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761efd395266be54e52fecf4c84679309d5c4844fd5f6f4e4d3f69c5bd6cf47e | 2026-01-21T00:00:00-05:00 | Exploration on Highly Dynamic Graphs | arXiv:2601.13047v1 Announce Type: new Abstract: We study the exploration problem by mobile agents in two prominent models of dynamic graphs: $1$-Interval Connectivity and Connectivity Time. The $1$-Interval Connectivity model was introduced by Kuhn et al.~[STOC 2010], and the Connectivity Time model was proposed by Mic... | https://arxiv.org/abs/2601.13047 | Academic Papers | svg |
58e414eae71148143fd12b0ccf3a97f2c3d13a0250450e0db6680ab215163141 | 2026-01-21T00:00:00-05:00 | Analysis of Long Range Dependency Understanding in State Space Models | arXiv:2601.13048v1 Announce Type: new Abstract: Although state-space models (SSMs) have demonstrated strong performance on long-sequence benchmarks, most research has emphasized predictive accuracy rather than interpretability. In this work, we present the first systematic kernel interpretability study of the diagonali... | https://arxiv.org/abs/2601.13048 | Academic Papers | svg |
2676c3b2d06bb85c6af6d8f33a273a6592ef091eee4b321a418577e5c3a77aac | 2026-01-21T00:00:00-05:00 | Profiling German Text Simplification with Interpretable Model-Fingerprints | arXiv:2601.13050v1 Announce Type: new Abstract: While Large Language Models (LLMs) produce highly nuanced text simplifications, developers currently lack tools for a holistic, efficient, and reproducible diagnosis of their behavior. This paper introduces the Simplification Profiler, a diagnostic toolkit that generates ... | https://arxiv.org/abs/2601.13050 | Academic Papers | svg |
75912413f39d0e97ccef6374aefe621e145139d568efb808f322d1f9836b2e2d | 2026-01-21T00:00:00-05:00 | GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure | arXiv:2601.13052v1 Announce Type: new Abstract: This paper presents GridNet-HD, a multi-modal dataset for 3D semantic segmentation of overhead electrical infrastructures, pairing high-density LiDAR with high-resolution oblique imagery. The dataset comprises 7,694 images and 2.5 billion points annotated into 11 classes,... | https://arxiv.org/abs/2601.13052 | Academic Papers | svg |
d2bcab840943aad5a8758096455e241bb81a2894ad71edb7ed7a34a76a204a1c | 2026-01-21T00:00:00-05:00 | TinyML-Enabled IoT for Sustainable Precision Irrigation | arXiv:2601.13054v1 Announce Type: new Abstract: Small-scale farming communities are disproportionately affected by water scarcity, erratic climate patterns, and a lack of access to advanced, affordable agricultural technologies. To address these challenges, this paper presents a novel, edge-first IoT framework that int... | https://arxiv.org/abs/2601.13054 | Academic Papers | svg |
aae88a8aa809057b8dc16ad1cecfe3ba699e67cb8be83efca632bb4f249fe656 | 2026-01-21T00:00:00-05:00 | Convex Model Predictive Control for Safe Output Consensus of Nonlinear Multi-Agent Systems | arXiv:2601.13057v1 Announce Type: new Abstract: Nonlinear dynamics and safety constraints typically result in a nonlinear programming problem when applying model predictive control to achieve safe output consensus. To avoid the heavy computational burden of solving a nonlinear programming problem directly, this paper p... | https://arxiv.org/abs/2601.13057 | Academic Papers | svg |
8f7ba256f2d2a4e204445fa57446b8e1f2506ffed2c41ced662140bc01a314da | 2026-01-21T00:00:00-05:00 | Prototype Learning-Based Few-Shot Segmentation for Low-Light Crack on Concrete Structures | arXiv:2601.13059v1 Announce Type: new Abstract: Crack detection is critical for concrete infrastructure safety, but real-world cracks often appear in low-light environments like tunnels and bridge undersides, degrading computer vision segmentation accuracy. Pixel-level annotation of low-light crack images is extremely ... | https://arxiv.org/abs/2601.13059 | Academic Papers | svg |
ba6a414d48e9748ffd0b1eeba2ca5f0059d6aaef1be67cc625ccf592907ab85b | 2026-01-21T00:00:00-05:00 | MagicGUI-RMS: A Multi-Agent Reward Model System for Self-Evolving GUI Agents via Automated Feedback Reflux | arXiv:2601.13060v1 Announce Type: new Abstract: Graphical user interface (GUI) agents are rapidly progressing toward autonomous interaction and reliable task execution across diverse applications. However, two central challenges remain unresolved: automating the evaluation of agent trajectories and generating high-qual... | https://arxiv.org/abs/2601.13060 | Academic Papers | svg |
7590c8f59664b5fc9e1fcad3c294c1ea9975c4e68f2fb920d64f736c6c3cace6 | 2026-01-21T00:00:00-05:00 | Two-timescale Optimization for Hybrid Mechanically and Electronically Tunable 6DMA Aided Communication | arXiv:2601.13064v1 Announce Type: new Abstract: This letter proposes a hybrid mechanically and electronically tunable six-dimensional movable antenna (6DMA) base station (BS) architecture for future wireless communication networks. Such BS consists of multiple antenna arrays that are mechanically movable along a circul... | https://arxiv.org/abs/2601.13064 | Academic Papers | svg |
40c5a92b0f0ea063ffaa23769513f2741e335e33659ae001bd32d63362025900 | 2026-01-21T00:00:00-05:00 | Stability of Information-Based Routing in Dynamic Transportation Networks | arXiv:2601.13066v1 Announce Type: new Abstract: Recent studies on transportation networks have shown that real-time route guidance can inadvertently induce congestion or oscillatory traffic patterns. Nevertheless, such technologies also offer a promising opportunity to manage traffic non-intrusively by shaping the info... | https://arxiv.org/abs/2601.13066 | Academic Papers | svg |
8ab33baab0e999a873288915a406b84737dddb27484fbb7e53e260469e3fb8aa | 2026-01-21T00:00:00-05:00 | METIS: Mentoring Engine for Thoughtful Inquiry & Solutions | arXiv:2601.13075v1 Announce Type: new Abstract: Many students lack access to expert research mentorship. We ask whether an AI mentor can move undergraduates from an idea to a paper. We build METIS, a tool-augmented, stage-aware assistant with literature search, curated guidelines, methodology checks, and memory. We eva... | https://arxiv.org/abs/2601.13075 | Academic Papers | svg |
ea0271b4bc0ff7f579524612b4f759d394da754726314ee8b7653905e66c5ac5 | 2026-01-21T00:00:00-05:00 | What's it like to be a chat? On the co-simulation of artificial minds in human-AI conversations | arXiv:2601.13081v1 Announce Type: new Abstract: Large Language Models (LLMs) can simulate person-like things which at least appear to have stable behavioural and psychological dispositions. Call these things characters. Are characters minded and psychologically continuous entities with mental states like beliefs, desir... | https://arxiv.org/abs/2601.13081 | Academic Papers | svg |
ba14d29a9362e2a669496145bc80a00004994a76e3537f4a7df9b30cc6f5e6da | 2026-01-21T00:00:00-05:00 | Adversarial News and Lost Profits: Manipulating Headlines in LLM-Driven Algorithmic Trading | arXiv:2601.13082v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly adopted in the financial domain. Their exceptional capabilities to analyse textual data make them well-suited for inferring the sentiment of finance-related news. Such feedback can be leveraged by algorithmic trading systems (... | https://arxiv.org/abs/2601.13082 | Academic Papers | svg |
