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8fb98cd307b6406379b862ba603bd2b4fd36168d690b6a118532e8b733d350a6 | 2026-01-13T00:00:00-05:00 | Fine-grained Verbal Attack Detection via a Hierarchical Divide-and-Conquer Framework | arXiv:2601.06907v1 Announce Type: new Abstract: In the digital era, effective identification and analysis of verbal attacks are essential for maintaining online civility and ensuring social security. However, existing research is limited by insufficient modeling of conversational structure and contextual dependency, pa... | https://arxiv.org/abs/2601.06907 | Academic Papers | svg |
cd89ae624c1565bfe89790c5a595f1c9f7ec199a25b7b8196e3e8347b2110ae4 | 2026-01-13T00:00:00-05:00 | UDPNet: Unleashing Depth-based Priors for Robust Image Dehazing | arXiv:2601.06909v1 Announce Type: new Abstract: Image dehazing has witnessed significant advancements with the development of deep learning models. However, a few methods predominantly focus on single-modal RGB features, neglecting the inherent correlation between scene depth and haze distribution. Even those that join... | https://arxiv.org/abs/2601.06909 | Academic Papers | svg |
7e889658642e303130a54c70a17ec4c0081660f24c6dae3d6d9b30e6ef491a43 | 2026-01-13T00:00:00-05:00 | PenForge: On-the-Fly Expert Agent Construction for Automated Penetration Testing | arXiv:2601.06910v1 Announce Type: new Abstract: Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has explored automating this process with Large Language Model (LLM)-powered agents, but existing approaches either rely on a single ... | https://arxiv.org/abs/2601.06910 | Academic Papers | svg |
e78432f2d67db998b7753ec90477f611d0ee36758d8b063dab8a4facc9ac9663 | 2026-01-13T00:00:00-05:00 | Distributional Clarity: The Hidden Driver of RL-Friendliness in Large Language Models | arXiv:2601.06911v1 Announce Type: new Abstract: Language model families exhibit striking disparity in their capacity to benefit from reinforcement learning: under identical training, models like Qwen achieve substantial gains, while others like Llama yield limited improvements. Complementing data-centric approaches, we... | https://arxiv.org/abs/2601.06911 | Academic Papers | svg |
fde9d5b2960aa654a8ee9b526dff14a22ff26c26b7d4c228d1eba586ec9f3263 | 2026-01-13T00:00:00-05:00 | Tractable Multinomial Logit Contextual Bandits with Non-Linear Utilities | arXiv:2601.06913v1 Announce Type: new Abstract: We study the multinomial logit (MNL) contextual bandit problem for sequential assortment selection. Although most existing research assumes utility functions to be linear in item features, this linearity assumption restricts the modeling of intricate interactions between ... | https://arxiv.org/abs/2601.06913 | Academic Papers | svg |
bc92eb3d57c8cd1abea4e5c5dc70c7ae4bd468f01e921fbfe042d5962632a957 | 2026-01-13T00:00:00-05:00 | Towards Compositional Generalization in LLMs for Smart Contract Security: A Case Study on Reentrancy Vulnerabilities | arXiv:2601.06914v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate remarkable capabilities in natural language understanding and generation. Despite being trained on large-scale, high-quality data, LLMs still fail to outperform traditional static analysis tools in specialized domains like smart co... | https://arxiv.org/abs/2601.06914 | Academic Papers | svg |
978a324a3d0fbbff00c1e065575413f273c415c5056390b74c4d98eb42b8cf33 | 2026-01-13T00:00:00-05:00 | Active Learning Strategies for Efficient Machine-Learned Interatomic Potentials Across Diverse Material Systems | arXiv:2601.06916v1 Announce Type: new Abstract: Efficient discovery of new materials demands strategies to reduce the number of costly first-principles calculations required to train predictive machine learning models. We develop and validate an active learning framework that iteratively selects informative training st... | https://arxiv.org/abs/2601.06916 | Academic Papers | svg |
c476ea1c19e6f557329220b04168f2eb73fc72800fc2ffaa6c7fb1825471f45e | 2026-01-13T00:00:00-05:00 | Calibrating Agent-Based Financial Markets Simulators with Pretrainable Automatic Posterior Transformation-Based Surrogates | arXiv:2601.06920v1 Announce Type: new Abstract: Calibrating Agent-Based Models (ABMs) is an important optimization problem for simulating the complex social systems, where the goal is to identify the optimal parameter of a given ABM by minimizing the discrepancy between the simulated data and the real-world observation... | https://arxiv.org/abs/2601.06920 | Academic Papers | svg |
84f6dae7c0a4cbb41a6a30dff91cbee30052dfd1ef19d65fd404fd79767b2e88 | 2026-01-13T00:00:00-05:00 | TreePS-RAG: Tree-based Process Supervision for Reinforcement Learning in Agentic RAG | arXiv:2601.06922v1 Announce Type: new Abstract: Agentic retrieval-augmented generation (RAG) formulates question answering as a multi-step interaction between reasoning and information retrieval, and has recently been advanced by reinforcement learning (RL) with outcome-based supervision. While effective, relying solel... | https://arxiv.org/abs/2601.06922 | Academic Papers | svg |
3579246e4c0136be3b3302a3113fe3e22932d2d405563a57349ea0bc6fa6892e | 2026-01-13T00:00:00-05:00 | Caching Yields up to 5x Spectral Efficiency in Multi-Beam Satellite Communications | arXiv:2601.06925v1 Announce Type: new Abstract: This paper examines the integration of vector coded caching (VCC) into multi-beam satellite communications (SATCOM) systems and demonstrates that even limited receiver-side caching can substantially enhance spectral efficiency. By leveraging cached content to suppress int... | https://arxiv.org/abs/2601.06925 | Academic Papers | svg |
4d5c901a7e7128b0048fda1a95bb8cbb79338dca60ce46567bcb1c494c4d23c4 | 2026-01-13T00:00:00-05:00 | RenderFlow: Single-Step Neural Rendering via Flow Matching | arXiv:2601.06928v1 Announce Type: new Abstract: Conventional physically based rendering (PBR) pipelines generate photorealistic images through computationally intensive light transport simulations. Although recent deep learning approaches leverage diffusion model priors with geometry buffers (G-buffers) to produce visu... | https://arxiv.org/abs/2601.06928 | Academic Papers | svg |
3b70393e20ce82e13a56b2770eb0bcc2b1fb9aba8b6907f08bfbd36a17a5ac7c | 2026-01-13T00:00:00-05:00 | Measuring Social Bias in Vision-Language Models with Face-Only Counterfactuals from Real Photos | arXiv:2601.06931v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly deployed in socially consequential settings, raising concerns about social bias driven by demographic cues. A central challenge in measuring such social bias is attribution under visual confounding: real-world images entangle... | https://arxiv.org/abs/2601.06931 | Academic Papers | svg |
