a0ed97a792cf4b5304c9c58ee8d | 2026-01-21T00:00:00-05:00 | A Scalable Entity-Based Framework for Auditing Bias in LLMs | arXiv:2601.12374v1 Announce Type: new Abstract: Existing approaches to bias evaluation in large language models (LLMs) trade ecological validity for statistical control, relying on artificial prompts that poorly reflect real-world use, or on naturalistic tasks that lack scale and rigor. We introduce a scalable bias-aud... | https://arxiv.org/abs/2601.12374 | Academic Papers | svg |
b550c71c5d5dfadb76a90de31161782a99443d91954f0186e072f997134dccc6 | 2026-01-21T00:00:00-05:00 | LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G | arXiv:2601.12375v1 Announce Type: new Abstract: Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Near-Real-Time (Near-RT) latency and computational constraints. While Transformer-based models are effective for sequence modeling, ... | https://arxiv.org/abs/2601.12375 | Academic Papers | svg |
6fed2da31e4b3e52da18bcfb135de4b6f69383f1f58df81e2754b9d891ed3b88 | 2026-01-21T00:00:00-05:00 | LR-DWM: Efficient Watermarking for Diffusion Language Models | arXiv:2601.12376v1 Announce Type: new Abstract: Watermarking (WM) is a critical mechanism for detecting and attributing AI-generated content. Current WM methods for Large Language Models (LLMs) are predominantly tailored for autoregressive (AR) models: They rely on tokens being generated sequentially, and embed stable ... | https://arxiv.org/abs/2601.12376 | Academic Papers | svg |
2ca324d837510f88419cc86a075baadb01e972c3c84b620f1071b96989877bdc | 2026-01-21T00:00:00-05:00 | R-VoxelMap: Accurate Voxel Mapping with Recursive Plane Fitting for Online LiDAR Odometry | arXiv:2601.12377v1 Announce Type: new Abstract: This paper proposes R-VoxelMap, a novel voxel mapping method that constructs accurate voxel maps using a geometry-driven recursive plane fitting strategy to enhance the localization accuracy of online LiDAR odometry. VoxelMap and its variants typically fit and check plane... | https://arxiv.org/abs/2601.12377 | Academic Papers | svg |
c31913f1aee09cb49c103bb3622b5aa2b9a21ec6f00d10b5027a8ae7d052a4fc | 2026-01-21T00:00:00-05:00 | Utilizing the Score of Data Distribution for Hyperspectral Anomaly Detection | arXiv:2601.12379v1 Announce Type: new Abstract: Hyperspectral images (HSIs) are a type of image that contains abundant spectral information. As a type of real-world data, the high-dimensional spectra in hyperspectral images are actually determined by only a few factors, such as chemical composition and illumination. Th... | https://arxiv.org/abs/2601.12379 | Academic Papers | svg |
d4ff16adef9c6352266fe6977a5cb0997840f8a61f02dec6dee1785d5464369f | 2026-01-21T00:00:00-05:00 | Statistical-Neural Interaction Networks for Interpretable Mixed-Type Data Imputation | arXiv:2601.12380v1 Announce Type: new Abstract: Real-world tabular databases routinely combine continuous measurements and categorical records, yet missing entries are pervasive and can distort downstream analysis. We propose Statistical-Neural Interaction (SNI), an interpretable mixed-type imputation framework that co... | https://arxiv.org/abs/2601.12380 | Academic Papers | svg |
19fe2f47b5035840485864a0960e1b9a499a25bbbad0d7e1547facc12f4a0d3f | 2026-01-21T00:00:00-05:00 | A Hierarchical Benchmark of Foundation Models for Dermatology | arXiv:2601.12382v1 Announce Type: new Abstract: Foundation models have transformed medical image analysis by providing robust feature representations that reduce the need for large-scale task-specific training. However, current benchmarks in dermatology often reduce the complex diagnostic taxonomy to flat, binary class... | https://arxiv.org/abs/2601.12382 | Academic Papers | svg |