dd9e594d69acd222f3c03f3efbc4e48b08e0aca4677fcbcb4a907762d27548c1 | 2026-01-21T00:00:00-05:00 | No Traffic to Cry: Traffic-Oblivious Link Deactivation for Green Traffic Engineering | arXiv:2601.13087v1 Announce Type: new Abstract: As internet traffic grows, the underlying infrastructure consumes increasing amounts of energy. During off-peak hours, large parts of the networks remain underutilized, presenting significant potential for energy savings. Existing Green Traffic Engineering approaches atte... | https://arxiv.org/abs/2601.13087 | Academic Papers | svg |
7acbf70c36bc871272e61437ac114cf72e6aaf90bd52f22e2da8074fbbd579b0 | 2026-01-21T00:00:00-05:00 | Exploiting Light To Enhance The Endurance and Navigation of Lighter-Than-Air Micro-Drones | arXiv:2601.13088v1 Announce Type: new Abstract: Micro-Unmanned Aerial Vehicles (UAVs) are rapidly expanding into tasks from inventory to environmental sensing, yet their short endurance and unreliable navigation in GPS-denied spaces limit deployment. Lighter-Than-Air (LTA) drones offer an energy-efficient alternative: ... | https://arxiv.org/abs/2601.13088 | Academic Papers | svg |
c1f331493b12dc9de14b2edd79b1e497ea7f9363341102050d941259a83800c1 | 2026-01-21T00:00:00-05:00 | Patient-Conditioned Adaptive Offsets for Reliable Diagnosis across Subgroups | arXiv:2601.13094v1 Announce Type: new Abstract: AI models for medical diagnosis often exhibit uneven performance across patient populations due to heterogeneity in disease prevalence, imaging appearance, and clinical risk profiles. Existing algorithmic fairness approaches typically seek to reduce such disparities by su... | https://arxiv.org/abs/2601.13094 | Academic Papers | svg |
fd732d1b215224fa5d3a22ff0ff827ad26b439731f19d028cc9bb8e3b1f8d592 | 2026-01-21T00:00:00-05:00 | LLM-VLM Fusion Framework for Autonomous Maritime Port Inspection using a Heterogeneous UAV-USV System | arXiv:2601.13096v1 Announce Type: new Abstract: Maritime port inspection plays a critical role in ensuring safety, regulatory compliance, and operational efficiency in complex maritime environments. However, existing inspection methods often rely on manual operations and conventional computer vision techniques that lac... | https://arxiv.org/abs/2601.13096 | Academic Papers | svg |
31c8764ea331170b1565882628edce50de0613b5becd60b350a1762fcfa1c64b | 2026-01-21T00:00:00-05:00 | RM -RF: Reward Model for Run-Free Unit Test Evaluation | arXiv:2601.13097v1 Announce Type: new Abstract: We present RM-RF, a lightweight reward model for run-free evaluation of automatically generated unit tests. Instead of repeatedly compiling and executing candidate tests, RM-RF predicts - from source and test code alone - three execution-derived signals: (1) whether the a... | https://arxiv.org/abs/2601.13097 | Academic Papers | svg |
61ef22f84d9ef1bf7d4a363adbf201df6d1e0ce77c8d7c83391372e56957ae24 | 2026-01-21T00:00:00-05:00 | Exploring the Impacts of Background Noise on Auditory Stimuli of Audio-Visual eHMIs for Hearing, Deaf, and Hard-of-Hearing People | arXiv:2601.13098v1 Announce Type: new Abstract: External Human-Machine Interfaces (eHMIs) have been proposed to enhance communication between automated vehicles (AVs) and pedestrians, with growing interest in multi-modal designs such as audio-visual eHMIs. Just as poor lighting can impair visual cues, a loud background... | https://arxiv.org/abs/2601.13098 | Academic Papers | svg |
d1319ea8045fc834b19602465a85b8de219b16e134a19a08228eaac1304b6273 | 2026-01-21T00:00:00-05:00 | Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs | arXiv:2601.13099v1 Announce Type: new Abstract: Arabic is a highly diglossic language where most daily communication occurs in regional dialects rather than Modern Standard Arabic. Despite this, machine translation (MT) systems often generalize poorly to dialectal input, limiting their utility for millions of speakers.... | https://arxiv.org/abs/2601.13099 | Academic Papers | svg |
329e549180159c3f708ca0481177c6eb619df4a759910a644091106b2b26be57 | 2026-01-21T00:00:00-05:00 | Recursive Meta-Distillation: An Axiomatic Framework for Iterative Knowledge Refinement | arXiv:2601.13100v1 Announce Type: new Abstract: Recent work in probability-domain knowledge distillation has established axiomatic frameworks for temperature scaling, multi-teacher aggregation, and bias-variance trade-offs in single-stage settings. However, the mathematical behavior of recursive or multi-generation dis... | https://arxiv.org/abs/2601.13100 | Academic Papers | svg |
e0ba48ce9050c19444b23a5c7709477816d7525f9737150f90c93586c6966f29 | 2026-01-21T00:00:00-05:00 | Leveraging Lora Fine-Tuning and Knowledge Bases for Construction Identification | arXiv:2601.13105v1 Announce Type: new Abstract: This study investigates the automatic identification of the English ditransitive construction by integrating LoRA-based fine-tuning of a large language model with a Retrieval-Augmented Generation (RAG) framework.A binary classification task was conducted on annotated data... | https://arxiv.org/abs/2601.13105 | Academic Papers | svg |
21e2a795061964fa1e0b79b26e9eb69e009efb4aae0460adb0ed9fc395fb21b1 | 2026-01-21T00:00:00-05:00 | Stochastic Gradient Descent for Nonlinear Inverse Problems in Banach Spaces | arXiv:2601.13110v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) and its variants are widely used and highly effective optimization methods in machine learning, especially for neural network training. By using a single datum or a small subset of the data, selected randomly at each iteration, SGD scales... | https://arxiv.org/abs/2601.13110 | Academic Papers | svg |
8fd0c120a24db7f5915278ea9bb56cf7c31f285645aaae0dddbf6445d7350cb2 | 2026-01-21T00:00:00-05:00 | CORE-T: COherent REtrieval of Tables for Text-to-SQL | arXiv:2601.13111v1 Announce Type: new Abstract: Realistic text-to-SQL workflows often require joining multiple tables. As a result, accurately retrieving the relevant set of tables becomes a key bottleneck for end-to-end performance. We study an open-book setting where queries must be answered over large, heterogeneous... | https://arxiv.org/abs/2601.13111 | Academic Papers | svg |
cb3462cf11cc42c5ad0091128c84892983569ca11915da62a3d477d89655c84b | 2026-01-21T00:00:00-05:00 | CODE: A Contradiction-Based Deliberation Extension Framework for Overthinking Attacks on Retrieval-Augmented Generation | arXiv:2601.13112v1 Announce Type: new Abstract: Introducing reasoning models into Retrieval-Augmented Generation (RAG) systems enhances task performance through step-by-step reasoning, logical consistency, and multi-step self-verification. However, recent studies have shown that reasoning models suffer from overthinkin... | https://arxiv.org/abs/2601.13112 | Academic Papers | svg |
