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ad5f64a854d41cbb4cd6a9c6d8649f6eda1610b0393f0bc7e42c31193ebb98e1 | 2026-01-16T00:00:00-05:00 | Adaptive Label Error Detection: A Bayesian Approach to Mislabeled Data Detection | arXiv:2601.10084v1 Announce Type: new Abstract: Machine learning classification systems are susceptible to poor performance when trained with incorrect ground truth labels, even when data is well-curated by expert annotators. As machine learning becomes more widespread, it is increasingly imperative to identify and cor... | https://arxiv.org/abs/2601.10084 | Academic Papers | svg |
ffe03630dd58b97bd35cfecaa15ecb3a9fac17482d3e5aa818f73736f5cf9377 | 2026-01-16T00:00:00-05:00 | CALM-IT: Generating Realistic Long-Form Motivational Interviewing Dialogues with Dual-Actor Conversational Dynamics Tracking | arXiv:2601.10085v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used in mental health-related settings, yet they struggle to sustain realistic, goal-directed dialogue over extended interactions. While LLMs generate fluent responses, they optimize locally for the next turn rather than maint... | https://arxiv.org/abs/2601.10085 | Academic Papers | svg |
68e909da75f23749bd80980272ec07fa21ede96d249abc8c7afb4d0f2e0800ed | 2026-01-16T00:00:00-05:00 | State of AI: An Empirical 100 Trillion Token Study with OpenRouter | arXiv:2601.10088v1 Announce Type: new Abstract: The past year has marked a turning point in the evolution and real-world use of large language models (LLMs). With the release of the first widely adopted reasoning model, o1, on December 5th, 2024, the field shifted from single-pass pattern generation to multi-step delib... | https://arxiv.org/abs/2601.10088 | Academic Papers | svg |
d42aa218f67c88ea5817570042be214e741c387d81a7956363fd0425214ee0f5 | 2026-01-16T00:00:00-05:00 | Bayesian Meta-Analyses Could Be More: A Case Study in Trial of Labor After a Cesarean-section Outcomes and Complications | arXiv:2601.10089v1 Announce Type: new Abstract: The meta-analysis's utility is dependent on previous studies having accurately captured the variables of interest, but in medical studies, a key decision variable that impacts a physician's decisions was not captured. This results in an unknown effect size and unreliable ... | https://arxiv.org/abs/2601.10089 | Academic Papers | svg |
e0857c8d2bfb6d650b76b997f4aa7c70f3dea360551809b5fb55f9785971e01f | 2026-01-16T00:00:00-05:00 | Difficulty-guided Sampling: Bridging the Target Gap between Dataset Distillation and Downstream Tasks | arXiv:2601.10090v1 Announce Type: new Abstract: In this paper, we propose difficulty-guided sampling (DGS) to bridge the target gap between the distillation objective and the downstream task, therefore improving the performance of dataset distillation. Deep neural networks achieve remarkable performance but have time a... | https://arxiv.org/abs/2601.10090 | Academic Papers | svg |
25a4ea4fbc779211fe5abac41e471da884f10968c8b000e2bddb3805afbc9b33 | 2026-01-16T00:00:00-05:00 | LeMoF: Level-guided Multimodal Fusion for Heterogeneous Clinical Data | arXiv:2601.10092v1 Announce Type: new Abstract: Multimodal clinical prediction is widely used to integrate heterogeneous data such as Electronic Health Records (EHR) and biosignals. However, existing methods tend to rely on static modality integration schemes and simple fusion strategies. As a result, they fail to full... | https://arxiv.org/abs/2601.10092 | Academic Papers | svg |
2f7dd1255beafc2e21df8279ed61c23087c4f985975daecd9748ae0816658918 | 2026-01-16T00:00:00-05:00 | Mark My Works Autograder for Programming Courses | arXiv:2601.10093v1 Announce Type: new Abstract: Large programming courses struggle to provide timely, detailed feedback on student code. We developed Mark My Works, a local autograding system that combines traditional unit testing with LLM-generated explanations. The system uses role-based prompts to analyze submission... | https://arxiv.org/abs/2601.10093 | Academic Papers | svg |
4b95e35fff8d060ad3b0f4451257a9163e2f16bda99d2b1ce08474b00c4dc0ae | 2026-01-16T00:00:00-05:00 | V-Zero: Self-Improving Multimodal Reasoning with Zero Annotation | arXiv:2601.10094v1 Announce Type: new Abstract: Recent advances in multimodal learning have significantly enhanced the reasoning capabilities of vision-language models (VLMs). However, state-of-the-art approaches rely heavily on large-scale human-annotated datasets, which are costly and time-consuming to acquire. To ov... | https://arxiv.org/abs/2601.10094 | Academic Papers | svg |
2f3e477a08738c16604b4ed587d5035452aaae028f1a746e82e61edc4735de6a | 2026-01-16T00:00:00-05:00 | On the Computation and Approximation of Backward Reachable Sets for Max-Plus Linear Systems using Polyhedras | arXiv:2601.10095v1 Announce Type: new Abstract: This paper investigates reachability analysis for max-plus linear systems (MPLS), an important class of dynamical systems that model synchronization and delay phenomena in timed discrete-event systems. We specifically focus on backward reachability analysis, i.e., determi... | https://arxiv.org/abs/2601.10095 | Academic Papers | svg |