2fe3069604bee6213be9fe2fe26ccd0e0aba5bd7a67bf91252f0d72773d52281 | 2026-01-21T00:00:00-05:00 | Context-Free Grammar Inference for Complex Programming Languages in Black Box Settings | arXiv:2601.12385v1 Announce Type: new Abstract: Grammar inference for complex programming languages remains a significant challenge, as existing approaches fail to scale to real world datasets within practical time constraints. In our experiments, none of the state-of-the-art tools, including Arvada, Treevada and Kedav... | https://arxiv.org/abs/2601.12385 | Academic Papers | svg |
748e312d9402cac6c1af89a9a1e81dedaf4d2eba487d49250c72d592624d4450 | 2026-01-21T00:00:00-05:00 | NADIR: Differential Attention Flow for Non-Autoregressive Transliteration in Indic Languages | arXiv:2601.12389v1 Announce Type: new Abstract: In this work, we argue that not all sequence-to-sequence tasks require the strong inductive biases of autoregressive (AR) models. Tasks like multilingual transliteration, code refactoring, grammatical correction or text normalization often rely on local dependencies where... | https://arxiv.org/abs/2601.12389 | Academic Papers | svg |
0257622480e0c12e126d7e53262d505d948e5fd61e6e826c3b4a2b40e2563fed | 2026-01-21T00:00:00-05:00 | Auditing Meta and TikTok Research API Data Access under Article 40(12) of the Digital Services Act | arXiv:2601.12390v1 Announce Type: new Abstract: Article 40(12) of the Digital Services Act (DSA) requires Very Large Online Platforms (VLOPs) to provide vetted researchers with access to publicly accessible data. While prior work has identified shortcomings of platform-provided data access mechanisms, existing research... | https://arxiv.org/abs/2601.12390 | Academic Papers | svg |
b698cb7e01f82751afdd5eee38af2733f29a0b3643f3cd13ae1c0ff8252aab47 | 2026-01-21T00:00:00-05:00 | Class-Partitioned VQ-VAE and Latent Flow Matching for Point Cloud Scene Generation | arXiv:2601.12391v1 Announce Type: new Abstract: Most 3D scene generation methods are limited to only generating object bounding box parameters while newer diffusion methods also generate class labels and latent features. Using object size or latent feature, they then retrieve objects from a predefined database. For com... | https://arxiv.org/abs/2601.12391 | Academic Papers | svg |
5db14d4a8eac830ec8a8504e0e44d9e8a7e8778e972bdc8251c502109b26f054 | 2026-01-21T00:00:00-05:00 | Psych\=eChat: An Empathic Framework Focused on Emotion Shift Tracking and Safety Risk Analysis in Psychological Counseling | arXiv:2601.12392v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated notable advancements in psychological counseling. However, existing models generally do not explicitly model seekers' emotion shifts across counseling sessions, a core focus in classical psychological schools. Moreover, how t... | https://arxiv.org/abs/2601.12392 | Academic Papers | svg |
d4cd6b1ff59a3d9afe73aa4a0d44e98d8a669505f3f7843f6e63a872330893eb | 2026-01-21T00:00:00-05:00 | $2$-quasi-perfect Lee codes and abelian Ramanujan graphs: a new construction and relationship | arXiv:2601.12393v1 Announce Type: new Abstract: In this paper, we obtain a new explicit family of $2$-quasi-perfect Lee codes of arbitrarily large length. Our construction is based on generating sets of abelian (almost) Ramanujan graphs obtained by Forey, Fres\'{a}n, Kowalski and Wigderson. Also, we develop a relations... | https://arxiv.org/abs/2601.12393 | Academic Papers | svg |