5bd4076f7d0d76963a87c78fd7483a834fef5d649d23681176a17dac0306817e | 2026-01-13T00:00:00-05:00 | Symphonym: Universal Phonetic Embeddings for Cross-Script Toponym Matching via Teacher-Student Distillation | arXiv:2601.06932v1 Announce Type: new Abstract: Linking place names across languages and writing systems is a fundamental challenge in digital humanities and geographic information retrieval. Existing approaches rely on language-specific phonetic algorithms or transliteration rules that fail when names cross script bou... | https://arxiv.org/abs/2601.06932 | Academic Papers | svg |
da47af0d22a2a940069f004c84b34b42e29f635121a5f720f463692da5bb61b7 | 2026-01-13T00:00:00-05:00 | mind_call: A Dataset for Mental Health Function Calling with Large Language Models | arXiv:2601.06937v1 Announce Type: new Abstract: Large Language Model (LLM)-based systems increasingly rely on function calling to enable structured and controllable interaction with external data sources, yet existing datasets do not address mental health-oriented access to wearable sensor data. This paper presents a s... | https://arxiv.org/abs/2601.06937 | Academic Papers | svg |
dc2604aeb0ef5a736ea3aa2cb5357f85bf811d4d5fdbdff08a4b5b1d7e782cc7 | 2026-01-13T00:00:00-05:00 | Forgetting Similar Samples: Can Machine Unlearning Do it Better? | arXiv:2601.06938v1 Announce Type: new Abstract: Machine unlearning, a process enabling pre-trained models to remove the influence of specific training samples, has attracted significant attention in recent years. Although extensive research has focused on developing efficient machine unlearning strategies, we argue tha... | https://arxiv.org/abs/2601.06938 | Academic Papers | svg |
a17841e109b3b7fdef3238c822fdac543fb8519f963a80955923b1bbfcca45a5 | 2026-01-13T00:00:00-05:00 | VISTA: Knowledge-Driven Interpretable Vessel Trajectory Imputation via Large Language Models | arXiv:2601.06940v1 Announce Type: new Abstract: The Automatic Identification System provides critical information for maritime navigation and safety, yet its trajectories are often incomplete due to signal loss or deliberate tampering. Existing imputation methods emphasize trajectory recovery, paying limited attention ... | https://arxiv.org/abs/2601.06940 | Academic Papers | svg |
6f4f27f50de377b693764b529fd9b62d38617adb4ed768cd85d2e9f4f13f2106 | 2026-01-13T00:00:00-05:00 | Towards Operational Streamflow Forecasting in the Limpopo River Basin using Long Short-Term Memory Networks | arXiv:2601.06941v1 Announce Type: new Abstract: Robust hydrological simulation is key for sustainable development, water management strategies, and climate change adaptation. In recent years, deep learning methods have been demonstrated to outperform mechanistic models at the task of hydrological discharge simulation. ... | https://arxiv.org/abs/2601.06941 | Academic Papers | svg |
cb765676aaa309f74d6973fd3c39c6e90cebc16643c26feddf1a6cc781a647bc | 2026-01-13T00:00:00-05:00 | Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning | arXiv:2601.06943v1 Announce Type: new Abstract: In real-world video question answering scenarios, videos often provide only localized visual cues, while verifiable answers are distributed across the open web; models therefore need to jointly perform cross-frame clue extraction, iterative retrieval, and multi-hop reason... | https://arxiv.org/abs/2601.06943 | Academic Papers | svg |
e720addf670c06976a0321e5392390ef063e93e38b949b7a48e509254c06ec3e | 2026-01-13T00:00:00-05:00 | SketchJudge: A Diagnostic Benchmark for Grading Hand-drawn Diagrams with Multimodal Large Language Models | arXiv:2601.06944v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have achieved remarkable progress in visual understanding, they often struggle when faced with the unstructured and ambiguous nature of human-generated sketches. This limitation is particularly pronounced in the underexplored... | https://arxiv.org/abs/2601.06944 | Academic Papers | svg |
c2c4ea55d6ce1f528dc9cf5268cc412064cce50bf15d8f031ff7ec6310ad05dd | 2026-01-13T00:00:00-05:00 | Optimal Extended Formulations from Optimal Dynamic Programming Algorithms | arXiv:2601.06947v1 Announce Type: new Abstract: Vertex Subset Problems (VSPs) are a class of combinatorial optimization problems on graphs where the goal is to find a subset of vertices satisfying a predefined condition. Two prominent approaches for solving VSPs are dynamic programming over tree-like structures, such a... | https://arxiv.org/abs/2601.06947 | Academic Papers | svg |
594604ad39ba0e11fa768155cb0172f366c7f10b02a3f955dfff1f45ebd5e76c | 2026-01-13T00:00:00-05:00 | Operational Runtime Behavior Mining for Open-Source Supply Chain Security | arXiv:2601.06948v1 Announce Type: new Abstract: Open-source software (OSS) is a critical component of modern software systems, yet supply chain security remains challenging in practice due to unavailable or obfuscated source code. Consequently, security teams often rely on runtime observations collected from sandboxed ... | https://arxiv.org/abs/2601.06948 | Academic Papers | svg |
a1ce1debb6b8454ac3aad8b529f03a24a33daa00cdc5b365276bd756effc3fd1 | 2026-01-13T00:00:00-05:00 | X-Coder: Advancing Competitive Programming with Fully Synthetic Tasks, Solutions, and Tests | arXiv:2601.06953v1 Announce Type: new Abstract: Competitive programming presents great challenges for Code LLMs due to its intensive reasoning demands and high logical complexity. However, current Code LLMs still rely heavily on real-world data, which limits their scalability. In this paper, we explore a fully syntheti... | https://arxiv.org/abs/2601.06953 | Academic Papers | svg |
338ef2cefdde2eeafb76f7969f8832f7fdacd7ee6dc6bc82b99766c66f70f8b6 | 2026-01-13T00:00:00-05:00 | Arithmetic Complexity of Solutions of the Dirichlet Problem | arXiv:2601.06954v1 Announce Type: new Abstract: The classical Dirichlet problem on the unit disk can be solved by different numerical approaches. The two most common and popular approaches are the integration of the associated Poisson integral and, by applying Dirichlet's principle, solving a particular minimization pr... | https://arxiv.org/abs/2601.06954 | Academic Papers | svg |
e936afdc157e7fbb758a0e6337b0cb99d0d8610f597898036910538fa49d3bcd | 2026-01-13T00:00:00-05:00 | HAS-VQ: Hessian-Adaptive Sparse Vector Quantization for High-Fidelity LLM Compression | arXiv:2601.06959v1 Announce Type: new Abstract: Post-training quantization is essential for deploying Large Language Models (LLMs) on resource- constrained devices. However, standard integer quantization (e.g., INT4) fundamentally degrades per- formance by imposing a uniform grid on the heavy-tailed distribution of wei... | https://arxiv.org/abs/2601.06959 | Academic Papers | svg |
6536581ae3b7e6efea0de6d1a5605536a797e05b3b18d0e629b0626b21780efe | 2026-01-13T00:00:00-05:00 | Hardware-in-the-loop wind-tunnel testing of wake interactions between two floating wind turbines | arXiv:2601.06964v1 Announce Type: new Abstract: Wake interactions in floating wind farms are inherently coupled to platform motion, yet most experimental studies to date neglect this two-way coupling by prescribing platform movements. This work presents a hardware-in-the-loop (HIL) wind-tunnel methodology to investigat... | https://arxiv.org/abs/2601.06964 | Academic Papers | svg |