773246150a8183a0675088e23aad99ad3eae0dfd55059079aece42358a3c27f0 | 2026-01-21T00:00:00-05:00 | IntAgent: NWDAF-Based Intent LLM Agent Towards Advanced Next Generation Networks | arXiv:2601.13114v1 Announce Type: new Abstract: Intent-based networks (IBNs) are gaining prominence as an innovative technology that automates network operations through high-level request statements, defining what the network should achieve. In this work, we introduce IntAgent, an intelligent intent LLM agent that int... | https://arxiv.org/abs/2601.13114 | Academic Papers | svg |
fe543412fa761af9a5a4ab973653e7695910a4fbb77511582e3eb80cd910c183 | 2026-01-21T00:00:00-05:00 | Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning | arXiv:2601.13115v1 Announce Type: new Abstract: Large Language Models (LLMs) have become a popular interface for human-AI interaction, supporting information seeking and task assistance through natural, multi-turn dialogue. To respond to users within multi-turn dialogues, the context-dependent user intent evolves acros... | https://arxiv.org/abs/2601.13115 | Academic Papers | svg |
b0f541939907fdb2649caf909642cff4563d12cbb807ff9d2d7a4a60ef0f6cfd | 2026-01-21T00:00:00-05:00 | xBound: Join Size Lower Bounds | arXiv:2601.13117v1 Announce Type: new Abstract: Cloud database vendors invest substantial resources into their query optimizers, and for good reason. Cardinality estimation, a cornerstone of the optimizer, is critical for the selection of efficient query plans, as well as downstream tasks such as resource allocation an... | https://arxiv.org/abs/2601.13117 | Academic Papers | svg |
a240bca0b6fde1ed7a3dd36d803a72d08fa029ac083f26c5c0e3a09231300c40 | 2026-01-21T00:00:00-05:00 | Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization | arXiv:2601.13118v1 Announce Type: new Abstract: Large Language Models (LLMs) are nowadays extensively used for various types of software engineering tasks, primarily code generation. Previous research has shown how suitable prompt engineering could help developers in improving their code generation prompts. However, so... | https://arxiv.org/abs/2601.13118 | Academic Papers | svg |
b99a25bd2f9ca9e544bc56ec25c3869955087c48d4952a7f9f89b8a1a34fb42d | 2026-01-21T00:00:00-05:00 | Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forward | arXiv:2601.13122v1 Announce Type: new Abstract: Modern general-purpose AI systems made using large language and vision models, are capable of performing a range of tasks like writing text articles, generating and debugging codes, querying databases, and translating from one language to another, which has made them quit... | https://arxiv.org/abs/2601.13122 | Academic Papers | svg |
29ad0f0ff402b916c210ec0281c62bc74ead51a0f917a38d10e56f1f5ea4c645 | 2026-01-21T00:00:00-05:00 | A Streamlined Attention-Based Network for Descriptor Extraction | arXiv:2601.13126v1 Announce Type: new Abstract: We introduce SANDesc, a Streamlined Attention-Based Network for Descriptor extraction that aims to improve on existing architectures for keypoint description. Our descriptor network learns to compute descriptors that improve matching without modifying the underlying keypo... | https://arxiv.org/abs/2601.13126 | Academic Papers | svg |
9d8acabb3075b05ff3eb4da1bee9f92f6b36549b2edee4459bfa3cc5c09f510b | 2026-01-21T00:00:00-05:00 | PhaseMark: A Post-hoc, Optimization-Free Watermarking of AI-generated Images in the Latent Frequency Domain | arXiv:2601.13128v1 Announce Type: new Abstract: The proliferation of hyper-realistic images from Latent Diffusion Models (LDMs) demands robust watermarking, yet existing post-hoc methods are prohibitively slow due to iterative optimization or inversion processes. We introduce PhaseMark, a single-shot, optimization-free... | https://arxiv.org/abs/2601.13128 | Academic Papers | svg |
7790ddb68d8cc9c9eebb8883babdf9906d1aa5a6f0382b1d8e0183847cb3fd21 | 2026-01-21T00:00:00-05:00 | GaussExplorer: 3D Gaussian Splatting for Embodied Exploration and Reasoning | arXiv:2601.13132v1 Announce Type: new Abstract: We present GaussExplorer, a framework for embodied exploration and reasoning built on 3D Gaussian Splatting (3DGS). While prior approaches to language-embedded 3DGS have made meaningful progress in aligning simple text queries with Gaussian embeddings, they are generally ... | https://arxiv.org/abs/2601.13132 | Academic Papers | svg |
6d16ce18c2ca01254ab610cc3746702948718218c3d43598b93278c540e8721d | 2026-01-21T00:00:00-05:00 | CLIP-Guided Adaptable Self-Supervised Learning for Human-Centric Visual Tasks | arXiv:2601.13133v1 Announce Type: new Abstract: Human-centric visual analysis plays a pivotal role in diverse applications, including surveillance, healthcare, and human-computer interaction. With the emergence of large-scale unlabeled human image datasets, there is an increasing need for a general unsupervised pre-tra... | https://arxiv.org/abs/2601.13133 | Academic Papers | svg |
a7aefa98b111ae8a1681edb40b6bc035e080058810928d58ae1d75fa87602eaa | 2026-01-21T00:00:00-05:00 | Earth Embeddings as Products: Taxonomy, Ecosystem, and Standardized Access | arXiv:2601.13134v1 Announce Type: new Abstract: Geospatial Foundation Models (GFMs) provide powerful representations, but high compute costs hinder their widespread use. Pre-computed embedding data products offer a practical "frozen" alternative, yet they currently exist in a fragmented ecosystem of incompatible format... | https://arxiv.org/abs/2601.13134 | Academic Papers | svg |
526e868411d297b767ff474e6de1a930612c5f959891b0e5915dc165aa227126 | 2026-01-21T00:00:00-05:00 | Adversarial Alignment: Ensuring Value Consistency in Large Language Models for Sensitive Domains | arXiv:2601.13137v1 Announce Type: new Abstract: With the wide application of large language models (LLMs), the problems of bias and value inconsistency in sensitive domains have gradually emerged, especially in terms of race, society and politics. In this paper, we propose an adversarial alignment framework, which enha... | https://arxiv.org/abs/2601.13137 | Academic Papers | svg |
939265498c534950b7de4c8add8b6c12518521613d96ac0590b31e4fdba27c8c | 2026-01-21T00:00:00-05:00 | From Human to Machine Refactoring: Assessing GPT-4's Impact on Python Class Quality and Readability | arXiv:2601.13139v1 Announce Type: new Abstract: Refactoring is a software engineering practice that aims to improve code quality without altering program behavior. Although automated refactoring tools have been extensively studied, their practical applicability remains limited. Recent advances in Large Language Models ... | https://arxiv.org/abs/2601.13139 | Academic Papers | svg |
7815fe4c5b1bf5b0033ef94e8d732ebc8a18c7463617359c0eb3843a8c3f0f15 | 2026-01-21T00:00:00-05:00 | TVWorld: Foundations for Remote-Control TV Agents | arXiv:2601.13142v1 Announce Type: new Abstract: Recent large vision-language models (LVLMs) have demonstrated strong potential for device control. However, existing research has primarily focused on point-and-click (PnC) interaction, while remote-control (RC) interaction commonly encountered in everyday TV usage remain... | https://arxiv.org/abs/2601.13142 | Academic Papers | svg |