68a5d4ede736fb2fcfc422f386f3a55f32c8d383bcc368714d07a58b5559268d | 2026-01-21T00:00:00-05:00 | Privacy via Modulation Rotation and Inter-Symbol Interference | arXiv:2601.12394v1 Announce Type: new Abstract: Two physical-layer mechanisms for achieving user-side differential privacy in communication systems are proposed. Focusing on binary phase-shift keying (BPSK) modulation, differential privacy (DP) is first studied under a deterministic phase rotation applied on the BPSK m... | https://arxiv.org/abs/2601.12394 | Academic Papers | svg |
b52db0a6525cb4c9b805e9ff80b61375acd1da9b7a45dce3429b389b8abad2fa | 2026-01-21T00:00:00-05:00 | VR$^2$: A Co-Located Dual-Headset Platform for Touch-Enabled Human-Robot Interaction Research | arXiv:2601.12395v1 Announce Type: new Abstract: Touch-rich human-robot interaction (HRI) is difficult to study: building and programming physical robots is costly and slow, while VR-based robot prototypes often remove physical contact or break the tight coupling between an agent's body and the user's felt touch. We pre... | https://arxiv.org/abs/2601.12395 | Academic Papers | svg |
5870350c6ef47503563027a3994af9b0974d1a32b188ed2fa398a6f3b6d6c92e | 2026-01-21T00:00:00-05:00 | Learning Diverse Skills for Behavior Models with Mixture of Experts | arXiv:2601.12397v1 Announce Type: new Abstract: Imitation learning has demonstrated strong performance in robotic manipulation by learning from large-scale human demonstrations. While existing models excel at single-task learning, it is observed in practical applications that their performance degrades in the multi-tas... | https://arxiv.org/abs/2601.12397 | Academic Papers | svg |
b1eb6dbbbac3b8e0f3c0191e3ca90412b06d802fd27b565db975d803b18eb534 | 2026-01-21T00:00:00-05:00 | Beyond the Dirac Delta: Mitigating Diversity Collapse in Reinforcement Fine-Tuning for Versatile Image Generation | arXiv:2601.12401v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow models, to align with complex human preferences and user-specified tasks. A fundamental limitation remains \textit{the curse of diversi... | https://arxiv.org/abs/2601.12401 | Academic Papers | svg |
e234011785ee78ba0b1d196b9c70656a710b90a4b036c1eac781cbd8d7513ad0 | 2026-01-21T00:00:00-05:00 | Weaknesses of Facial Emotion Recognition Systems | arXiv:2601.12402v1 Announce Type: new Abstract: Emotion detection from faces is one of the machine learning problems needed for human-computer interaction. The variety of methods used is enormous, which motivated an in-depth review of articles and scientific studies. Three of the most interesting and best solutions are... | https://arxiv.org/abs/2601.12402 | Academic Papers | svg |
7193f42c410bebe87a2da05dd7a2d5c9edc0ce919089741ddd0f72eae8ee9f15 | 2026-01-21T00:00:00-05:00 | Explainable Machine Learning for Pediatric Dental Risk Stratification Using Socio-Demographic Determinants | arXiv:2601.12405v1 Announce Type: new Abstract: Background: Pediatric dental disease remains one of the most prevalent and inequitable chronic health conditions worldwide. Although strong epidemiological evidence links oral health outcomes to socio-economic and demographic determinants, most artificial intelligence (AI... | https://arxiv.org/abs/2601.12405 | Academic Papers | svg |
31dd5486d8040044e470eebf1f4ce63806369da5f950f681266c5be6d59893c1 | 2026-01-21T00:00:00-05:00 | De-Anonymization at Scale via Tournament-Style Attribution | arXiv:2601.12407v1 Announce Type: new Abstract: As LLMs rapidly advance and enter real-world use, their privacy implications are increasingly important. We study an authorship de-anonymization threat: using LLMs to link anonymous documents to their authors, potentially compromising settings such as double-blind peer re... | https://arxiv.org/abs/2601.12407 | Academic Papers | svg |