7fab8c2b1d63be6217ed565f8ea135dbc3985b1bcc8674a2ca2924e52447954c | 2026-01-13T00:00:00-05:00 | Unified Personalized Understanding, Generating and Editing | arXiv:2601.06965v1 Announce Type: new Abstract: Unified large multimodal models (LMMs) have achieved remarkable progress in general-purpose multimodal understanding and generation. However, they still operate under a ``one-size-fits-all'' paradigm and struggle to model user-specific concepts (e.g., generate a photo of ... | https://arxiv.org/abs/2601.06965 | Academic Papers | svg |
3fb339ed381038a96c84a70b192a97d0f203980e1f0f340a9da8e1bf9ad26619 | 2026-01-13T00:00:00-05:00 | RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction | arXiv:2601.06966v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve from static dialogue interfaces to autonomous general agents, effective memory is paramount to ensuring long-term consistency. However, existing benchmarks primarily focus on casual conversation or task-oriented dialogue, failing to ... | https://arxiv.org/abs/2601.06966 | Academic Papers | svg |
c90be628c5186e9a7ead1d8a3dbe3f449f1b69bf08ecd8339562d1459905a031 | 2026-01-13T00:00:00-05:00 | A Robust Certified Machine Unlearning Method Under Distribution Shift | arXiv:2601.06967v1 Announce Type: new Abstract: The Newton method has been widely adopted to achieve certified unlearning. A critical assumption in existing approaches is that the data requested for unlearning are selected i.i.d.(independent and identically distributed). However,the problem of certified unlearning unde... | https://arxiv.org/abs/2601.06967 | Academic Papers | svg |
712010ca0ff6b056125945f5552bc00b9c49048d19e01153bdca73dece84ed67 | 2026-01-13T00:00:00-05:00 | Generalization Bounds for Transformer Channel Decoders | arXiv:2601.06969v1 Announce Type: new Abstract: Transformer channel decoders, such as the Error Correction Code Transformer (ECCT), have shown strong empirical performance in channel decoding, yet their generalization behavior remains theoretically unclear. This paper studies the generalization performance of ECCT from... | https://arxiv.org/abs/2601.06969 | Academic Papers | svg |
eac833c24b71ce49d67bb6dfadb5479b5fc7292e9a781050a984e8083b31fe88 | 2026-01-13T00:00:00-05:00 | Categorize Early, Integrate Late: Divergent Processing Strategies in Automatic Speech Recognition | arXiv:2601.06972v1 Announce Type: new Abstract: In speech language modeling, two architectures dominate the frontier: the Transformer and the Conformer. However, it remains unknown whether their comparable performance stems from convergent processing strategies or distinct architectural inductive biases. We introduce A... | https://arxiv.org/abs/2601.06972 | Academic Papers | svg |
60647fb175b377bb36a7e4cd5415c9e021cfd9edddb31f7f2dbd2362c3c139ea | 2026-01-13T00:00:00-05:00 | LLMs Can't Play Hangman: On the Necessity of a Private Working Memory for Language Agents | arXiv:2601.06973v1 Announce Type: new Abstract: As LLMs move from text completion toward autonomous agents, they remain constrained by the standard chat interface, which lacks private working memory. This raises a fundamental question: can agents reliably perform interactive tasks that depend on hidden state? We define... | https://arxiv.org/abs/2601.06973 | Academic Papers | svg |
b1d20069d951acf27141ad665b4d9e70c210f7bf5f20ec25891ce7a60952b86f | 2026-01-13T00:00:00-05:00 | UETQuintet at BioCreative IX - MedHopQA: Enhancing Biomedical QA with Selective Multi-hop Reasoning and Contextual Retrieval | arXiv:2601.06974v1 Announce Type: new Abstract: Biomedical Question Answering systems play a critical role in processing complex medical queries, yet they often struggle with the intricate nature of medical data and the demand for multi-hop reasoning. In this paper, we propose a model designed to effectively address bo... | https://arxiv.org/abs/2601.06974 | Academic Papers | svg |
f2df80a9fa92785c65dadc059dbd4265c1183ab9b0bc9a214f8a615b89a240ca | 2026-01-13T00:00:00-05:00 | MedTutor: A Retrieval-Augmented LLM System for Case-Based Medical Education | arXiv:2601.06979v1 Announce Type: new Abstract: The learning process for medical residents presents significant challenges, demanding both the ability to interpret complex case reports and the rapid acquisition of accurate medical knowledge from reliable sources. Residents typically study case reports and engage in dis... | https://arxiv.org/abs/2601.06979 | Academic Papers | svg |
f63ee3775c260db0987b12b96f6a418ab4316c2e76ccf24a2b78e9d12e88dc6f | 2026-01-13T00:00:00-05:00 | A New Perspective on Drawing Venn Diagrams for Data Visualization | arXiv:2601.06980v1 Announce Type: new Abstract: We introduce VennFan, a method for generating $n$-set Venn diagrams based on the polar coordinate projection of trigonometric boundaries, resulting in Venn diagrams that resemble a set of fan blades. Unlike most classical constructions, our method emphasizes readability a... | https://arxiv.org/abs/2601.06980 | Academic Papers | svg |
51817a0ecb86b58c0ee822c8e9d31e96a65bd044a3de0093efae284d37964437 | 2026-01-13T00:00:00-05:00 | Directional Selective Fixed-Filter Active Noise Control Based on a Convolutional Neural Network in Reverberant Environments | arXiv:2601.06981v1 Announce Type: new Abstract: Selective fixed-filter active noise control (SFANC) is a novel approach capable of mitigating noise with varying frequency characteristics. It offers faster response and greater computational efficiency compared to traditional adaptive algorithms. However, spatial factors... | https://arxiv.org/abs/2601.06981 | Academic Papers | svg |
0f018866e22bac7417faae99c037394ca5779cf375e0ada3de79b9623043b16d | 2026-01-13T00:00:00-05:00 | FinCARDS: Card-Based Analyst Reranking for Financial Document Question Answering | arXiv:2601.06992v1 Announce Type: new Abstract: Financial question answering (QA) over long corporate filings requires evidence to satisfy strict constraints on entities, financial metrics, fiscal periods, and numeric values. However, existing LLM-based rerankers primarily optimize semantic relevance, leading to unstab... | https://arxiv.org/abs/2601.06992 | Academic Papers | svg |
8ec7c5b96ebef90d71339c3811bbf4336573269bb6ac786669d0d35a123354eb | 2026-01-13T00:00:00-05:00 | Can Textual Reasoning Improve the Performance of MLLMs on Fine-grained Visual Classification? | arXiv:2601.06993v1 Announce Type: new Abstract: Multi-modal large language models (MLLMs) exhibit strong general-purpose capabilities, yet still struggle on Fine-Grained Visual Classification (FGVC), a core perception task that requires subtle visual discrimination and is crucial for many real-world applications. A wid... | https://arxiv.org/abs/2601.06993 | Academic Papers | svg |