e67d3b3088fdeeda56d433198ae8f4173cb1c8c5e487ecc239ca35f72f04f015 | 2026-01-21T00:00:00-05:00 | FastAV: Efficient Token Pruning for Audio-Visual Large Language Model Inference | arXiv:2601.13143v1 Announce Type: new Abstract: In this work, we present FastAV, the first token pruning framework tailored for audio-visual large language models (AV-LLMs). While token pruning has been actively explored in standard large language models (LLMs) and vision-language models (LVLMs), its application to AV-... | https://arxiv.org/abs/2601.13143 | Academic Papers | svg |
c584d312e0c4db64c67a1f1eeeb8b9543e0a07a833a5e7db524c74ce5767adeb | 2026-01-21T00:00:00-05:00 | OPTIMUM-DERAM: Highly Consistent, Scalable, and Secure Multi-Object Memory using RLNC | arXiv:2601.13146v1 Announce Type: new Abstract: This paper introduces OPTIMUM-DERAM, a highly consistent, scalable, secure, and decentralized shared memory solution. Traditional distributed shared memory implementations offer multi-object support by multi-threading a single object memory instance over the same set of d... | https://arxiv.org/abs/2601.13146 | Academic Papers | svg |
f0dd3c0d312ce61ab1c2bfe5f2a1a3898888aeef49926d14bf23de3abc8c89d0 | 2026-01-21T00:00:00-05:00 | ICo3D: An Interactive Conversational 3D Virtual Human | arXiv:2601.13148v1 Announce Type: new Abstract: This work presents Interactive Conversational 3D Virtual Human (ICo3D), a method for generating an interactive, conversational, and photorealistic 3D human avatar. Based on multi-view captures of a subject, we create an animatable 3D face model and a dynamic 3D body model... | https://arxiv.org/abs/2601.13148 | Academic Papers | svg |
a87b6e4cb6044f5cb172d87716053b79112b45f3aaee471a664e5e784f47afe2 | 2026-01-21T00:00:00-05:00 | Probe and Skip: Self-Predictive Token Skipping for Efficient Long-Context LLM Inference | arXiv:2601.13155v1 Announce Type: new Abstract: Long-context inference enhances the reasoning capability of Large Language Models (LLMs) while incurring significant computational overhead. Token-oriented methods, such as pruning and skipping, have shown promise in reducing inference latency, but still suffer from inher... | https://arxiv.org/abs/2601.13155 | Academic Papers | svg |
b372dd1e7d9e658aeb4f2162f74aee8e5c1e7717544eaee692db54582e5806b1 | 2026-01-21T00:00:00-05:00 | Training instability in deep learning follows low-dimensional dynamical principles | arXiv:2601.13160v1 Announce Type: new Abstract: Deep learning systems achieve remarkable empirical performance, yet the stability of the training process itself remains poorly understood. Training unfolds as a high-dimensional dynamical system in which small perturbations to optimization, data, parameters, or learning ... | https://arxiv.org/abs/2601.13160 | Academic Papers | svg |
ef2617ec9bc48c3f98ebf6fbc90d277959f44861f62a124270b503bfd26db2cf | 2026-01-21T00:00:00-05:00 | NeuroShield: A Neuro-Symbolic Framework for Adversarial Robustness | arXiv:2601.13162v1 Announce Type: new Abstract: Adversarial vulnerability and lack of interpretability are critical limitations of deep neural networks, especially in safety-sensitive settings such as autonomous driving. We introduce \DesignII, a neuro-symbolic framework that integrates symbolic rule supervision into n... | https://arxiv.org/abs/2601.13162 | Academic Papers | svg |
60f41acab5605389f850e81d23c2b159847463b9d1cd15e01ad2675703c1f6e5 | 2026-01-21T00:00:00-05:00 | Optimistic Imprecise Shortest Watchtower in 1.5D and 2.5D | arXiv:2601.13165v1 Announce Type: new Abstract: A 1.5D imprecise terrain is an $x$-monotone polyline with fixed $x$-coordinates, the $y$-coordinate of each vertex is not fixed but is constrained to be in a given vertical interval. A 2.5D imprecise terrain is a triangulation with fixed $x$ and $y$-coordinates, but the $... | https://arxiv.org/abs/2601.13165 | Academic Papers | svg |
c568f33aff7fea9b24edf2f02cafa21bb9a1d98a9626bac1f70bf38f4174988e | 2026-01-21T00:00:00-05:00 | From 100,000+ images to winning the first brain MRI foundation model challenges: Sharing lessons and models | arXiv:2601.13166v1 Announce Type: new Abstract: Developing Foundation Models for medical image analysis is essential to overcome the unique challenges of radiological tasks. The first challenges of this kind for 3D brain MRI, SSL3D and FOMO25, were held at MICCAI 2025. Our solution ranked first in tracks of both contes... | https://arxiv.org/abs/2601.13166 | Academic Papers | svg |
1437f6432f80419c3b83b63db4079ea70512d5ddaba9afa61fa6ac2dfa1de13b | 2026-01-21T00:00:00-05:00 | QoS-Aware Energy Optimization via Cell Switching in Heterogeneous Networks | arXiv:2601.13174v1 Announce Type: new Abstract: The growing demand for mobile data services in dense urban areas has intensified the need for energy-efficient radio access networks (RANs) in future 6G systems. In this context, one promising strategy is cell switching (CS), which dynamically deactivates underutilized sm... | https://arxiv.org/abs/2601.13174 | Academic Papers | svg |
e5f0c136011460bc78e956049f832a46600f67baf33b77b51dbb326dc07fb3db | 2026-01-21T00:00:00-05:00 | Helical Tendon-Driven Continuum Robot with Programmable Follow-the-Leader Operation | arXiv:2601.13177v1 Announce Type: new Abstract: Spinal cord stimulation (SCS) is primarily utilized for pain management and has recently demonstrated efficacy in promoting functional recovery in patients with spinal cord injury. Effective stimulation of motor neurons ideally requires the placement of SCS leads in the v... | https://arxiv.org/abs/2601.13177 | Academic Papers | svg |
e31588f599b650092f77c0f52f8efc4fc853b235eb6236b2dcfde28e99eeb3f5 | 2026-01-21T00:00:00-05:00 | Medical Triage as Pairwise Ranking: A Benchmark for Urgency in Patient Portal Messages | arXiv:2601.13178v1 Announce Type: new Abstract: Medical triage is the task of allocating medical resources and prioritizing patients based on medical need. This paper introduces the first large-scale public dataset for studying medical triage in the context of asynchronous outpatient portal messages. Our novel task for... | https://arxiv.org/abs/2601.13178 | Academic Papers | svg |
1fcaae2dd9f90e41e175ad4a1321cb7956fcac74da584b57ce55d3fcfd2da422 | 2026-01-21T00:00:00-05:00 | OpenExempt: A Diagnostic Benchmark for Legal Reasoning and a Framework for Creating Custom Benchmarks on Demand | arXiv:2601.13183v1 Announce Type: new Abstract: Reasoning benchmarks have played a crucial role in the progress of language models. Yet rigorous evaluation remains a significant challenge as static question-answer pairs provide only a snapshot of performance, compressing complex behavior into a single accuracy metric. ... | https://arxiv.org/abs/2601.13183 | Academic Papers | svg |