60b4debc694dbcef47b3c9d3d1c595463778a51ae773f38a808c4780436a9877 | 2026-01-21T00:00:00-05:00 | Are LLMs Smarter Than Chimpanzees? An Evaluation on Perspective Taking and Knowledge State Estimation | arXiv:2601.12410v1 Announce Type: new Abstract: Cognitive anthropology suggests that the distinction of human intelligence lies in the ability to infer other individuals' knowledge states and understand their intentions. In comparison, our closest animal relative, chimpanzees, lack the capacity to do so. With this pape... | https://arxiv.org/abs/2601.12410 | Academic Papers | svg |
01979acbd574c0410f251792682ab0363b59910c6e72fd671264399462fb4745 | 2026-01-21T00:00:00-05:00 | Orthogonalized Policy Optimization:Decoupling Sampling Geometry from Optimization Geometry in RLHF | arXiv:2601.12415v1 Announce Type: new Abstract: Recent alignment methods for large language models, including PPO, DPO, and IPO, are often presented as distinct algorithms. In this work, we show that many of these approaches implicitly conflate two fundamental and independent design choices: (i) the sampling geometry, ... | https://arxiv.org/abs/2601.12415 | Academic Papers | svg |
0b77da6b2290e229ff9747e7856878257abdab2ac2d09a0f650babee008bf0af | 2026-01-21T00:00:00-05:00 | RLMiner: Finding the Most Frequent k-sized Subgraph via Reinforcement Learning | arXiv:2601.12416v1 Announce Type: new Abstract: Identifying the most frequent induced subgraph of size $k$ in a target graph is a fundamental graph mining problem with direct implications for Web-related data mining and social network analysis. Despite its importance, finding the most frequent induced subgraph remains ... | https://arxiv.org/abs/2601.12416 | Academic Papers | svg |
c2dba4bd17f341c0b6fbaf45efd80b4d10cda8ef78092a6c9dc9f404f9cadc81 | 2026-01-21T00:00:00-05:00 | Legal experts disagree with rationale extraction techniques for explaining ECtHR case outcome classification | arXiv:2601.12419v1 Announce Type: new Abstract: Interpretability is critical for applications of large language models in the legal domain which requires trust and transparency. While some studies develop task-specific approaches, other use the classification model's parameters to explain the decisions. However, which ... | https://arxiv.org/abs/2601.12419 | Academic Papers | svg |
8b78656d248d101ba90607c0fea766ecc6d9305baf3d07617110f848ed9d37f5 | 2026-01-21T00:00:00-05:00 | HOT-POT: Optimal Transport for Sparse Stereo Matching | arXiv:2601.12423v1 Announce Type: new Abstract: Stereo vision between images faces a range of challenges, including occlusions, motion, and camera distortions, across applications in autonomous driving, robotics, and face analysis. Due to parameter sensitivity, further complications arise for stereo matching with spars... | https://arxiv.org/abs/2601.12423 | Academic Papers | svg |
cda070c2f931d644279801444d5e19fe128f27847220a4f5867b3a0d87a81b66 | 2026-01-21T00:00:00-05:00 | Graph Attention Networks with Physical Constraints for Anomaly Detection | arXiv:2601.12426v1 Announce Type: new Abstract: Water distribution systems (WDSs) face increasing cyber-physical risks, which make reliable anomaly detection essential. Many data-driven models ignore network topology and are hard to interpret, while model-based ones depend strongly on parameter accuracy. This work prop... | https://arxiv.org/abs/2601.12426 | Academic Papers | svg |