c07949b4652f3a177f8b2731e0372669ba0c84bb493c256d0f4ae1ffaeb500ac | 2026-01-16T00:00:00-05:00 | Multilingual-To-Multimodal (M2M): Unlocking New Languages with Monolingual Text | arXiv:2601.10096v1 Announce Type: new Abstract: Multimodal models excel in English, supported by abundant image-text and audio-text data, but performance drops sharply for other languages due to limited multilingual multimodal resources. Existing solutions rely heavily on machine translation, while advances in multilin... | https://arxiv.org/abs/2601.10096 | Academic Papers | svg |
134d5d19a036c8281325ecb6b07066651149d639d9af93c65db393b153e48a9a | 2026-01-16T00:00:00-05:00 | InfoSculpt: Sculpting the Latent Space for Generalized Category Discovery | arXiv:2601.10098v1 Announce Type: new Abstract: Generalized Category Discovery (GCD) aims to classify instances from both known and novel categories within a large-scale unlabeled dataset, a critical yet challenging task for real-world, open-world applications. However, existing methods often rely on pseudo-labeling, o... | https://arxiv.org/abs/2601.10098 | Academic Papers | svg |
41e8cc8a1e9d21cb14c3a1e8a1339026e5e5952863bddeb5f03ffd68953a3121 | 2026-01-16T00:00:00-05:00 | MATRIX AS PLAN: Structured Logical Reasoning with Feedback-Driven Replanning | arXiv:2601.10101v1 Announce Type: new Abstract: As knowledge and semantics on the web grow increasingly complex, enhancing Large Language Models (LLMs) comprehension and reasoning capabilities has become particularly important. Chain-of-Thought (CoT) prompting has been shown to enhance the reasoning capabilities of LLM... | https://arxiv.org/abs/2601.10101 | Academic Papers | svg |
0cd94553ac16098e8518cfe9296dd97f4e13cfb30a085f53a06aa010fd67f373 | 2026-01-16T00:00:00-05:00 | When Personas Override Payoffs: Role Identity Bias in Multi-Agent LLM Decision-Making | arXiv:2601.10102v1 Announce Type: new Abstract: Large language models are increasingly deployed in multi-agent systems for strategic tasks, yet how design choices such as role-based personas and payoff visibility affect reasoning remains poorly understood. We investigate whether multi-agent systems function as strategi... | https://arxiv.org/abs/2601.10102 | Academic Papers | svg |
3e70bdef2ead1ed728488b8bfe8ad54c64384c9b4a14b69258e93a4894df06cb | 2026-01-16T00:00:00-05:00 | FlowAct-R1: Towards Interactive Humanoid Video Generation | arXiv:2601.10103v1 Announce Type: new Abstract: Interactive humanoid video generation aims to synthesize lifelike visual agents that can engage with humans through continuous and responsive video. Despite recent advances in video synthesis, existing methods often grapple with the trade-off between high-fidelity synthes... | https://arxiv.org/abs/2601.10103 | Academic Papers | svg |
fe4dec5f2eda2ab42ca3211119f28371a8b08644723ec3e4e051a16ef99bb0ea | 2026-01-16T00:00:00-05:00 | MathDoc: Benchmarking Structured Extraction and Active Refusal on Noisy Mathematics Exam Papers | arXiv:2601.10104v1 Announce Type: new Abstract: The automated extraction of structured questions from paper-based mathematics exams is fundamental to intelligent education, yet remains challenging in real-world settings due to severe visual noise. Existing benchmarks mainly focus on clean documents or generic layout an... | https://arxiv.org/abs/2601.10104 | Academic Papers | svg |
f3eee2019ed4d9617fa53e8173578acef4a80e45260d147365b4a4af72ce36a3 | 2026-01-16T00:00:00-05:00 | Fuzzychain-edge: A novel Fuzzy logic-based adaptive Access control model for Blockchain in Edge Computing | arXiv:2601.10105v1 Announce Type: new Abstract: The rapid integration of IoT with edge computing has revolutionized various domains, particularly healthcare, by enabling real-time data sharing, remote monitoring, and decision-making. However, it introduces critical challenges, including data privacy breaches, security ... | https://arxiv.org/abs/2601.10105 | Academic Papers | svg |
41bcc85f30fabe70bb641f11db82914fd0cf952386e38c0c23e9bca3a91f5e23 | 2026-01-16T00:00:00-05:00 | Enhancing Visual In-Context Learning by Multi-Faceted Fusion | arXiv:2601.10107v1 Announce Type: new Abstract: Visual In-Context Learning (VICL) has emerged as a powerful paradigm, enabling models to perform novel visual tasks by learning from in-context examples. The dominant "retrieve-then-prompt" approach typically relies on selecting the single best visual prompt, a practice t... | https://arxiv.org/abs/2601.10107 | Academic Papers | svg |
4f40a06efecf7e9032df5f19e6e43e4b6828650d7095d8f8646b3fc57eb7e6f4 | 2026-01-16T00:00:00-05:00 | SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature | arXiv:2601.10108v1 Announce Type: new Abstract: Evaluating whether multimodal large language models truly understand long-form scientific papers remains challenging: answer-only metrics and synthetic "Needle-In-A-Haystack" tests often reward answer matching without requiring a causal, evidence-linked reasoning trace in... | https://arxiv.org/abs/2601.10108 | Academic Papers | svg |
0e002cd546256c4e5e5ed55dfb016f33c449e1c44827df73788b76e1119755a4 | 2026-01-16T00:00:00-05:00 | Skill-Aware Data Selection and Fine-Tuning for Data-Efficient Reasoning Distillation | arXiv:2601.10109v1 Announce Type: new Abstract: Large reasoning models such as DeepSeek-R1 and their distilled variants achieve strong performance on complex reasoning tasks. Yet, distilling these models often demands large-scale data for supervised fine-tuning (SFT), motivating the pursuit of data-efficient training m... | https://arxiv.org/abs/2601.10109 | Academic Papers | svg |