84ac7964b6ce1d18886293c061835a92817148a376910d8e6b3a0a270b106a6d | 2026-01-13T00:00:00-05:00 | ObjSplat: Geometry-Aware Gaussian Surfels for Active Object Reconstruction | arXiv:2601.06997v1 Announce Type: new Abstract: Autonomous high-fidelity object reconstruction is fundamental for creating digital assets and bridging the simulation-to-reality gap in robotics. We present ObjSplat, an active reconstruction framework that leverages Gaussian surfels as a unified representation to progres... | https://arxiv.org/abs/2601.06997 | Academic Papers | svg |
cd8082c521dee7ea2f0c721c9ff921a216c182796c9e63a09f60ee6f1215c436 | 2026-01-13T00:00:00-05:00 | Spatial Multi-Task Learning for Breast Cancer Molecular Subtype Prediction from Single-Phase DCE-MRI | arXiv:2601.07001v1 Announce Type: new Abstract: Accurate molecular subtype classification is essential for personalized breast cancer treatment, yet conventional immunohistochemical analysis relies on invasive biopsies and is prone to sampling bias. Although dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI... | https://arxiv.org/abs/2601.07001 | Academic Papers | svg |
886d547d65a38649bf3db588bbcc049c626ff14c49dd3aa19adfdbddf5832ddb | 2026-01-13T00:00:00-05:00 | MemTrust: A Zero-Trust Architecture for Unified AI Memory System | arXiv:2601.07004v1 Announce Type: new Abstract: AI memory systems are evolving toward unified context layers that enable efficient cross-agent collaboration and multi-tool workflows, facilitating better accumulation of personal data and learning of user preferences. However, centralization creates a trust crisis where ... | https://arxiv.org/abs/2601.07004 | Academic Papers | svg |
083111b184034be5f11bb24b22847b5fb1e82c67daca5735d48cfa1de82b5ce6 | 2026-01-13T00:00:00-05:00 | MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning | arXiv:2601.07005v1 Announce Type: new Abstract: Log parsing converts semi-structured logs into structured templates, forming a critical foundation for downstream analysis. Traditional syntax and semantic-based parsers often struggle with semantic variations in evolving logs and data scarcity stemming from their limited... | https://arxiv.org/abs/2601.07005 | Academic Papers | svg |
019a4b5d25c431304aad728bdc55294232e280276914ed12eb56d22496682feb | 2026-01-13T00:00:00-05:00 | LLM Performance Predictors: Learning When to Escalate in Hybrid Human-AI Moderation Systems | arXiv:2601.07006v1 Announce Type: new Abstract: As LLMs are increasingly integrated into human-in-the-loop content moderation systems, a central challenge is deciding when their outputs can be trusted versus when escalation for human review is preferable. We propose a novel framework for supervised LLM uncertainty quan... | https://arxiv.org/abs/2601.07006 | Academic Papers | svg |
982576aacae56d3b013ac34e72900b726d50129c700bc85be313780b89c4c27d | 2026-01-13T00:00:00-05:00 | Lexicalized Constituency Parsing for Middle Dutch: Low-resource Training and Cross-Domain Generalization | arXiv:2601.07008v1 Announce Type: new Abstract: Recent years have seen growing interest in applying neural networks and contextualized word embeddings to the parsing of historical languages. However, most advances have focused on dependency parsing, while constituency parsing for low-resource historical languages like ... | https://arxiv.org/abs/2601.07008 | Academic Papers | svg |
4f96393130d2ecfd4a7df0f658e39c7d9add320b6d5ea34ce84f3adb315f62df | 2026-01-13T00:00:00-05:00 | A Sliding Mode Controller Based on Timoshenko Beam Theory Developed for a Tendon-Driven Robotic Wrist | arXiv:2601.07009v1 Announce Type: new Abstract: Development of dexterous robotic joints is essential for advancing manipulation capabilities in robotic systems. This paper presents the design and implementation of a tendon-driven robotic wrist joint together with an efficient Sliding Mode Controller (SMC) for precise m... | https://arxiv.org/abs/2601.07009 | Academic Papers | svg |
2413f46d1fd36aff3766591a0d610b637eb8ad938d1304a1875d9f64b2ada134 | 2026-01-13T00:00:00-05:00 | Belief in False Information: A Human-Centered Security Risk in Sociotechnical Systems | arXiv:2601.07016v1 Announce Type: new Abstract: This paper provides a comprehensive literature review on the belief in false information, including misinformation, disinformation, and fake information. It addresses the increasing societal concern regarding false information, which is fueled by technological progress, e... | https://arxiv.org/abs/2601.07016 | Academic Papers | svg |
ed8cd285c6988e612053118ee0cd1cd7529339cdefa1bb1271575d849ab994fd | 2026-01-13T00:00:00-05:00 | The Ill-Posed Foundations of Physics-Informed Neural Networks and Their Finite-Difference Variants | arXiv:2601.07017v1 Announce Type: new Abstract: Physics-informed neural networks based on automatic differentiation (AD-PINNs) and their finite-difference counterparts (FD-PINNs) are widely used for solving partial differential equations (PDEs), yet their analytical properties remain poorly understood. This work provid... | https://arxiv.org/abs/2601.07017 | Academic Papers | svg |
f91b370c9b60a28614377f7f8f62029f0d8f7ba45d746aef5e7ba2c6c158698d | 2026-01-13T00:00:00-05:00 | Zer0n: An AI-Assisted Vulnerability Discovery and Blockchain-Backed Integrity Framework | arXiv:2601.07019v1 Announce Type: new Abstract: As vulnerability research increasingly adopts generative AI, a critical reliance on opaque model outputs has emerged, creating a "trust gap" in security automation. We address this by introducing Zer0n, a framework that anchors the reasoning capabilities of Large Language... | https://arxiv.org/abs/2601.07019 | Academic Papers | svg |
1181b98c6fe1c6f515231eb12d9f4e6d7d5f73b76f176b4a174eb8b955da2982 | 2026-01-13T00:00:00-05:00 | TurkBench: A Benchmark for Evaluating Turkish Large Language Models | arXiv:2601.07020v1 Announce Type: new Abstract: With the recent surge in the development of large language models, the need for comprehensive and language-specific evaluation benchmarks has become critical. While significant progress has been made in evaluating English language models, benchmarks for other languages, p... | https://arxiv.org/abs/2601.07020 | Academic Papers | svg |
6d04361bd22f5bc45dbc2ccfb0ad2f8c47d1fe20da5e66219b2f7c21bc78cbf2 | 2026-01-13T00:00:00-05:00 | Tight Analysis of Decentralized SGD: A Markov Chain Perspective | arXiv:2601.07021v1 Announce Type: new Abstract: We propose a novel analysis of the Decentralized Stochastic Gradient Descent (DSGD) algorithm with constant step size, interpreting the iterates of the algorithm as a Markov chain. We show that DSGD converges to a stationary distribution, with its bias, to first order, de... | https://arxiv.org/abs/2601.07021 | Academic Papers | svg |
3979c0a3b04957a0abf458a0ebee503e50b449c16817575da533544d0fc61647 | 2026-01-13T00:00:00-05:00 | Solar Open Technical Report | arXiv:2601.07022v1 Announce Type: new Abstract: We introduce Solar Open, a 102B-parameter bilingual Mixture-of-Experts language model for underserved languages. Solar Open demonstrates a systematic methodology for building competitive LLMs by addressing three interconnected challenges. First, to train effectively despi... | https://arxiv.org/abs/2601.07022 | Academic Papers | svg |