28d05166f005b41cd49462d57926d1f842d76f9697cdc559a1dacc4b3c59c6d0 | 2026-01-21T00:00:00-05:00 | Counterexamples, Constructions, and Nonexistence Results for Optimal Ternary Cyclic Codes | arXiv:2601.12427v1 Announce Type: new Abstract: Cyclic codes are an important subclass of linear codes with wide applications in communication systems and data storage systems. In 2013, Ding and Helleseth presented nine open problems on optimal ternary cyclic codes $\mathcal{C}_{(1,e)}$. While the first two and the six... | https://arxiv.org/abs/2601.12427 | Academic Papers | svg |
9fde9c51a204ec4ccf3644d0acb5ec28ea7fa9091ed85e6547e15dd8dd5a5099 | 2026-01-21T00:00:00-05:00 | ReWorld: Multi-Dimensional Reward Modeling for Embodied World Models | arXiv:2601.12428v1 Announce Type: new Abstract: Recently, video-based world models that learn to simulate the dynamics have gained increasing attention in robot learning. However, current approaches primarily emphasize visual generative quality while overlooking physical fidelity, dynamic consistency, and task logic, e... | https://arxiv.org/abs/2601.12428 | Academic Papers | svg |
6bb89c40238136eed33a6ab35f0d4b32c56e43184197cb6578340888ee5df052 | 2026-01-21T00:00:00-05:00 | System-Mediated Attention Imbalances Make Vision-Language Models Say Yes | arXiv:2601.12430v1 Announce Type: new Abstract: Vision-language model (VLM) hallucination is commonly linked to imbalanced allocation of attention across input modalities: system, image and text. However, existing mitigation strategies tend towards an image-centric interpretation of these imbalances, often prioritising... | https://arxiv.org/abs/2601.12430 | Academic Papers | svg |
6c566d40ae9e18d97cb25cb0ab971751b46dc04fab1bd45731c3140c0ad400e3 | 2026-01-21T00:00:00-05:00 | SkeFi: Cross-Modal Knowledge Transfer for Wireless Skeleton-Based Action Recognition | arXiv:2601.12432v1 Announce Type: new Abstract: Skeleton-based action recognition leverages human pose keypoints to categorize human actions, which shows superior generalization and interoperability compared to regular end-to-end action recognition. Existing solutions use RGB cameras to annotate skeletal keypoints, but... | https://arxiv.org/abs/2601.12432 | Academic Papers | svg |
6d383011f2f155e379b82fd5adb0147534c55d7be9ed1be22a0fb26b2619384f | 2026-01-21T00:00:00-05:00 | ASAS-BridgeAMM: Trust-Minimized Cross-Chain Bridge AMM with Failure Containment | arXiv:2601.12434v1 Announce Type: new Abstract: Cross-chain bridges constitute the single largest vector of systemic risk in Decentralized Finance (DeFi), accounting for over \$2.8 billion in losses since 2021. The fundamental vulnerability lies in the binary nature of existing bridge security models: a bridge is eithe... | https://arxiv.org/abs/2601.12434 | Academic Papers | svg |
bb0b1c397160946b65ef62793057cfef9dd7d5beb389f4e61813fdb93648ef4b | 2026-01-21T00:00:00-05:00 | The Dynamic and Endogenous Behavior of Re-Offense Risk: An Agent-Based Simulation Study of Treatment Allocation in Incarceration Diversion Programs | arXiv:2601.12441v1 Announce Type: new Abstract: Incarceration-diversion treatment programs aim to improve societal reintegration and reduce recidivism, but limited capacity forces policymakers to make prioritization decisions that often rely on risk assessment tools. While predictive, these tools typically treat risk a... | https://arxiv.org/abs/2601.12441 | Academic Papers | svg |
b808af5f122823803c88aacb55b09056f5e9461e1cfc310932806feca8ac3e6b | 2026-01-21T00:00:00-05:00 | Constraint-Aware Neurosymbolic Uncertainty Quantification with Bayesian Deep Learning for Scientific Discovery | arXiv:2601.12442v1 Announce Type: new Abstract: Scientific Artificial Intelligence (AI) applications require models that deliver trustworthy uncertainty estimates while respecting domain constraints. Existing uncertainty quantification methods lack mechanisms to incorporate symbolic scientific knowledge, while neurosym... | https://arxiv.org/abs/2601.12442 | Academic Papers | svg |