5b7f5a6562aea64f0342161726e9b84a14ebcda6820b668a8d70d0b0ce1da68c | 2026-01-21T00:00:00-05:00 | Prompt Injection Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching | arXiv:2601.13186v1 Announce Type: new Abstract: Prompt injection remains a central obstacle to the safe deployment of large language models, particularly in multi-agent settings where intermediate outputs can propagate or amplify malicious instructions. Building on earlier work that introduced a four-metric Total Injec... | https://arxiv.org/abs/2601.13186 | Academic Papers | svg |
6bf5e57613d372e1d9e7a82d4cae6ce07e091fddd296d4bf722914f11ce9299d | 2026-01-21T00:00:00-05:00 | Scientific production in the era of Large Language Models | arXiv:2601.13187v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly reshaping scientific research. We analyze these changes in multiple, large-scale datasets with 2.1M preprints, 28K peer review reports, and 246M online accesses to scientific documents. We find: 1) scientists adopting LLMs to draft... | https://arxiv.org/abs/2601.13187 | Academic Papers | svg |
d87e1fd17ba33c2f7e45ca6de3f9922cb662ed677208e231c78634337b722e73 | 2026-01-21T00:00:00-05:00 | Negotiating Relationships with ChatGPT: Perceptions, External Influences, and Strategies for AI Companionship | arXiv:2601.13188v1 Announce Type: new Abstract: Individuals are turning to increasingly anthropomorphic, general-purpose chatbots for AI companionship, rather than roleplay-specific platforms. However, not much is known about how individuals perceive and conduct their relationships with general-purpose chatbots. We ana... | https://arxiv.org/abs/2601.13188 | Academic Papers | svg |
251ad24b328cae97c943740abfa7044ef66698b32edc69a23911553ac2f93a04 | 2026-01-21T00:00:00-05:00 | LAViG-FLOW: Latent Autoregressive Video Generation for Fluid Flow Simulations | arXiv:2601.13190v1 Announce Type: new Abstract: Modeling and forecasting subsurface multiphase fluid flow fields underpin applications ranging from geological CO2 sequestration (GCS) operations to geothermal production. This is essential for ensuring both operational performance and long-term safety. While high fidelit... | https://arxiv.org/abs/2601.13190 | Academic Papers | svg |
37099b3b5e3190ba11c9ad8dc3c045c9aa1f063bf1d4da0cdb146d13067d7e7f | 2026-01-21T00:00:00-05:00 | Active Informative Planning for UAV-based Weed Mapping using Discrete Gaussian Process Representations | arXiv:2601.13196v1 Announce Type: new Abstract: Accurate agricultural weed mapping using unmanned aerial vehicles (UAVs) is crucial for precision farming. While traditional methods rely on rigid, pre-defined flight paths and intensive offline processing, informative path planning (IPP) offers a way to collect data adap... | https://arxiv.org/abs/2601.13196 | Academic Papers | svg |
02ac85b63da692f434c2003a7af2ee7469e953d28d60820698aa7c0233bee4e7 | 2026-01-21T00:00:00-05:00 | Diffusion-Driven Synthetic Tabular Data Generation for Enhanced DoS/DDoS Attack Classification | arXiv:2601.13197v1 Announce Type: new Abstract: Class imbalance refers to a situation where certain classes in a dataset have significantly fewer samples than oth- ers, leading to biased model performance. Class imbalance in network intrusion detection using Tabular Denoising Diffusion Probability Models (TabDDPM) for ... | https://arxiv.org/abs/2601.13197 | Academic Papers | svg |
2d05d79e41fb09563c8b55def587a41620c5cc6546fb237c5e92e944f4cc11a4 | 2026-01-21T00:00:00-05:00 | The Achilles' Heel of Angular Margins: A Chebyshev Polynomial Fix for Speaker Verification | arXiv:2601.13198v1 Announce Type: new Abstract: Angular margin losses, such as AAM-Softmax, have become the de facto in speaker and face verification. Their success hinges on directly manipulating the angle between features and class prototypes. However, this manipulation relies on the arccos function to recover the an... | https://arxiv.org/abs/2601.13198 | Academic Papers | svg |
38051e24d00d89b7deb8136766376dcb20f66e9a4f4ad5ca6b25358985577e98 | 2026-01-21T00:00:00-05:00 | Emissions and cost tradeoffs of time-matched clean electricity procurement under inter-annual weather variability: case study of hydrogen production | arXiv:2601.13202v1 Announce Type: new Abstract: Time-matching requirements (TMRs) for clean electricity procurement are increasingly adopted in voluntary corporate sustainability initiatives and regulatory frameworks. While prior research has evaluated cost and emissions impacts of hourly vs. annual TMR, these studies ... | https://arxiv.org/abs/2601.13202 | Academic Papers | svg |
16a6fbc612d5855a4e5ee2322e4431f814018fc1fc6107c14157067e8d36f965 | 2026-01-21T00:00:00-05:00 | Real-Time Deadlines Reveal Temporal Awareness Failures in LLM Strategic Dialogues | arXiv:2601.13206v1 Announce Type: new Abstract: Large Language Models (LLMs) generate text token-by-token in discrete time, yet real-world communication, from therapy sessions to business negotiations, critically depends on continuous time constraints. Current LLM architectures and evaluation protocols rarely test for ... | https://arxiv.org/abs/2601.13206 | Academic Papers | svg |
071665f3d180574cb36813b033c658f43535b3a87d07e3c5a45e0c3844677456 | 2026-01-21T00:00:00-05:00 | GTPred: Benchmarking MLLMs for Interpretable Geo-localization and Time-of-capture Prediction | arXiv:2601.13207v1 Announce Type: new Abstract: Geo-localization aims to infer the geographic location where an image was captured using observable visual evidence. Traditional methods achieve impressive results through large-scale training on massive image corpora. With the emergence of multi-modal large language mode... | https://arxiv.org/abs/2601.13207 | Academic Papers | svg |
e302a08bfb1b781d03d71da7c8e9b5728b634fbf979c45a337d5ac91d042c104 | 2026-01-21T00:00:00-05:00 | Rethinking Skip Connections: Additive U-Net for Robust and Interpretable Denoising | arXiv:2601.13208v1 Announce Type: new Abstract: Skip connections are central to U-Net architectures for image denoising, but standard concatenation doubles channel dimensionality and obscures information flow, allowing uncontrolled noise transfer. We propose the Additive U-Net, which replaces concatenative skips with g... | https://arxiv.org/abs/2601.13208 | Academic Papers | svg |
b67945766cac42fa45c32a961dd774e3dbe9c2a693b0957d827f6a0f433d49f1 | 2026-01-21T00:00:00-05:00 | Conflict Detection in AI-RAN: Efficient Interaction Learning and Autonomous Graph Reconstruction | arXiv:2601.13213v1 Announce Type: new Abstract: Artificial Intelligence (AI)-native mobile networks represent a fundamental step toward 6G, where learning, inference, and decision making are embedded into the Radio Access Network (RAN) itself. In such networks, multiple AI agents optimize the network to achieve distinc... | https://arxiv.org/abs/2601.13213 | Academic Papers | svg |