e7e872e2032da517183d00eeefe3465e08918aadec2b13ca5ec49458cab8b9ea | 2026-01-13T00:00:00-05:00 | CloneMem: Benchmarking Long-Term Memory for AI Clones | arXiv:2601.07023v1 Announce Type: new Abstract: AI Clones aim to simulate an individual's thoughts and behaviors to enable long-term, personalized interaction, placing stringent demands on memory systems to model experiences, emotions, and opinions over time. Existing memory benchmarks primarily rely on user-agent conv... | https://arxiv.org/abs/2601.07023 | Academic Papers | svg |
59817dc0878f2b2216186fdd12bb52c0b93264ba869f124ec200b8285a832131 | 2026-01-13T00:00:00-05:00 | A Relaxed Direct-insertion Downscaling Method For Discrete-in-time Data Assimilation | arXiv:2601.07025v1 Announce Type: new Abstract: This paper improves the spectrally-filtered direct-insertion downscaling method for discrete-in-time data assimilation by introducing a relaxation parameter that overcomes a constraint on the observation frequency. Numerical simulations demonstrate that taking the relaxat... | https://arxiv.org/abs/2601.07025 | Academic Papers | svg |
4c224fcd933b1578c348d27cd2fce49ebe1ebf849885417d3ae7b57f08497f86 | 2026-01-13T00:00:00-05:00 | Codified Foreshadowing-Payoff Text Generation | arXiv:2601.07033v1 Announce Type: new Abstract: Foreshadowing and payoff are ubiquitous narrative devices through which authors introduce commitments early in a story and resolve them through concrete, observable outcomes. However, despite advances in story generation, large language models (LLMs) frequently fail to br... | https://arxiv.org/abs/2601.07033 | Academic Papers | svg |
45d6c4f067ba3e6c9337cd018e720858f0fa2a0dcb27454d966e05ba5e3848b1 | 2026-01-13T00:00:00-05:00 | Quantum Optical Integrated Sensing and Communication with Homodyne BPSK Detection | arXiv:2601.07034v1 Announce Type: new Abstract: In this letter, we propose a quantum integrated sensing and communication scheme for a quantum optical link using binary phase-shift keying modulation and homodyne detection. The link operates over a phase-insensitive Gaussian channel with an unknown deterministic phase r... | https://arxiv.org/abs/2601.07034 | Academic Papers | svg |
691ad58c228d6d0d7ce0c13578f61be1299fac87c40e67a7bb4221ba8cb4e8fb | 2026-01-13T00:00:00-05:00 | Explainable Deep Radiogenomic Molecular Imaging for MGMT Methylation Prediction in Glioblastoma | arXiv:2601.07035v1 Announce Type: new Abstract: Glioblastoma (GBM) is a highly aggressive primary brain tumor with limited therapeutic options and poor prognosis. The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) gene promoter is a critical molecular biomarker that influences patient response ... | https://arxiv.org/abs/2601.07035 | Academic Papers | svg |
cdab818c5dec16ab90eb706bbdc22c17802897287f505e1f086b7c504807f513 | 2026-01-13T00:00:00-05:00 | Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers | arXiv:2601.07036v1 Announce Type: new Abstract: Hybrid reasoning language models are commonly controlled through high-level Think/No-think instructions to regulate reasoning behavior, yet we found that such mode switching is largely driven by a small set of trigger tokens rather than the instructions themselves. Throug... | https://arxiv.org/abs/2601.07036 | Academic Papers | svg |
b94d79fab11f0ec0319a70e60c6d562f4e6be0c16348075ffb89b8bb5d6ed81c | 2026-01-13T00:00:00-05:00 | Task Arithmetic with Support Languages for Low-Resource ASR | arXiv:2601.07038v1 Announce Type: new Abstract: The development of resource-constrained approaches to automatic speech recognition (ASR) is of great interest due to its broad applicability to many low-resource languages for which there is scant usable data. Existing approaches to many low-resource natural language proc... | https://arxiv.org/abs/2601.07038 | Academic Papers | svg |
a44c839c456be240f12a3d53004577910a3c296ab6aefd2382f08e7349c4f844 | 2026-01-13T00:00:00-05:00 | When Abundance Conceals Weakness: Knowledge Conflict in Multilingual Models | arXiv:2601.07041v1 Announce Type: new Abstract: Large Language Models (LLMs) encode vast world knowledge across multiple languages, yet their internal beliefs are often unevenly distributed across linguistic spaces. When external evidence contradicts these language-dependent memories, models encounter \emph{cross-lingu... | https://arxiv.org/abs/2601.07041 | Academic Papers | svg |
9f057a88f28d7d923959c0951a31a49160a8357b1396ea682cc2189bc934555c | 2026-01-13T00:00:00-05:00 | Engineering of Hallucination in Generative AI: It's not a Bug, it's a Feature | arXiv:2601.07046v1 Announce Type: new Abstract: Generative artificial intelligence (AI) is conquering our lives at lightning speed. Large language models such as ChatGPT answer our questions or write texts for us, large computer vision models such as GAIA-1 generate videos on the basis of text descriptions or continue ... | https://arxiv.org/abs/2601.07046 | Academic Papers | svg |
50e75b8534a356a2cf2a502db4e15e6f0e082c3d43abbeab25ed663a89ae9062 | 2026-01-13T00:00:00-05:00 | Jasper: ANNS Quantized for Speed, Built for Change on GPU | arXiv:2601.07048v1 Announce Type: new Abstract: Approximate nearest neighbor search (ANNS) is a core problem in machine learning and information retrieval applications. GPUs offer a promising path to high-performance ANNS: they provide massive parallelism for distance computations, are readily available, and can co-loc... | https://arxiv.org/abs/2601.07048 | Academic Papers | svg |
7056c1ec060e60965ca918221c940d6de784555f8c37f637cade9e9e9bbf5517 | 2026-01-13T00:00:00-05:00 | Between Policy and Practice: GenAI Adoption in Agile Software Development Teams | arXiv:2601.07051v1 Announce Type: new Abstract: Context: The rapid emergence of generative AI (GenAI) tools has begun to reshape various software engineering activities. Yet, their adoption within agile environments remains underexplored. Objective: This study investigates how agile practitioners adopt GenAI tools in r... | https://arxiv.org/abs/2601.07051 | Academic Papers | svg |
1f4b239eb585c09d9c04116af73eee5eb907f6028c0e7343a1f0660ec72ec598 | 2026-01-13T00:00:00-05:00 | RSLCPP - Deterministic Simulations Using ROS 2 | arXiv:2601.07052v1 Announce Type: new Abstract: Simulation is crucial in real-world robotics, offering safe, scalable, and efficient environments for developing applications, ranging from humanoid robots to autonomous vehicles and drones. While the Robot Operating System (ROS) has been widely adopted as the backbone of... | https://arxiv.org/abs/2601.07052 | Academic Papers | svg |
dbdfeed49db3527a5a999b8fe7a3af8ae9ab14d7f07431d65f669e8d54e88784 | 2026-01-13T00:00:00-05:00 | Random Access in DNA Storage: Algorithms, Constructions, and Bounds | arXiv:2601.07053v1 Announce Type: new Abstract: As DNA data storage moves closer to practical deployment, minimizing sequencing coverage depth is essential to reduce both operational costs and retrieval latency. This paper addresses the recently studied Random Access Problem, which evaluates the expected number of read... | https://arxiv.org/abs/2601.07053 | Academic Papers | svg |