5ac0cdb4050c4920aac1425fcbd1336842c2fca6e19f8a081fc6a417a5f9a0c5 | 2026-01-16T00:00:00-05:00 | Multi-Constrained Evolutionary Molecular Design Framework: An Interpretable Drug Design Method Combining Rule-Based Evolution and Molecular Crossover | arXiv:2601.10110v1 Announce Type: new Abstract: This study proposes MCEMOL (Multi-Constrained Evolutionary Molecular Design Framework), a molecular optimization approach integrating rule-based evolution with molecular crossover. MCEMOL employs dual-layer evolution: optimizing transformation rules at rule level while ap... | https://arxiv.org/abs/2601.10110 | Academic Papers | svg |
bbcd0b0b325b5984dea8d2674e8ef6bbe759d3218364bdc45823e176833c404f | 2026-01-16T00:00:00-05:00 | Repository Intelligence Graph: Deterministic Architectural Map for LLM Code Assistants | arXiv:2601.10112v1 Announce Type: new Abstract: Repository aware coding agents often struggle to recover build and test structure, especially in multilingual projects where cross language dependencies are encoded across heterogeneous build systems and tooling. We introduce the Repository Intelligence Graph (RIG), a det... | https://arxiv.org/abs/2601.10112 | Academic Papers | svg |
032665e57105488ede7ee90842b5ae191b5431644cd274709a8526af94afb620 | 2026-01-16T00:00:00-05:00 | Following the Teacher's Footsteps: Scheduled Checkpoint Distillation for Domain-Specific LLMs | arXiv:2601.10114v1 Announce Type: new Abstract: Large language models (LLMs) are challenging to deploy for domain-specific tasks due to their massive scale. While distilling a fine-tuned LLM into a smaller student model is a promising alternative, the capacity gap between teacher and student often leads to suboptimal p... | https://arxiv.org/abs/2601.10114 | Academic Papers | svg |
7202f5c9bb18264c3c54e72ce30dd4087f1709543479dc5f64c52ed19d92b0f3 | 2026-01-16T00:00:00-05:00 | CoCoPlan: Adaptive Coordination and Communication for Multi-robot Systems in Dynamic and Unknown Environments | arXiv:2601.10116v1 Announce Type: new Abstract: Multi-robot systems can greatly enhance efficiency through coordination and collaboration, yet in practice, full-time communication is rarely available and interactions are constrained to close-range exchanges. Existing methods either maintain all-time connectivity, rely ... | https://arxiv.org/abs/2601.10116 | Academic Papers | svg |
1317ee3d7a3eafb9f2e6a89604a86b6fa999368e4320de7b8ee69e2364018c77 | 2026-01-16T00:00:00-05:00 | Beyond Single Prompts: Synergistic Fusion and Arrangement for VICL | arXiv:2601.10117v1 Announce Type: new Abstract: Vision In-Context Learning (VICL) enables inpainting models to quickly adapt to new visual tasks from only a few prompts. However, existing methods suffer from two key issues: (1) selecting only the most similar prompt discards complementary cues from other high-quality p... | https://arxiv.org/abs/2601.10117 | Academic Papers | svg |
89bb815cbe1cdd2b1b8857599858ea3525bb80a707a43b02e03260b8a2787007 | 2026-01-16T00:00:00-05:00 | Advanced Encryption Technique for Multimedia Data Using Sudoku-Based Algorithms for Enhanced Security | arXiv:2601.10119v1 Announce Type: new Abstract: Encryption and Decryption is the process of sending a message in a ciphered way that appears meaningless and could be deciphered using a key for security purposes to avoid data breaches. This paper expands on the previous work on Sudoku-based encryption methods, applying ... | https://arxiv.org/abs/2601.10119 | Academic Papers | svg |
a22035a24088f770bbe2ff873939bbf1ed4ac96995a55b656cb212e9b07f2da6 | 2026-01-16T00:00:00-05:00 | TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems | arXiv:2601.10120v1 Announce Type: new Abstract: Optimizing communication topology in LLM-based multi-agent system is critical for enabling collective intelligence. Existing methods mainly rely on spatio-temporal interaction paradigms, where the sequential execution of multi-round dialogues incurs high latency and compu... | https://arxiv.org/abs/2601.10120 | Academic Papers | svg |
a9e37ef795154a8011e8bbf864537c2fdd00704c586c38a60545366e8d00c6f8 | 2026-01-16T00:00:00-05:00 | Role-Playing Agents Driven by Large Language Models: Current Status, Challenges, and Future Trends | arXiv:2601.10122v1 Announce Type: new Abstract: In recent years, with the rapid advancement of large language models (LLMs), role-playing language agents (RPLAs) have emerged as a prominent research focus at the intersection of natural language processing (NLP) and human-computer interaction. This paper systematically ... | https://arxiv.org/abs/2601.10122 | Academic Papers | svg |
70822210bf921e84f72b6746fc205dbbdd4e921c1f822f40ee31cfbf1ffbf4d7 | 2026-01-16T00:00:00-05:00 | Fairness Driven Multi-Agent Path Finding Problem | arXiv:2601.10123v1 Announce Type: new Abstract: The Multi-Agent Path Finding (MAPF) problem aims at finding non-conflicting paths for multiple agents from their respective sources to destinations. This problem arises in multiple real-life situations, including robot motion planning and airspace assignment for unmanned ... | https://arxiv.org/abs/2601.10123 | Academic Papers | svg |
18b4db988bb3bc52367ff07c9aac8359dbeb346dafdd183f7279a4a5d88595f8 | 2026-01-16T00:00:00-05:00 | VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation | arXiv:2601.10124v1 Announce Type: new Abstract: Consistency learning with feature perturbation is a widely used strategy in semi-supervised medical image segmentation. However, many existing perturbation methods rely on dropout, and thus require a careful manual tuning of the dropout rate, which is a sensitive hyperpar... | https://arxiv.org/abs/2601.10124 | Academic Papers | svg |