7db5a01cea65339d8a868a17cfdc0247ab93760548fbb1ded3af6706e6692394 | 2026-01-21T00:00:00-05:00 | Adversarial Defense in Vision-Language Models: An Overview | arXiv:2601.12443v1 Announce Type: new Abstract: The widespread use of Vision Language Models (VLMs, e.g. CLIP) has raised concerns about their vulnerability to sophisticated and imperceptible adversarial attacks. These attacks could compromise model performance and system security in cross-modal tasks. To address this ... | https://arxiv.org/abs/2601.12443 | Academic Papers | svg |
cdec834ee053af9b50a211f21389a7b07ca832aa997be262e0ac7649638e3ce4 | 2026-01-21T00:00:00-05:00 | Large Language Model for OWL Proofs | arXiv:2601.12444v1 Announce Type: new Abstract: The ability of Large Language Models (LLMs) to perform reasoning tasks such as deduction has been widely investigated in recent years. Yet, their capacity to generate proofs-faithful, human-readable explanations of why conclusions follow-remains largely under explored. In... | https://arxiv.org/abs/2601.12444 | Academic Papers | svg |
c22bbd85c912e65890b9b0e39581dccfcdfddbcc2d8767a8287af0001d2372dd | 2026-01-21T00:00:00-05:00 | Privacy-Preserving Federated Learning with Verifiable Fairness Guarantees | arXiv:2601.12447v1 Announce Type: new Abstract: Federated learning enables collaborative model training across distributed institutions without centralizing sensitive data; however, ensuring algorithmic fairness across heterogeneous data distributions while preserving privacy remains fundamentally unresolved. This pape... | https://arxiv.org/abs/2601.12447 | Academic Papers | svg |
2191ed0c6c766f10fda7dcc42a2ab42ee8a30cb2f442a83d8d5d5ae8708f24b3 | 2026-01-21T00:00:00-05:00 | Evaluating Large Language Models for Time Series Anomaly Detection in Aerospace Software | arXiv:2601.12448v1 Announce Type: new Abstract: Time series anomaly detection (TSAD) is essential for ensuring the safety and reliability of aerospace software systems. Although large language models (LLMs) provide a promising training-free alternative to unsupervised approaches, their effectiveness in aerospace settin... | https://arxiv.org/abs/2601.12448 | Academic Papers | svg |
46c01116c4f6a146c1c5225dd265e9ed033f50a7fe889a9bc3a7648ef61b435e | 2026-01-21T00:00:00-05:00 | AgenTRIM: Tool Risk Mitigation for Agentic AI | arXiv:2601.12449v1 Announce Type: new Abstract: AI agents are autonomous systems that combine LLMs with external tools to solve complex tasks. While such tools extend capability, improper tool permissions introduce security risks such as indirect prompt injection and tool misuse. We characterize these failures as unbal... | https://arxiv.org/abs/2601.12449 | Academic Papers | svg |
6cf89946a889924a4fff06f62e11a1b06e7a60fcbefe5651b01678161c6b85a1 | 2026-01-21T00:00:00-05:00 | Bringing Data Transformations Near-Memory for Low-Latency Analytics in HTAP Environments | arXiv:2601.12456v1 Announce Type: new Abstract: In this paper we propose an approach for executing data transformations near- or in-storage on intelligent storage systems. The currently prevailing approach of extracting the data and then transforming it to a target format suffers degraded performance during transformat... | https://arxiv.org/abs/2601.12456 | Academic Papers | svg |