3709f472589f472f89f292771ac9baa6c40b8c62254b387e7ec4753047f95ac2 | 2026-01-21T00:00:00-05:00 | An AMP-Based Asymptotic Analysis For Nonlinear One-Bit Precoding | arXiv:2601.13214v1 Announce Type: new Abstract: This paper focuses on the asymptotic analysis of a class of nonlinear one-bit precoding schemes under Rayleigh fading channels. The considered scheme employs a convex-relaxation-then-quantization (CRQ) approach to the well-known minimum mean square error (MMSE) model, whi... | https://arxiv.org/abs/2601.13214 | Academic Papers | svg |
f823ee24fd20e6abd0eb2e94706f8035f371225e3cacc7383adeb15fce31e5c0 | 2026-01-21T00:00:00-05:00 | On the Reliability of Estimation Bounds in Low-SNR Bistatic ISAC | arXiv:2601.13216v1 Announce Type: new Abstract: This paper explores a bistatic Integrated Sensing and Communication (ISAC) framework, where a base station transmits communication signal that serve both direct communication with a user and multi-target parameter estimation through reflections captured by a separate sens... | https://arxiv.org/abs/2601.13216 | Academic Papers | svg |
afc01f438235ba88065fa97bbe8475ab3f39fb4976097434731c3d7c7b72e661 | 2026-01-21T00:00:00-05:00 | Beyond Single-shot Writing: Deep Research Agents are Unreliable at Multi-turn Report Revision | arXiv:2601.13217v1 Announce Type: new Abstract: Existing benchmarks for Deep Research Agents (DRAs) treat report generation as a single-shot writing task, which fundamentally diverges from how human researchers iteratively draft and revise reports via self-reflection or peer feedback. Whether DRAs can reliably revise r... | https://arxiv.org/abs/2601.13217 | Academic Papers | svg |
a34c00cdff18088026d8cd0bc1c857a2776829bd002c3a810425b3e5236e1997 | 2026-01-21T00:00:00-05:00 | ObjectVisA-120: Object-based Visual Attention Prediction in Interactive Street-crossing Environments | arXiv:2601.13218v1 Announce Type: new Abstract: The object-based nature of human visual attention is well-known in cognitive science, but has only played a minor role in computational visual attention models so far. This is mainly due to a lack of suitable datasets and evaluation metrics for object-based attention. To ... | https://arxiv.org/abs/2601.13218 | Academic Papers | svg |
84b69912bc8859a01861b647a8fd607b4ebc5d02a1efd4f7590241116d5d4900 | 2026-01-21T00:00:00-05:00 | The Energy-Throughput Trade-off in Lossless-Compressed Source Code Storage | arXiv:2601.13220v1 Announce Type: new Abstract: Retrieving data from large-scale source code archives is vital for AI training, neural-based software analysis, and information retrieval, to cite a few. This paper studies and experiments with the design of a compressed key-value store for the indexing of large-scale sou... | https://arxiv.org/abs/2601.13220 | Academic Papers | svg |
35ec651cbaf55d08973de90bd96aaf908723eca0d3549f4533a42d54de6015ca | 2026-01-21T00:00:00-05:00 | Incorporating Q&A Nuggets into Retrieval-Augmented Generation | arXiv:2601.13222v1 Announce Type: new Abstract: RAGE systems integrate ideas from automatic evaluation (E) into Retrieval-augmented Generation (RAG). As one such example, we present Crucible, a Nugget-Augmented Generation System that preserves explicit citation provenance by constructing a bank of Q&A nuggets from ... | https://arxiv.org/abs/2601.13222 | Academic Papers | svg |
b5206d397a046b0d64ceb6f0647ad6c847953daa7427209653dc4283df50f453 | 2026-01-21T00:00:00-05:00 | Functional Logic Program Transformations | arXiv:2601.13224v1 Announce Type: new Abstract: Many tools used to process programs, like compilers, analyzers, or verifiers, perform transformations on their intermediate program representation, like abstract syntax trees. Implementing such program transformations is a non-trivial task, since it is necessary to iterat... | https://arxiv.org/abs/2601.13224 | Academic Papers | svg |
328db5b8c526664e68d65c11e2d8c6b1217ed4e40da184c3bf3cc87a21f60da0 | 2026-01-21T00:00:00-05:00 | Not all Blends are Equal: The BLEMORE Dataset of Blended Emotion Expressions with Relative Salience Annotations | arXiv:2601.13225v1 Announce Type: new Abstract: Humans often experience not just a single basic emotion at a time, but rather a blend of several emotions with varying salience. Despite the importance of such blended emotions, most video-based emotion recognition approaches are designed to recognize single emotions only... | https://arxiv.org/abs/2601.13225 | Academic Papers | svg |
529b2d7238c259745462f7e12e08b2f6114660a268c8aa6c4df469bbcc888526 | 2026-01-21T00:00:00-05:00 | Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets? | arXiv:2601.13227v1 Announce Type: new Abstract: RAG systems are increasingly evaluated and optimized using LLM judges, an approach that is rapidly becoming the dominant paradigm for system assessment. Nugget-based approaches in particular are now embedded not only in evaluation frameworks but also in the architectures ... | https://arxiv.org/abs/2601.13227 | Academic Papers | svg |
8b969d382af0e78fb81e84c61dc763ade4e3e8e7987236636f7b70e0be3d8e9c | 2026-01-21T00:00:00-05:00 | Autoregressive Models Rival Diffusion Models at ANY-ORDER Generation | arXiv:2601.13228v1 Announce Type: new Abstract: Diffusion language models enable any-order generation and bidirectional conditioning, offering appealing flexibility for tasks such as infilling, rewriting, and self-correction. However, their formulation-predicting one part of a sequence from another within a single-step... | https://arxiv.org/abs/2601.13228 | Academic Papers | svg |
4922308cea060fd7937caf0edc115a489f0d9de4544b06bbe60704d729071852 | 2026-01-21T00:00:00-05:00 | Towards Matrix-Free Patch Smoothers for the Stokes Problem: Evaluating Local p-Multigrid Solvers | arXiv:2601.13230v1 Announce Type: new Abstract: Vertex-patch smoothers offer an effective strategy for achieving robust geometric multigrid convergence for the Stokes equations, particularly in the context of high-order finite elements. However, their practical efficiency is often limited by the computational cost of s... | https://arxiv.org/abs/2601.13230 | Academic Papers | svg |
495531de904b32aae96dfd08fedac289e5d9083692702e905094ccdff8c338e2 | 2026-01-21T00:00:00-05:00 | MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation | arXiv:2601.13232v1 Announce Type: new Abstract: Accelerated materials discovery is critical for addressing global challenges. However, developing new laboratory workflows relies heavily on real-world experimental trials, and this can hinder scalability because of the need for numerous physical make-and-test iterations.... | https://arxiv.org/abs/2601.13232 | Academic Papers | svg |
02cba6a34a4abc70206d6fffbff58c36322dc0eec5c043b95d237d724a0ab14c | 2026-01-21T00:00:00-05:00 | RAG: A Random-Forest-Based Generative Design Framework for Uncertainty-Aware Design of Metamaterials with Complex Functional Response Requirements | arXiv:2601.13233v1 Announce Type: new Abstract: Metamaterials design for advanced functionality often entails the inverse design on nonlinear and condition-dependent responses (e.g., stress-strain relation and dispersion relation), which are described by continuous functions. Most existing design methods focus on vecto... | https://arxiv.org/abs/2601.13233 | Academic Papers | svg |