9c1820956894d46954ea762ccde398d318036a76246a0dca9894ea553d3ed2a4 | 2026-01-16T00:00:00-05:00 | A Generalizable Framework for Building Executable Domain-Specific LLMs under Data Scarcity: Demonstration on Semiconductor TCAD Simulation | arXiv:2601.10128v1 Announce Type: new Abstract: Scientific and engineering verticals often suffer from data scarcity and strict executability requirements: models must generate not only fluent text, but also syntactically valid, tool-compilable scripts. We present a schema-first alignment framework for building compact... | https://arxiv.org/abs/2601.10128 | Academic Papers | svg |
23f63eacc0dcbef478190891270a0841c877ce7f7c4a8eda92ff00b06b172e2c | 2026-01-16T00:00:00-05:00 | LaViT: Aligning Latent Visual Thoughts for Multi-modal Reasoning | arXiv:2601.10129v1 Announce Type: new Abstract: Current multimodal latent reasoning often relies on external supervision (e.g., auxiliary images), ignoring intrinsic visual attention dynamics. In this work, we identify a critical Perception Gap in distillation: student models frequently mimic a teacher's textual output... | https://arxiv.org/abs/2601.10129 | Academic Papers | svg |
be9f47c065fd63ed4032e606ea9e17bdeec137938e6477c99dcabcd7a02a25d2 | 2026-01-16T00:00:00-05:00 | Redundancy-Driven Top-$k$ Functional Dependency Discovery | arXiv:2601.10130v1 Announce Type: new Abstract: Functional dependencies (FDs) are basic constraints in relational databases and are used for many data management tasks. Most FD discovery algorithms find all valid dependencies, but this causes two problems. First, the computational cost is prohibitive: computational com... | https://arxiv.org/abs/2601.10130 | Academic Papers | svg |
19c200132a3a168d23c76b2f1a1851a5fce437263a2a2950ee016fc69815a2d8 | 2026-01-16T00:00:00-05:00 | M^4olGen: Multi-Agent, Multi-Stage Molecular Generation under Precise Multi-Property Constraints | arXiv:2601.10131v1 Announce Type: new Abstract: Generating molecules that satisfy precise numeric constraints over multiple physicochemical properties is critical and challenging. Although large language models (LLMs) are expressive, they struggle with precise multi-objective control and numeric reasoning without exter... | https://arxiv.org/abs/2601.10131 | Academic Papers | svg |
90974f7e9acf64017e44d53c365b60f1a5019c0d5dc0093d4bd3ce186b7c5361 | 2026-01-16T00:00:00-05:00 | Is More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction | arXiv:2601.10132v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning and prediction across different domains. Yet, their ability to infer temporal regularities from structured behavioral data remains underexplored. This paper presents a systematic study inv... | https://arxiv.org/abs/2601.10132 | Academic Papers | svg |
35f712c8d5d0d0477330715560849d1ce44fb88a62e0e8786952f86bef39b288 | 2026-01-16T00:00:00-05:00 | Function Correcting Codes for Maximally-Unbalanced Boolean Functions | arXiv:2601.10135v1 Announce Type: new Abstract: Function-Correcting Codes (FCCs) enable reliable computation of a function of a $k$-bit message over noisy channels without requiring full message recovery. In this work, we study optimal single-error correcting FCCs (SEFCCs) for maximally-unbalanced Boolean functions, wh... | https://arxiv.org/abs/2601.10135 | Academic Papers | svg |
3f037ff1c2c1ef9f5d9c23103d0aa1bf0f1e1c79bcf946035facdd45cd723e47 | 2026-01-16T00:00:00-05:00 | Step-by-Step Causality: Transparent Causal Discovery with Multi-Agent Tree-Query and Adversarial Confidence Estimation | arXiv:2601.10137v1 Announce Type: new Abstract: Causal discovery aims to recover ``what causes what'', but classical constraint-based methods (e.g., PC, FCI) suffer from error propagation, and recent LLM-based causal oracles often behave as opaque, confidence-free black boxes. This paper introduces Tree-Query, a tree-s... | https://arxiv.org/abs/2601.10137 | Academic Papers | svg |
9612dc7aedba9c94a50830c03979f5dba25e80121c3031defe5ed4d7543aa73e | 2026-01-16T00:00:00-05:00 | Understanding and Preserving Safety in Fine-Tuned LLMs | arXiv:2601.10141v1 Announce Type: new Abstract: Fine-tuning is an essential and pervasive functionality for applying large language models (LLMs) to downstream tasks. However, it has the potential to substantially degrade safety alignment, e.g., by greatly increasing susceptibility to jailbreak attacks, even when the f... | https://arxiv.org/abs/2601.10141 | Academic Papers | svg |
9f4cb787507128ba454965973594ded8cfc71235ad3feda5b059e88416203b4b | 2026-01-16T00:00:00-05:00 | Actors, Frames and Arguments: A Multi-Decade Computational Analysis of Climate Discourse in Financial News using Large Language Models | arXiv:2601.10142v1 Announce Type: new Abstract: Financial news media shapes trillion-dollar climate investment decisions, yet discourse in this elite domain remains underexplored. We analyze two decades of climate-related articles (2000-2023) from Dow Jones Newswire using an Actor-Frame-Argument (AFA) pipeline that ext... | https://arxiv.org/abs/2601.10142 | Academic Papers | svg |