8ec07e05733bfcf84af5f50fce6b394eb5cf0ea3c956060211e860c917fdfb15 | 2026-01-13T00:00:00-05:00 | Fine-Tuning vs. RAG for Multi-Hop Question Answering with Novel Knowledge | arXiv:2601.07054v1 Announce Type: new Abstract: Multi-hop question answering is widely used to evaluate the reasoning capabilities of large language models (LLMs), as it requires integrating multiple pieces of supporting knowledge to arrive at a correct answer. While prior work has explored different mechanisms for pro... | https://arxiv.org/abs/2601.07054 | Academic Papers | svg |
cdaf88b083418502d33f2645b408b0d97bd5fae63518e12a17809d67f30f346e | 2026-01-13T00:00:00-05:00 | Dr. Zero: Self-Evolving Search Agents without Training Data | arXiv:2601.07055v1 Announce Type: new Abstract: As high-quality data becomes increasingly difficult to obtain, data-free self-evolution has emerged as a promising paradigm. This approach allows large language models (LLMs) to autonomously generate and solve complex problems, thereby improving their reasoning capabiliti... | https://arxiv.org/abs/2601.07055 | Academic Papers | svg |
efb3198d724905c508f69301dc908015de3164fb4cb559a4d2f7c18f0ca4e929 | 2026-01-13T00:00:00-05:00 | Adversarial Attacks on Medical Hyperspectral Imaging Exploiting Spectral-Spatial Dependencies and Multiscale Features | arXiv:2601.07056v1 Announce Type: new Abstract: Medical hyperspectral imaging (HSI) enables accurate disease diagnosis by capturing rich spectral-spatial tissue information, but recent advances in deep learning have exposed its vulnerability to adversarial attacks. In this work, we identify two fundamental causes of th... | https://arxiv.org/abs/2601.07056 | Academic Papers | svg |
4ee7e2f23e3b4370632f2477e0524e37bca91e16b3b9c43c69cd083a06f61271 | 2026-01-13T00:00:00-05:00 | Hallucinations Live in Variance | arXiv:2601.07058v1 Announce Type: new Abstract: Benchmarks measure whether a model is correct. They do not measure whether a model is reliable. This distinction is largely academic for single-shot inference, but becomes critical for agentic AI systems, where a single rephrased prompt can trigger cascading failures in m... | https://arxiv.org/abs/2601.07058 | Academic Papers | svg |
119c5336d72cb1652258c60683d1920f9bfd24a6b33eb8447725a60b5fbcbfff | 2026-01-13T00:00:00-05:00 | PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation | arXiv:2601.07060v1 Announce Type: new Abstract: Recent advancements in vision-language-action (VLA) models have shown promise in robotic manipulation, yet they continue to struggle with long-horizon, multi-step tasks. Existing methods lack internal reasoning mechanisms that can identify task-relevant interaction cues o... | https://arxiv.org/abs/2601.07060 | Academic Papers | svg |
b0e044f9a60d9dd4834bf5df0ccd6e4464ec8858e8beaa464763bab03d3fe30c | 2026-01-13T00:00:00-05:00 | Automated Domain Question Mapping (DQM) with Educational Learning Materials | arXiv:2601.07062v1 Announce Type: new Abstract: Concept maps have been widely utilized in education to depict knowledge structures and the interconnections between disciplinary concepts. Nonetheless, devising a computational method for automatically constructing a concept map from unstructured educational materials pre... | https://arxiv.org/abs/2601.07062 | Academic Papers | svg |
d6ee0f1c23a73c57e5b0e70772b90fca532e21e6635b11fcc5de1bac4bed204b | 2026-01-13T00:00:00-05:00 | Neuromorphic FPGA Design for Digital Signal Processing | arXiv:2601.07069v1 Announce Type: new Abstract: In this paper, the foundations of neuromorphic computing, spiking neural networks (SNNs) and memristors, are analyzed and discussed. Neuromorphic computing is then applied to FPGA design for digital signal processing (DSP). Finite impulse response (FIR) and infinite impul... | https://arxiv.org/abs/2601.07069 | Academic Papers | svg |
23e272c6b5ef138a0918b78a5622dc1a3a1467baa3c261decf7d37fd0faf1244 | 2026-01-13T00:00:00-05:00 | LINEture: novel signature cryptosystem | arXiv:2601.07071v1 Announce Type: new Abstract: We propose a novel digital signature cryptosystem that exploits the concept of the brute-force problem. To ensure the security of the cryptosystem, we employed several mechanisms: sharing a common secret for factorable permutations, associating permutations with the messa... | https://arxiv.org/abs/2601.07071 | Academic Papers | svg |
47e038fc9c62a087c3e57103123b79de30926b32b3b725d2af03773a795ae407 | 2026-01-13T00:00:00-05:00 | Overcoming the Retrieval Barrier: Indirect Prompt Injection in the Wild for LLM Systems | arXiv:2601.07072v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on retrieving information from external corpora. This creates a new attack surface: indirect prompt injection (IPI), where hidden instructions are planted in the corpora and hijack model behavior once retrieved. Previous stud... | https://arxiv.org/abs/2601.07072 | Academic Papers | svg |
72a0faf90eba97b0df7a09cdd705172fcfce6cfe953a94a37d69386687d2f828 | 2026-01-13T00:00:00-05:00 | Billboard in Focus: Estimating Driver Gaze Duration from a Single Image | arXiv:2601.07073v1 Announce Type: new Abstract: Roadside billboards represent a central element of outdoor advertising, yet their presence may contribute to driver distraction and accident risk. This study introduces a fully automated pipeline for billboard detection and driver gaze duration estimation, aiming to evalu... | https://arxiv.org/abs/2601.07073 | Academic Papers | svg |
4216cbb1246bc385a09d19698b7c62b4ac329f66961b30c7191b339b14b4e756 | 2026-01-13T00:00:00-05:00 | An efficient hyper reduced-order model for segregated solvers for geometrical parametrization problems | arXiv:2601.07082v1 Announce Type: new Abstract: We propose an efficient hyper-reduced order model (HROM) designed for segregated finite-volume solvers in geometrically parametrized problems. The method follows a discretize-then-project strategy: the full-order operators are first assembled using finite volume or finite... | https://arxiv.org/abs/2601.07082 | Academic Papers | svg |
be0d412c30e88d203535c82ae8d76186e51ba2610eb3f35ef05afba39c142365 | 2026-01-13T00:00:00-05:00 | How Secure is Secure Code Generation? Adversarial Prompts Put LLM Defenses to the Test | arXiv:2601.07084v1 Announce Type: new Abstract: Recent secure code generation methods, using vulnerability-aware fine-tuning, prefix-tuning, and prompt optimization, claim to prevent LLMs from producing insecure code. However, their robustness under adversarial conditions remains untested, and current evaluations decou... | https://arxiv.org/abs/2601.07084 | Academic Papers | svg |