ff85b94057156a904d567bc2b1434f2e7c8613f580714f0a720081ee1d5f5deb | 2026-01-21T00:00:00-05:00 | ConvMambaNet: A Hybrid CNN-Mamba State Space Architecture for Accurate and Real-Time EEG Seizure Detection | arXiv:2601.13234v1 Announce Type: new Abstract: Epilepsy is a chronic neurological disorder marked by recurrent seizures that can severely impact quality of life. Electroencephalography (EEG) remains the primary tool for monitoring neural activity and detecting seizures, yet automated analysis remains challenging due t... | https://arxiv.org/abs/2601.13234 | Academic Papers | svg |
db576f4a6111e0c1e33536ced12a83b70a15ee26a3156cf2283648dee720d03a | 2026-01-21T00:00:00-05:00 | RubRIX: Rubric-Driven Risk Mitigation in Caregiver-AI Interactions | arXiv:2601.13235v1 Announce Type: new Abstract: Caregivers seeking AI-mediated support express complex needs -- information-seeking, emotional validation, and distress cues -- that warrant careful evaluation of response safety and appropriateness. Existing AI evaluation frameworks, primarily focused on general risks (t... | https://arxiv.org/abs/2601.13235 | Academic Papers | svg |
74b2a1cca560b2bad9dd5ea22acfa0d2375a87b4900495df903f467db86437dc | 2026-01-21T00:00:00-05:00 | A Semantic Decoupling-Based Two-Stage Rainy-Day Attack for Revealing Weather Robustness Deficiencies in Vision-Language Models | arXiv:2601.13238v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are trained on image-text pairs collected under canonical visual conditions and achieve strong performance on multimodal tasks. However, their robustness to real-world weather conditions, and the stability of cross-modal semantic alignment un... | https://arxiv.org/abs/2601.13238 | Academic Papers | svg |
a9ed3154609d9008e55027997f37829d5dbb019635920bd89ecd5fd48fff7b5b | 2026-01-21T00:00:00-05:00 | KOCO-BENCH: Can Large Language Models Leverage Domain Knowledge in Software Development? | arXiv:2601.13240v1 Announce Type: new Abstract: Large language models (LLMs) excel at general programming but struggle with domain-specific software development, necessitating domain specialization methods for LLMs to learn and utilize domain knowledge and data. However, existing domain-specific code benchmarks cannot ... | https://arxiv.org/abs/2601.13240 | Academic Papers | svg |
cefbad2fa1bba429341591dc36b5c35fd9a16bbbd2e6214a20cc0419bebe32f0 | 2026-01-21T00:00:00-05:00 | A Comprehensive Evaluation of LLM Reasoning: From Single-Model to Multi-Agent Paradigms | arXiv:2601.13243v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as reasoning systems, where reasoning paradigms - such as Chain-of-Thought (CoT) and multi-agent systems (MAS) - play a critical role, yet their relative effectiveness and cost-accuracy trade-offs remain poorly unders... | https://arxiv.org/abs/2601.13243 | Academic Papers | svg |
c9166adefe3ff5a2a562436186952d185d87c7ad2c0f31a60b5bb0822d6185e3 | 2026-01-21T00:00:00-05:00 | Do Instruction-Tuned Models Always Perform Better Than Base Models? Evidence from Math and Domain-Shifted Benchmarks | arXiv:2601.13244v1 Announce Type: new Abstract: Instruction finetuning is standard practice for improving LLM performance, yet it remains unclear whether it enhances reasoning or merely induces surface-level pattern matching. We investigate this by evaluating base and instruction-tuned models on standard math benchmark... | https://arxiv.org/abs/2601.13244 | Academic Papers | svg |
c2ff29253e985948a88744ab3f5b0b89e667212ad9279f10339e591b863ded35 | 2026-01-21T00:00:00-05:00 | The Cost of Failure: On The Complexity of Recampaigning under Fixed Districts | arXiv:2601.13246v1 Announce Type: new Abstract: Redistricting efforts have gathered contemporary attention in both quotidian and scholarly debates, particularly in the United States where efforts to redraw congressional districts to favor either of the two major parties in 12 states -- such as California, Texas, and Oh... | https://arxiv.org/abs/2601.13246 | Academic Papers | svg |
ef8130640cf71c7d130ed50ee7af9fb96562b54d0d2948ba703a913d12e71284 | 2026-01-21T00:00:00-05:00 | Aligning Agentic World Models via Knowledgeable Experience Learning | arXiv:2601.13247v1 Announce Type: new Abstract: Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural grounding to respect the immutable laws of the physical world. Consequently, while these agents implicitly function as world models, thei... | https://arxiv.org/abs/2601.13247 | Academic Papers | svg |
1e350ecf3ae4129378f335139ebb1651d673c61e6a99346b5424a912502dfbe3 | 2026-01-21T00:00:00-05:00 | Diffusion-based Inverse Model of a Distributed Tactile Sensor for Object Pose Estimation | arXiv:2601.13250v1 Announce Type: new Abstract: Tactile sensing provides a promising sensing modality for object pose estimation in manipulation settings where visual information is limited due to occlusion or environmental effects. However, efficiently leveraging tactile data for estimation remains a challenge due to ... | https://arxiv.org/abs/2601.13250 | Academic Papers | svg |
f08c3f142cbbb560e32ffbc66ba57bd271140d1eaff905c615475e525abbf296 | 2026-01-21T00:00:00-05:00 | Beyond Cosine Similarity: Taming Semantic Drift and Antonym Intrusion in a 15-Million Node Turkish Synonym Graph | arXiv:2601.13251v1 Announce Type: new Abstract: Neural embeddings have a notorious blind spot: they can't reliably tell synonyms apart from antonyms. Consequently, increasing similarity thresholds often fails to prevent opposites from being grouped together. We've built a large-scale semantic clustering system specific... | https://arxiv.org/abs/2601.13251 | Academic Papers | svg |
40f944546a41ea4557f2111d965ac81ea46c73becfae7ae64a3bcf2f8f677d94 | 2026-01-21T00:00:00-05:00 | Autonomous Navigation at the Nano-Scale: Algorithms, Architectures, and Constraints | arXiv:2601.13252v1 Announce Type: new Abstract: Autonomous navigation for nano-scale unmanned aerial vehicles (nano-UAVs) is governed by extreme Size, Weight, and Power (SWaP) constraints (with the weight < 50 g and sub-100 mW onboard processor), distinguishing it fundamentally from standard robotic paradigms. This rev... | https://arxiv.org/abs/2601.13252 | Academic Papers | svg |
331f5f5012a77d16c240ebbafaa05480dfcd78178b4f65e2b78905bcbeef7951 | 2026-01-21T00:00:00-05:00 | A Hybrid Protocol for Large-Scale Semantic Dataset Generation in Low-Resource Languages: The Turkish Semantic Relations Corpus | arXiv:2601.13253v1 Announce Type: new Abstract: We present a hybrid methodology for generating large-scale semantic relationship datasets in low-resource languages, demonstrated through a comprehensive Turkish semantic relations corpus. Our approach integrates three phases: (1) FastText embeddings with Agglomerative Cl... | https://arxiv.org/abs/2601.13253 | Academic Papers | svg |