3b0e432adb6af826dcda891ae4b7cacaee43f89760b5e34ee71de3e017e3d7c8 | 2026-01-16T00:00:00-05:00 | History Is Not Enough: An Adaptive Dataflow System for Financial Time-Series Synthesis | arXiv:2601.10143v1 Announce Type: new Abstract: In quantitative finance, the gap between training and real-world performance-driven by concept drift and distributional non-stationarity-remains a critical obstacle for building reliable data-driven systems. Models trained on static historical data often overfit, resultin... | https://arxiv.org/abs/2601.10143 | Academic Papers | svg |
4ec569e3da4cb86951fc1bd89fecdaae5816d81a51745241cae580c547ed8ec5 | 2026-01-16T00:00:00-05:00 | DecisionLLM: Large Language Models for Long Sequence Decision Exploration | arXiv:2601.10148v1 Announce Type: new Abstract: Long-sequence decision-making, which is usually addressed through reinforcement learning (RL), is a critical component for optimizing strategic operations in dynamic environments, such as real-time bidding in computational advertising. The Decision Transformer (DT) introd... | https://arxiv.org/abs/2601.10148 | Academic Papers | svg |
d3eca6eaded94f15cbfb5373d07ba21ea5c805a007247d3057510970cc44bb50 | 2026-01-16T00:00:00-05:00 | New Second-order Convergent Schemes for Solving decoupled FBSDEs | arXiv:2601.10149v1 Announce Type: new Abstract: This paper proposes a new second-order symmetric algorithm for solving decoupled forward-backward stochastic differential equations. Inspired by the alternating direction implicit splitting method for partial differential equations, we split the generator into the sum of ... | https://arxiv.org/abs/2601.10149 | Academic Papers | svg |
82cff031b0b713fb701f6e3933c92a39e4ad25dd72f9490a5fe93617acfe05f4 | 2026-01-16T00:00:00-05:00 | Simple Network Graph Comparative Learning | arXiv:2601.10150v1 Announce Type: new Abstract: The effectiveness of contrastive learning methods has been widely recognized in the field of graph learning, especially in contexts where graph data often lack labels or are difficult to label. However, the application of these methods to node classification tasks still f... | https://arxiv.org/abs/2601.10150 | Academic Papers | svg |
5e0e6698e95df2e14452e42648e082c1bfba105250cec8aa48b154eb9bbbd760 | 2026-01-16T00:00:00-05:00 | Leveraging Digital Twin Technologies: All-Photonics Networks-as-a-Service for Data Center Xchange in the Era of AI [Invited Tutorial] | arXiv:2601.10153v1 Announce Type: new Abstract: This paper presents a data center exchange (Data Center Xchange, DCX) architecture for all-photonics networks-as-a-service in distributed data center infrastructures, enabling the creation of a virtual large-scale data center by directly interconnecting distributed data c... | https://arxiv.org/abs/2601.10153 | Academic Papers | svg |
258496c525dd61a956f5dc2fef576f728c3544b09119bbdf5236de871fd8829a | 2026-01-16T00:00:00-05:00 | MHub.ai: A Simple, Standardized, and Reproducible Platform for AI Models in Medical Imaging | arXiv:2601.10154v1 Announce Type: new Abstract: Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research and clinical use are limited by the wide variety of AI implementations and architectures, inconsistent documentat... | https://arxiv.org/abs/2601.10154 | Academic Papers | svg |
7ed2ad73e81b7968bfa97685d307c28803c5c3610d8fc344ab49cb03e0ed3184 | 2026-01-16T00:00:00-05:00 | LOOKAT: Lookup-Optimized Key-Attention for Memory-Efficient Transformers | arXiv:2601.10155v1 Announce Type: new Abstract: Compressing the KV cache is a required step to deploy large language models on edge devices. Current quantization methods compress storage but fail to reduce bandwidth as attention calculation requires dequantizing keys from INT4/INT8 to FP16 before use. We observe that a... | https://arxiv.org/abs/2601.10155 | Academic Papers | svg |
611efff2255f9746f6670d957dd612205f9bd6358ecec5990b46ef27b89130e2 | 2026-01-16T00:00:00-05:00 | ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback | arXiv:2601.10156v1 Announce Type: new Abstract: While LLM-based agents can interact with environments via invoking external tools, their expanded capabilities also amplify security risks. Monitoring step-level tool invocation behaviors in real time and proactively intervening before unsafe execution is critical for age... | https://arxiv.org/abs/2601.10156 | Academic Papers | svg |
972206f47e05d8b769ecf9e926177e0db1fddfc536d0d3a5a4a7ca81b0dd2202 | 2026-01-16T00:00:00-05:00 | MMPG: MoE-based Adaptive Multi-Perspective Graph Fusion for Protein Representation Learning | arXiv:2601.10157v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have been widely adopted for Protein Representation Learning (PRL), as residue interaction networks can be naturally represented as graphs. Current GNN-based PRL methods typically rely on single-perspective graph construction strategies, which... | https://arxiv.org/abs/2601.10157 | Academic Papers | svg |
1c227ff8a702fdf07698ec2bc5d1053c408a932660205c012c2355dbffd2ef37 | 2026-01-16T00:00:00-05:00 | What Gets Activated: Uncovering Domain and Driver Experts in MoE Language Models | arXiv:2601.10159v1 Announce Type: new Abstract: Most interpretability work focuses on layer- or neuron-level mechanisms in Transformers, leaving expert-level behavior in MoE LLMs underexplored. Motivated by functional specialization in the human brain, we analyze expert activation by distinguishing domain and driver ex... | https://arxiv.org/abs/2601.10159 | Academic Papers | svg |