25afe8a632d5986f0ae9bbc1ed34289559cb66c106e4082712d71dfab6cb0e5f | 2026-01-21T00:00:00-05:00 | TrojanPraise: Jailbreak LLMs via Benign Fine-Tuning | arXiv:2601.12460v1 Announce Type: new Abstract: The demand of customized large language models (LLMs) has led to commercial LLMs offering black-box fine-tuning APIs, yet this convenience introduces a critical security loophole: attackers could jailbreak the LLMs by fine-tuning them with malicious data. Though this secu... | https://arxiv.org/abs/2601.12460 | Academic Papers | svg |
88758a2beb0f5228f0856301f80579cdb2a2db4f06c0197938905a735fc62c74 | 2026-01-21T00:00:00-05:00 | KILO-EKF: Koopman-Inspired Learned Observations Extended Kalman Filter | arXiv:2601.12463v1 Announce Type: new Abstract: We present the Koopman-Inspired Learned Observations Extended Kalman Filter (KILO-EKF), which combines a standard EKF prediction step with a correction step based on a Koopman-inspired measurement model learned from data. By lifting measurements into a feature space where... | https://arxiv.org/abs/2601.12463 | Academic Papers | svg |
f95a0f9ce2eb7092ec4688c053407c2252c0a50b8909c7a4fc463d34a9ff3e3a | 2026-01-21T00:00:00-05:00 | Large-scale EM Benchmark for Multi-Organelle Instance Segmentation in the Wild | arXiv:2601.12464v1 Announce Type: new Abstract: Accurate instance-level segmentation of organelles in electron microscopy (EM) is critical for quantitative analysis of subcellular morphology and inter-organelle interactions. However, current benchmarks, based on small, curated datasets, fail to capture the inherent het... | https://arxiv.org/abs/2601.12464 | Academic Papers | svg |
c1f9dd1d3941f6c2eafd0e598c133fd45a2314af040edfa39a938c79f581a261 | 2026-01-21T00:00:00-05:00 | Incentivizing In-depth Reasoning over Long Contexts with Process Advantage Shaping | arXiv:2601.12465v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing LLMs short-context reasoning, but its performance degrades in long-context scenarios that require both precise grounding and robust long-range reasoning. We identify the "almost-there"... | https://arxiv.org/abs/2601.12465 | Academic Papers | svg |
2da03b54b3fc62ebfec69f4bc8e8e5e28ca32d1722071cbc450f7f57317cbf7d | 2026-01-21T00:00:00-05:00 | Patch-Level Tokenization with CNN Encoders and Attention for Improved Transformer Time-Series Forecasting | arXiv:2601.12467v1 Announce Type: new Abstract: Transformer-based models have shown strong performance in time-series forecasting by leveraging self-attention to model long-range temporal dependencies. However, their effectiveness depends critically on the quality and structure of input representations derived from raw... | https://arxiv.org/abs/2601.12467 | Academic Papers | svg |
ac72744d9c76e8d8188ef87e34256d06d60ed63f0767c4c12337353d6304597c | 2026-01-21T00:00:00-05:00 | DCAC: Dynamic Class-Aware Cache Creates Stronger Out-of-Distribution Detectors | arXiv:2601.12468v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection remains a fundamental challenge for deep neural networks, particularly due to overconfident predictions on unseen OOD samples during testing. We reveal a key insight: OOD samples predicted as the same class, or given high probabilities ... | https://arxiv.org/abs/2601.12468 | Academic Papers | svg |
98b0fce24dcd65439844238a7395c11a1d0d382599984c65f42a7d30a36079a3 | 2026-01-21T00:00:00-05:00 | Knowing When to Abstain: Medical LLMs Under Clinical Uncertainty | arXiv:2601.12471v1 Announce Type: new Abstract: Current evaluation of large language models (LLMs) overwhelmingly prioritizes accuracy; however, in real-world and safety-critical applications, the ability to abstain when uncertain is equally vital for trustworthy deployment. We introduce MedAbstain, a unified benchmark... | https://arxiv.org/abs/2601.12471 | Academic Papers | svg |
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