2527ab1d8d1f4877b768e716577ba49bbc10782b96fd21b538feaf156eac20fc | 2026-01-21T00:00:00-05:00 | Deep Neural networks for solving high-dimensional parabolic partial differential equations | arXiv:2601.13256v1 Announce Type: new Abstract: The numerical solution of high dimensional partial differential equations (PDEs) is severely constrained by the curse of dimensionality (CoD), rendering classical grid--based methods impractical beyond a few dimensions. In recent years, deep neural networks have emerged a... | https://arxiv.org/abs/2601.13256 | Academic Papers | svg |
35ba9444b6889dd47b2d554d8ae981dfe7d5d3ca8b0c2aa4b97a3d75ac37a938 | 2026-01-21T00:00:00-05:00 | Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models | arXiv:2601.13260v1 Announce Type: new Abstract: Tokenization underlies every large language model, yet it remains an under-theorized and inconsistently designed component. Common subword approaches such as Byte Pair Encoding (BPE) offer scalability but often misalign with linguistic structure, amplify bias, and waste c... | https://arxiv.org/abs/2601.13260 | Academic Papers | svg |
52076c048b1f1dfea64777eb550f9640a29034f690f42cd018ab25292534f794 | 2026-01-21T00:00:00-05:00 | CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning | arXiv:2601.13262v1 Announce Type: new Abstract: While large language models (LLMs) have shown to perform well on monolingual mathematical and commonsense reasoning, they remain unreliable for multilingual medical reasoning applications, hindering their deployment in multilingual healthcare settings. We address this by ... | https://arxiv.org/abs/2601.13262 | Academic Papers | svg |
0c3aa054d08193a140fc74479eb0c6cc7aff6f03228bfe59f5cb4a7d2e2e2377 | 2026-01-21T00:00:00-05:00 | Deep Learning for Semantic Segmentation of 3D Ultrasound Data | arXiv:2601.13263v1 Announce Type: new Abstract: Developing cost-efficient and reliable perception systems remains a central challenge for automated vehicles. LiDAR and camera-based systems dominate, yet they present trade-offs in cost, robustness and performance under adverse conditions. This work introduces a novel fr... | https://arxiv.org/abs/2601.13263 | Academic Papers | svg |
96492f174bd4a073a990d94a73a0cda60a5f3ac39f238ef68f2f37f1ec3e6bc0 | 2026-01-21T00:00:00-05:00 | Unlearning in LLMs: Methods, Evaluation, and Open Challenges | arXiv:2601.13264v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable success across natural language processing tasks, yet their widespread deployment raises pressing concerns around privacy, copyright, security, and bias. Machine unlearning has emerged as a promising paradigm for selec... | https://arxiv.org/abs/2601.13264 | Academic Papers | svg |
0ea1ef6320f79601552c9ed230c1094ff17c6bc701895a4acb8740fac67d73be | 2026-01-21T00:00:00-05:00 | The Query Complexity of Local Search in Rounds on General Graphs | arXiv:2601.13266v1 Announce Type: new Abstract: We analyze the query complexity of finding a local minimum in $t$ rounds on general graphs. More precisely, given a graph $G = (V,E)$ and oracle access to an unknown function $f : V \to \mathbb{R}$, the goal is to find a local minimum--a vertex $v$ such that $f(v) \leq f(... | https://arxiv.org/abs/2601.13266 | Academic Papers | svg |
ec7396c220e47d51cc7966ed8959efe1e3c5c946819c0814fed8fc54ba363915 | 2026-01-21T00:00:00-05:00 | Improving the Safety and Trustworthiness of Medical AI via Multi-Agent Evaluation Loops | arXiv:2601.13268v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly applied in healthcare, yet ensuring their ethical integrity and safety compliance remains a major barrier to clinical deployment. This work introduces a multi-agent refinement framework designed to enhance the safety and relia... | https://arxiv.org/abs/2601.13268 | Academic Papers | svg |
de7badcf0fbacd89aa749c6c9d70e33be5b9879b8fb93882defb2127202d5792 | 2026-01-21T00:00:00-05:00 | Probabilistic Linear Logic Programming with an application to Bayesian Networks computations | arXiv:2601.13270v1 Announce Type: new Abstract: Bayesian networks are a canonical formalism for representing probabilistic dependencies, yet their integration within logic programming frameworks remains a nontrivial challenge, mainly due to the complex structure of these networks. In this paper, we propose probLO (prob... | https://arxiv.org/abs/2601.13270 | Academic Papers | svg |
3aede8093ed5342b1c1b3f650c23b07be5f61c443d38250293554d5b5a3ed55c | 2026-01-21T00:00:00-05:00 | Function Recovery Attacks in Gate-Hiding Garbled Circuits using SAT Solving | arXiv:2601.13271v1 Announce Type: new Abstract: Semi-Private Function Evaluation enables joint computation while protecting both input data and function logic. A practical instantiation is gate-hiding garbled circuits, which conceal gate functionalities while revealing the circuit topology. Existing security definition... | https://arxiv.org/abs/2601.13271 | Academic Papers | svg |
2a16f4569cfb2b5fddf4dfda2a183c282479b21bfd1b1d549748f17b2030bb9c | 2026-01-21T00:00:00-05:00 | Multi-level Monte Carlo Dropout for Efficient Uncertainty Quantification | arXiv:2601.13272v1 Announce Type: new Abstract: We develop a multilevel Monte Carlo (MLMC) framework for uncertainty quantification with Monte Carlo dropout. Treating dropout masks as a source of epistemic randomness, we define a fidelity hierarchy by the number of stochastic forward passes used to estimate predictive ... | https://arxiv.org/abs/2601.13272 | Academic Papers | svg |
3cac187cc57dda0895623704a1c8301ec364229b9be9a67325c998fbc0284f1c | 2026-01-21T00:00:00-05:00 | Safe Navigation in Cluttered Environments Via Spline-Based Harmonic Potential Fields | arXiv:2601.13273v1 Announce Type: new Abstract: We provide a complete motion-planning mechanism that ensures target tracking and obstacle avoidance in a cluttered environment. For a given polyhedral decomposition of the feasible space, we adopt a novel procedure that constrains the agent to move only through a prescrib... | https://arxiv.org/abs/2601.13273 | Academic Papers | svg |
45f5334db51f31442cf284b0cac853fda43fbb14ad5e4d0728d261c7443ecd49 | 2026-01-21T00:00:00-05:00 | Balancing Classification and Calibration Performance in Decision-Making LLMs via Calibration Aware Reinforcement Learning | arXiv:2601.13284v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in decision-making tasks, where not only accuracy but also reliable confidence estimates are essential. Well-calibrated confidence enables downstream systems to decide when to trust a model and when to defer to fallba... | https://arxiv.org/abs/2601.13284 | Academic Papers | svg |
17f9b0482e227006bbdbaf7d51139331d3f1502f5dda27f8f18b493ba24c0b43 | 2026-01-21T00:00:00-05:00 | Tight Asymptotic Bounds for Fair Division With Externalities | arXiv:2601.13287v1 Announce Type: new Abstract: We study the problem of allocating a set of indivisible items among agents whose preferences include externalities. Unlike the standard fair division model, agents may derive positive or negative utility not only from items allocated directly to them, but also from items ... | https://arxiv.org/abs/2601.13287 | Academic Papers | svg |
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