9d30963444f51976e7caf2afa3193c7cd8836b9436aa2fc33e012a4d9687c2e3 | 2026-01-16T00:00:00-05:00 | Alignment Pretraining: AI Discourse Causes Self-Fulfilling (Mis)alignment | arXiv:2601.10160v1 Announce Type: new Abstract: Pretraining corpora contain extensive discourse about AI systems, yet the causal influence of this discourse on downstream alignment remains poorly understood. If prevailing descriptions of AI behaviour are predominantly negative, LLMs may internalise corresponding behavi... | https://arxiv.org/abs/2601.10160 | Academic Papers | svg |
26b93e85f3b25bf9ff02b04befd77748dee20a5e8058d348a8c2d5df7572669b | 2026-01-16T00:00:00-05:00 | AWED-FiNER: Agents, Web applications, and Expert Detectors for Fine-grained Named Entity Recognition across 36 Languages for 6.6 Billion Speakers | arXiv:2601.10161v1 Announce Type: new Abstract: We introduce AWED-FiNER, an open-source ecosystem designed to bridge the gap in Fine-grained Named Entity Recognition (FgNER) for 36 global languages spoken by more than 6.6 billion people. While Large Language Models (LLMs) dominate general Natural Language Processing (N... | https://arxiv.org/abs/2601.10161 | Academic Papers | svg |
6597ea6c726a47e9f43eed8ebb37a933b5f70ad8220443c0299e0fc1f05b59e0 | 2026-01-16T00:00:00-05:00 | Towards Online Malware Detection using Process Resource Utilization Metrics | arXiv:2601.10164v1 Announce Type: new Abstract: The rapid growth of Cloud Computing and Internet of Things (IoT) has significantly increased the interconnection of computational resources, creating an environment where malicious software (malware) can spread rapidly. To address this challenge, researchers are increasin... | https://arxiv.org/abs/2601.10164 | Academic Papers | svg |
4c921bc4f9bf046a749bdd0b7a92e99f71a0f373cceab60d1302238a415ac0da | 2026-01-16T00:00:00-05:00 | Advancing Adaptive Multi-Stage Video Anomaly Reasoning: A Benchmark Dataset and Method | arXiv:2601.10165v1 Announce Type: new Abstract: Recent progress in reasoning capabilities of Multimodal Large Language Models(MLLMs) has highlighted their potential for performing complex video understanding tasks. However, in the domain of Video Anomaly Detection and Understanding (VAD&U), existing MLLM-based meth... | https://arxiv.org/abs/2601.10165 | Academic Papers | svg |
69fcfe2c8fbea73984f09955628a6c01516a01ae2a9e0d43ce10ce125026d15f | 2026-01-16T00:00:00-05:00 | Credit C-GPT: A Domain-Specialized Large Language Model for Conversational Understanding in Vietnamese Debt Collection | arXiv:2601.10167v1 Announce Type: new Abstract: Debt collection is a critical function within the banking, financial services, and insurance (BFSI) sector, relying heavily on large-scale human-to-human conversational interactions conducted primarily in Vietnamese contact centers. These conversations involve informal sp... | https://arxiv.org/abs/2601.10167 | Academic Papers | svg |
f3aee305bf6004c339c6887beea15e8bf1718442cecd2d3d6f85e5cb140e1d52 | 2026-01-16T00:00:00-05:00 | RAG-3DSG: Enhancing 3D Scene Graphs with Re-Shot Guided Retrieval-Augmented Generation | arXiv:2601.10168v1 Announce Type: new Abstract: Open-vocabulary 3D Scene Graph (3DSG) generation can enhance various downstream tasks in robotics, such as manipulation and navigation, by leveraging structured semantic representations. A 3DSG is constructed from multiple images of a scene, where objects are represented ... | https://arxiv.org/abs/2601.10168 | Academic Papers | svg |
3877698d1d2d2497ba6d6c1e8cb896b58340e7c6df1d4dca9fa142c702f20141 | 2026-01-16T00:00:00-05:00 | CtD: Composition through Decomposition in Emergent Communication | arXiv:2601.10169v1 Announce Type: new Abstract: Compositionality is a cognitive mechanism that allows humans to systematically combine known concepts in novel ways. This study demonstrates how artificial neural agents acquire and utilize compositional generalization to describe previously unseen images. Our method, ter... | https://arxiv.org/abs/2601.10169 | Academic Papers | svg |
8f0c1163ae3ddf9c9c6e74840aeb6fb661fdb4dcd44b210491dd9cf9010494aa | 2026-01-16T00:00:00-05:00 | On Existence of Girth-8 QC-LDPC Code with Large Column Weight: Combining Mirror-sequence with Classification Modulo Ten | arXiv:2601.10170v1 Announce Type: new Abstract: Quasi-cyclic (QC) LDPC codes with large girths play a crucial role in several research and application fields, including channel coding, compressed sensing and distributed storage systems. A major challenge in respect of the code construction is how to obtain such codes w... | https://arxiv.org/abs/2601.10170 | Academic Papers | svg |
93ccaea4e64f9122ed6ddb67fd2c7723e012d7aca8db1ec71934d6286db6569a | 2026-01-16T00:00:00-05:00 | ReasAlign: Reasoning Enhanced Safety Alignment against Prompt Injection Attack | arXiv:2601.10173v1 Announce Type: new Abstract: Large Language Models (LLMs) have enabled the development of powerful agentic systems capable of automating complex workflows across various fields. However, these systems are highly vulnerable to indirect prompt injection attacks, where malicious instructions embedded in... | https://arxiv.org/abs/2601.10173 | Academic Papers | svg |
c83dfb3d519d65b7d5e046cc40384c380c82335e457e9d925640d33cc6c8f8ae | 2026-01-16T00:00:00-05:00 | A Low-Complexity Architecture for Multi-access Coded Caching Systems with Arbitrary User-cache Access Topology | arXiv:2601.10175v1 Announce Type: new Abstract: This paper studies the multi-access coded caching (MACC) problem under arbitrary user-cache access topologies, extending existing models that rely on highly structured and combinatorially designed connectivity. We consider a MACC system consisting of a single server, mult... | https://arxiv.org/abs/2601.10175 | Academic Papers | svg |