d6213f10243d388b6b9694ee314496b39c50d02205d2e71698e3c0d8e920ed0f | 2026-01-13T00:00:00-05:00 | The AI Cognitive Trojan Horse: How Large Language Models May Bypass Human Epistemic Vigilance | arXiv:2601.07085v1 Announce Type: new Abstract: Large language model (LLM)-based conversational AI systems present a challenge to human cognition that current frameworks for understanding misinformation and persuasion do not adequately address. This paper proposes that a significant epistemic risk from conversational A... | https://arxiv.org/abs/2601.07085 | Academic Papers | svg |
9ebc4058d7bc695a68716fe52bd766c443c0629eadcccc18cad776ed0e18e458 | 2026-01-13T00:00:00-05:00 | XBTorch: A Unified Framework for Modeling and Co-Design of Crossbar-Based Deep Learning Accelerators | arXiv:2601.07086v1 Announce Type: new Abstract: Emerging memory technologies have gained significant attention as a promising pathway to overcome the limitations of conventional computing architectures in deep learning applications. By enabling computation directly within memory, these technologies - built on nanoscale... | https://arxiv.org/abs/2601.07086 | Academic Papers | svg |
996bfa41e6e310453f48d02769ce2c692ed4472cec7056baa9797ebf31797ac9 | 2026-01-13T00:00:00-05:00 | When Should We Introduce Safety Interventions During Pretraining? | arXiv:2601.07087v1 Announce Type: new Abstract: Ensuring the safety of language models in high-stakes settings remains a pressing challenge, as aligned behaviors are often brittle and easily undone by adversarial pressure or downstream finetuning. Prior work has shown that interventions applied during pretraining, such... | https://arxiv.org/abs/2601.07087 | Academic Papers | svg |
aeff22e57908b9a5369dafb8dd36615373c256f9284e590d1cd9d2150bff8493 | 2026-01-13T00:00:00-05:00 | Next-Generation Grid Codes: Toward a New Paradigm for Dynamic Ancillary Services | arXiv:2601.07090v1 Announce Type: new Abstract: This paper presents preliminary results toward a conceptual foundation for Next Generation Grid Codes (NGGCs) based on decentralized stability and performance certification for dynamic ancillary services. The proposed NGGC framework targets two core outcomes: (i) guarante... | https://arxiv.org/abs/2601.07090 | Academic Papers | svg |
c982eaf55abae296136cc003a5810f2c0cfc9556ac799b9a437b81f94de40944 | 2026-01-13T00:00:00-05:00 | Efficient Visual Question Answering Pipeline for Autonomous Driving via Scene Region Compression | arXiv:2601.07092v1 Announce Type: new Abstract: Autonomous driving increasingly relies on Visual Question Answering (VQA) to enable vehicles to understand complex surroundings by analyzing visual inputs and textual queries. Currently, a paramount concern for VQA in this domain is the stringent requirement for fast late... | https://arxiv.org/abs/2601.07092 | Academic Papers | svg |
6d77f1b4ecebf5c3c7bc9a57226675ac0e352b5bb86f9b6fd95bdc805af274c7 | 2026-01-13T00:00:00-05:00 | 3D Wavelet-Based Structural Priors for Controlled Diffusion in Whole-Body Low-Dose PET Denoising | arXiv:2601.07093v1 Announce Type: new Abstract: Low-dose Positron Emission Tomography (PET) imaging reduces patient radiation exposure but suffers from increased noise that degrades image quality and diagnostic reliability. Although diffusion models have demonstrated strong denoising capability, their stochastic nature... | https://arxiv.org/abs/2601.07093 | Academic Papers | svg |
cf2c10baa6f2476726c806a52d3960d9628242f6db315de9306a401cc3fb6f30 | 2026-01-13T00:00:00-05:00 | Score-Based VAMP with Fisher-Information-Based Onsager Correction | arXiv:2601.07095v1 Announce Type: new Abstract: We propose score-based VAMP (SC-VAMP), a variant of vector approximate message passing (VAMP) in which the Onsager correction is expressed and computed via conditional Fisher information, thereby enabling a Jacobian-free implementation. Using learned score functions, SC-V... | https://arxiv.org/abs/2601.07095 | Academic Papers | svg |
2d53095eede4cd072cac60bfe2af045fd91c4aae6ec41b969a0aaedbcb16c764 | 2026-01-13T00:00:00-05:00 | MEDVISTAGYM: A Scalable Training Environment for Thinking with Medical Images via Tool-Integrated Reinforcement Learning | arXiv:2601.07107v1 Announce Type: new Abstract: Vision language models (VLMs) achieve strong performance on general image understanding but struggle to think with medical images, especially when performing multi-step reasoning through iterative visual interaction. Medical VLMs often rely on static visual embeddings and... | https://arxiv.org/abs/2601.07107 | Academic Papers | svg |
17c957c53184f771cbf68d8112652186069ea2a5d77d7967d2117bc581209f79 | 2026-01-13T00:00:00-05:00 | The Need for a Socially-Grounded Persona Framework for User Simulation | arXiv:2601.07110v1 Announce Type: new Abstract: Synthetic personas are widely used to condition large language models (LLMs) for social simulation, yet most personas are still constructed from coarse sociodemographic attributes or summaries. We revisit persona creation by introducing SCOPE, a socially grounded framewor... | https://arxiv.org/abs/2601.07110 | Academic Papers | svg |
3326cc0db7c214f701736ef698ddb91047c0669c00283a8cdb1b4a3b8b15f27d | 2026-01-13T00:00:00-05:00 | Few-shot Class-Incremental Learning via Generative Co-Memory Regularization | arXiv:2601.07117v1 Announce Type: new Abstract: Few-shot class-incremental learning (FSCIL) aims to incrementally learn models from a small amount of novel data, which requires strong representation and adaptation ability of models learned under few-example supervision to avoid catastrophic forgetting on old classes an... | https://arxiv.org/abs/2601.07117 | Academic Papers | svg |
4b6ac24ecb6661762ba208de2fb5f04adac93f1d303266c8a7c305bc4efb94bc | 2026-01-13T00:00:00-05:00 | Reward-Preserving Attacks For Robust Reinforcement Learning | arXiv:2601.07118v1 Announce Type: new Abstract: Adversarial robustness in RL is difficult because perturbations affect entire trajectories: strong attacks can break learning, while weak attacks yield little robustness, and the appropriate strength varies by state. We propose $\alpha$-reward-preserving attacks, which ad... | https://arxiv.org/abs/2601.07118 | Academic Papers | svg |
4b7626d4392fe7fcc86323ab93a37234093195e06b059aa80e4d8f51ba484ed4 | 2026-01-13T00:00:00-05:00 | SC-MII: Infrastructure LiDAR-based 3D Object Detection on Edge Devices for Split Computing with Multiple Intermediate Outputs Integration | arXiv:2601.07119v1 Announce Type: new Abstract: 3D object detection using LiDAR-based point cloud data and deep neural networks is essential in autonomous driving technology. However, deploying state-of-the-art models on edge devices present challenges due to high computational demands and energy consumption. Additiona... | https://arxiv.org/abs/2601.07119 | Academic Papers | svg |
1737511ca89596185be5ed4419ba16bd3d402472e823bf312a889f927ebd825b | 2026-01-13T00:00:00-05:00 | ReMIND: Orchestrating Modular Large Language Models for Controllable Serendipity A REM-Inspired System Design for Emergent Creative Ideation | arXiv:2601.07121v1 Announce Type: new Abstract: Large language models (LLMs) are used not only for problem solving but also for creative ideation; however, eliciting serendipitous insights that are both novel and internally coherent remains difficult. While stochastic sampling promotes novelty, it often degrades consis... | https://arxiv.org/abs/2601.07121 | Academic Papers | svg |