9f26ad42df47339083aaf4e1850bae506c02d54a7b3ce4f058bf2fe93bb27a8a | 2026-01-16T00:00:00-05:00 | CC-OR-Net: A Unified Framework for LTV Prediction through Structural Decoupling | arXiv:2601.10176v1 Announce Type: new Abstract: Customer Lifetime Value (LTV) prediction, a central problem in modern marketing, is characterized by a unique zero-inflated and long-tail data distribution. This distribution presents two fundamental challenges: (1) the vast majority of low-to-medium value users numerical... | https://arxiv.org/abs/2601.10176 | Academic Papers | svg |
aa0120265fc678d5051e3c0cfc0eb554c97554986fc048c106028adb096daf59 | 2026-01-16T00:00:00-05:00 | Distributed Linearly Separable Computation with Arbitrary Heterogeneous Data Assignment | arXiv:2601.10177v1 Announce Type: new Abstract: Distributed linearly separable computation is a fundamental problem in large-scale distributed systems, requiring the computation of linearly separable functions over different datasets across distributed workers. This paper studies a heterogeneous distributed linearly se... | https://arxiv.org/abs/2601.10177 | Academic Papers | svg |
12f4e9d8ecd9ce9ae127101f7dce53de76bc3181c489b4afeea8d1ee3ab4bd24 | 2026-01-16T00:00:00-05:00 | HyMGP: A Customized MILP-Based Tool for Techno-Economic Planning of Islanded Microgrids | arXiv:2601.10178v1 Announce Type: new Abstract: This paper presents a customized microgrid planning algorithm and tool, HyMGP, for remote sites in arid regions, which is formulated as a Mixed Integer Linear Programming (MILP) problem. HyMGP is compared with HOMER Pro to evaluate its performance in optimizing the sizing... | https://arxiv.org/abs/2601.10178 | Academic Papers | svg |
706c631310fb907c5d4adbdddf7edd3e0690e8b046bf97dd327113bc02f763da | 2026-01-16T00:00:00-05:00 | Bias in the Shadows: Explore Shortcuts in Encrypted Network Traffic Classification | arXiv:2601.10180v1 Announce Type: new Abstract: Pre-trained models operating directly on raw bytes have achieved promising performance in encrypted network traffic classification (NTC), but often suffer from shortcut learning-relying on spurious correlations that fail to generalize to real-world data. Existing solution... | https://arxiv.org/abs/2601.10180 | Academic Papers | svg |
36c03c0d754196f8112d23420c0bd7c27300aabba77a925297c05d276cc6454c | 2026-01-16T00:00:00-05:00 | Reinforcement Learning to Discover a NorthEast Monsoon Index for Monthly Rainfall Prediction in Thailand | arXiv:2601.10181v1 Announce Type: new Abstract: Climate prediction is a challenge due to the intricate spatiotemporal patterns within Earth systems. Global climate indices, such as the El Ni\~no Southern Oscillation, are standard input features for long-term rainfall prediction. However, a significant gap persists rega... | https://arxiv.org/abs/2601.10181 | Academic Papers | svg |
bacc6e5d542ec7837809d77b04d2639ba7c61bb44675bd5d45a9675aadda7b79 | 2026-01-16T00:00:00-05:00 | HOMURA: Taming the Sand-Glass for Time-Constrained LLM Translation via Reinforcement Learning | arXiv:2601.10187v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable strides in multilingual translation but are hindered by a systemic cross-lingual verbosity bias, rendering them unsuitable for strict time-constrained tasks like subtitling and dubbing. Current prompt-engineering appro... | https://arxiv.org/abs/2601.10187 | Academic Papers | svg |
71114351c6ab3bc7dcc69893efcbddcc04dff3c2f95636e1884d58ca541070ab | 2026-01-16T00:00:00-05:00 | Model Predictive Control of Thermo-Hydraulic Systems Using Primal Decomposition | arXiv:2601.10189v1 Announce Type: new Abstract: Decarbonizing the global energy supply requires more efficient heating and cooling systems. Model predictive control enhances the operation of cooling and heating systems but depends on accurate system models, often based on control volumes. We present an automated framew... | https://arxiv.org/abs/2601.10189 | Academic Papers | svg |
2b64767a82f2fd0798c15f83ef97bbae17c6e70f08bcb313ff781046720138b9 | 2026-01-16T00:00:00-05:00 | How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series | arXiv:2601.10191v1 Announce Type: new Abstract: Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases (NMDs), yet the signals' high and heterogeneous sampling rates pose substantial computational challenges for feature-based machine-learnin... | https://arxiv.org/abs/2601.10191 | Academic Papers | svg |
2f9ea871f32d17ad4ed6324c9ad1db27fb5d8b5e8d0ce555fd96a889b5c45f9b | 2026-01-16T00:00:00-05:00 | From Physical Degradation Models to Task-Aware All-in-One Image Restoration | arXiv:2601.10192v1 Announce Type: new Abstract: All-in-one image restoration aims to adaptively handle multiple restoration tasks with a single trained model. Although existing methods achieve promising results by introducing prompt information or leveraging large models, the added learning modules increase system comp... | https://arxiv.org/abs/2601.10192 | Academic Papers | svg |