e6376969e8dc1e1b81f87bb5157ea424734791b93e35e2888d8d6e901d7a8eb8 | 2026-01-13T00:00:00-05:00 | Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Framework | arXiv:2601.07122v1 Announce Type: new Abstract: While virtualization and resource pooling empower cloud networks with structural flexibility and elastic scalability, they inevitably expand the attack surface and challenge cyber resilience. Reinforcement Learning (RL)-based defense strategies have been developed to opti... | https://arxiv.org/abs/2601.07122 | Academic Papers | svg |
b022356b243b4e215d6a1b698e3090d743aabf107d00761efeb0abc5a8c8152e | 2026-01-13T00:00:00-05:00 | ENTRA: Entropy-Based Redundancy Avoidance in Large Language Model Reasoning | arXiv:2601.07123v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) often suffer from overthinking, generating unnecessarily long reasoning chains even for simple tasks. This leads to substantial computational overhead with limited performance gain, primarily due to redundant verification and repetitive gener... | https://arxiv.org/abs/2601.07123 | Academic Papers | svg |
61e4d2f3d58a810b33e1f45c3a7e45979dcb61aa7f76e55c5ddda0d29ae5ee37 | 2026-01-13T00:00:00-05:00 | Towards Automated Diagnosis of Inherited Arrhythmias: Combined Arrhythmia Classification Using Lead-Aware Spatial Attention Networks | arXiv:2601.07124v1 Announce Type: new Abstract: Arrhythmogenic right ventricular cardiomyopathy (ARVC) and long QT syndrome (LQTS) are inherited arrhythmia syndromes associated with sudden cardiac death. Deep learning shows promise for ECG interpretation, but multi-class inherited arrhythmia classification with clinica... | https://arxiv.org/abs/2601.07124 | Academic Papers | svg |
9497fb8200e5bc72fb537a34346de93ce72ac0614ae71328a186e74afbbaf8e1 | 2026-01-13T00:00:00-05:00 | ReinPool: Reinforcement Learning Pooling Multi-Vector Embeddings for Retrieval System | arXiv:2601.07125v1 Announce Type: new Abstract: Multi-vector embedding models have emerged as a powerful paradigm for document retrieval, preserving fine-grained visual and textual details through token-level representations. However, this expressiveness comes at a staggering cost: storing embeddings for every token in... | https://arxiv.org/abs/2601.07125 | Academic Papers | svg |
fc075aee376fb1e8cc64be5d92903df702010256a675778195ba92bd4c1527ee | 2026-01-13T00:00:00-05:00 | Digital Twin for Ultra-Reliable & Low-Latency 6G Wireless Communications in Dense Urban City | arXiv:2601.07132v1 Announce Type: new Abstract: High-frequency deployments in dense cities are difficult to plan because coverage, interference, and service reliability depend sensitively on local morphology. This paper develops a geometric Digital Twin (DT) of the Sunway City and uses it to study the service implicati... | https://arxiv.org/abs/2601.07132 | Academic Papers | svg |
260135d044b59718d8bfd0bb598d19c8cd1bd2198e7cf4559d28274a97872c88 | 2026-01-13T00:00:00-05:00 | Geometry-Aware LoRaWAN Gateway Placement in Dense Urban Cities Using Digital Twins | arXiv:2601.07133v1 Announce Type: new Abstract: LoRaWAN deployments rely on rough range estimates or simplified propagation models to decide where to place/mount gateways. As a result, operators have limited visibility into how rooftop choice, streets, and building shadowing jointly affect coverage and reliability. Thi... | https://arxiv.org/abs/2601.07133 | Academic Papers | svg |
6c01dfa9c1111bb04a9ab299ab9ecd12a34f3cbf7b620c616322ebf1233787cd | 2026-01-13T00:00:00-05:00 | Proof of Reasoning for Privacy Enhanced Federated Blockchain Learning at the Edge | arXiv:2601.07134v1 Announce Type: new Abstract: Consensus mechanisms are the core of any blockchain system. However, the majority of these mechanisms do not target federated learning directly nor do they aid in the aggregation step. This paper introduces Proof of Reasoning (PoR), a novel consensus mechanism specificall... | https://arxiv.org/abs/2601.07134 | Academic Papers | svg |
508beaad1b02ca0449e38acb74e03d1363a2a5707836679fb87369a10b2eb5e0 | 2026-01-13T00:00:00-05:00 | A Large-Scale Study on the Development and Issues of Multi-Agent AI Systems | arXiv:2601.07136v1 Announce Type: new Abstract: The rapid emergence of multi-agent AI systems (MAS), including LangChain, CrewAI, and AutoGen, has shaped how large language model (LLM) applications are developed and orchestrated. However, little is known about how these systems evolve and are maintained in practice. Th... | https://arxiv.org/abs/2601.07136 | Academic Papers | svg |
c71d247e6a8c43c3bba1daf276ab46baf244e669dbd82724336d13c9da8d4699 | 2026-01-13T00:00:00-05:00 | Recovering polynomials over finite fields from noisy character values | arXiv:2601.07137v1 Announce Type: new Abstract: Let $g(X)$ be a polynomial over a finite field ${\mathbb F}_q$ with degree $o(q^{1/2})$, and let $\chi$ be the quadratic residue character. We give a polynomial time algorithm to recover $g(X)$ (up to perfect square factors) given the values of $\chi \circ g$ on ${\mathbb... | https://arxiv.org/abs/2601.07137 | Academic Papers | svg |
a4b4ddea0aad48f4e69cc75adee1f6711bc1992c98a4fb422d2f3102dece8b22 | 2026-01-13T00:00:00-05:00 | AdaField: Generalizable Surface Pressure Modeling with Physics-Informed Pre-training and Flow-Conditioned Adaptation | arXiv:2601.07139v1 Announce Type: new Abstract: The surface pressure field of transportation systems, including cars, trains, and aircraft, is critical for aerodynamic analysis and design. In recent years, deep neural networks have emerged as promising and efficient methods for modeling surface pressure field, being al... | https://arxiv.org/abs/2601.07139 | Academic Papers | svg |
1c36584d562e7d525fcc912f92156333d0337761161975541332e8fbe2043cdc | 2026-01-13T00:00:00-05:00 | MacPrompt: Maraconic-guided Jailbreak against Text-to-Image Models | arXiv:2601.07141v1 Announce Type: new Abstract: Text-to-image (T2I) models have raised increasing safety concerns due to their capacity to generate NSFW and other banned objects. To mitigate these risks, safety filters and concept removal techniques have been introduced to block inappropriate prompts or erase sensitive... | https://arxiv.org/abs/2601.07141 | Academic Papers | svg |
924f4bb22fd4b573a1cc2292341974b5c1bf87f9ce43dfab3a1efc9805ada661 | 2026-01-13T00:00:00-05:00 | EZBlender: Efficient 3D Editing with Plan-and-ReAct Agent | arXiv:2601.07143v1 Announce Type: new Abstract: As a cornerstone of the modern digital economy, 3D modeling and rendering demand substantial resources and manual effort when scene editing is performed in the traditional manner. Despite recent progress in VLM-based agents for 3D editing, the fundamental trade-off betwee... | https://arxiv.org/abs/2601.07143 | Academic Papers | svg |
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