cdf31d8992a25b0ec09cb1ea0c503c008f527d2031b9573eee06f13cc18af9de | 2026-01-16T00:00:00-05:00 | GFM4GA: Graph Foundation Model for Group Anomaly Detection | arXiv:2601.10193v1 Announce Type: new Abstract: Group anomaly detection is crucial in many network applications, but faces challenges due to diverse anomaly patterns. Motivated by the success of large language models (LLMs) in natural language processing, graph foundation models (GFMs) is proposed to handle few-shot le... | https://arxiv.org/abs/2601.10193 | Academic Papers | svg |
65b752a0fdf018a1f882699c523c2edefd9443419c887ed2fe4145573c817e28 | 2026-01-16T00:00:00-05:00 | HUMANLLM: Benchmarking and Reinforcing LLM Anthropomorphism via Human Cognitive Patterns | arXiv:2601.10198v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona simulation and Role-Playing Language Agents (RPLAs). However, achieving authentic alignment with human cognitive and behavior... | https://arxiv.org/abs/2601.10198 | Academic Papers | svg |
25ab0583fb1fbe548523b2f1109b370364da41371a4f1d13544453cbf83ad144 | 2026-01-16T00:00:00-05:00 | Graph Regularized PCA | arXiv:2601.10199v1 Announce Type: new Abstract: High-dimensional data often exhibit dependencies among variables that violate the isotropic-noise assumption under which principal component analysis (PCA) is optimal. For cases where the noise is not independent and identically distributed across features (i.e., the cova... | https://arxiv.org/abs/2601.10199 | Academic Papers | svg |
a01d8b3a42f33d7e3f2388cd9c65bd66c02738c12167b4f874c65f49f79d8dda | 2026-01-16T00:00:00-05:00 | ELITE: Efficient Gaussian Head Avatar from a Monocular Video via Learned Initialization and TEst-time Generative Adaptation | arXiv:2601.10200v1 Announce Type: new Abstract: We introduce ELITE, an Efficient Gaussian head avatar synthesis from a monocular video via Learned Initialization and TEst-time generative adaptation. Prior works rely either on a 3D data prior or a 2D generative prior to compensate for missing visual cues in monocular vi... | https://arxiv.org/abs/2601.10200 | Academic Papers | svg |
30fcf3e710db5099d08e0908a4bf7f9611a74c3c77d4607866068425c76b1c16 | 2026-01-16T00:00:00-05:00 | PRL: Process Reward Learning Improves LLMs' Reasoning Ability and Broadens the Reasoning Boundary | arXiv:2601.10201v1 Announce Type: new Abstract: Improving the reasoning abilities of Large Language Models (LLMs) has been a continuous topic recently. But most relevant works are based on outcome rewards at the trajectory level, missing fine-grained supervision during the reasoning process. Other existing training fra... | https://arxiv.org/abs/2601.10201 | Academic Papers | svg |
d3a65a0f72703876680eb44e01e892fa14c12e66c5ca02be0ebb673c67118142 | 2026-01-16T00:00:00-05:00 | One Instruction Does Not Fit All: How Well Do Embeddings Align Personas and Instructions in Low-Resource Indian Languages? | arXiv:2601.10205v1 Announce Type: new Abstract: Aligning multilingual assistants with culturally grounded user preferences is essential for serving India's linguistically diverse population of over one billion speakers across multiple scripts. However, existing benchmarks either focus on a single language or conflate r... | https://arxiv.org/abs/2601.10205 | Academic Papers | svg |
30a1b24a2fafa9bec4fefbd73eb4d952ac78d1d9dcd96aebde39be9e9a30a927 | 2026-01-16T00:00:00-05:00 | Terrain-Adaptive Mobile 3D Printing with Hierarchical Control | arXiv:2601.10208v1 Announce Type: new Abstract: Mobile 3D printing on unstructured terrain remains challenging due to the conflict between platform mobility and deposition precision. Existing gantry-based systems achieve high accuracy but lack mobility, while mobile platforms struggle to maintain print quality on uneve... | https://arxiv.org/abs/2601.10208 | Academic Papers | svg |
27150025ecbacfeb9296d881c1f785590032010b359c8c2bb4e557da4abb1b90 | 2026-01-16T00:00:00-05:00 | PADER: Paillier-based Secure Decentralized Social Recommendation | arXiv:2601.10212v1 Announce Type: new Abstract: The prevalence of recommendation systems also brings privacy concerns to both the users and the sellers, as centralized platforms collect as much data as possible from them. To keep the data private, we propose PADER: a Paillier-based secure decentralized social recommend... | https://arxiv.org/abs/2601.10212 | Academic Papers | svg |
c61bdd611beb8a7018db5823f0b26ac81a45d9a438fc103a286abd30c2be298d | 2026-01-16T00:00:00-05:00 | Beyond Inpainting: Unleash 3D Understanding for Precise Camera-Controlled Video Generation | arXiv:2601.10214v1 Announce Type: new Abstract: Camera control has been extensively studied in conditioned video generation; however, performing precisely altering the camera trajectories while faithfully preserving the video content remains a challenging task. The mainstream approach to achieving precise camera contro... | https://arxiv.org/abs/2601.10214 | Academic Papers | svg |
3508241422fd15cc068ec3f67f7ace9273963bbb99cd1c0fb57e828421299f63 | 2026-01-16T00:00:00-05:00 | Topo-RAG: Topology-aware retrieval for hybrid text-table documents | arXiv:2601.10215v1 Announce Type: new Abstract: In enterprise datasets, documents are rarely pure. They are not just text, nor just numbers; they are a complex amalgam of narrative and structure. Current Retrieval-Augmented Generation (RAG) systems have attempted to address this complexity with a blunt tool: linearizat... | https://arxiv.org/abs/2601.10215 | Academic Papers | svg |
81e02d2bdf1d29e03256a8333b13cb751a7db81f940b103042b409f09f4a37bf | 2026-01-16T00:00:00-05:00 | Agentic Pipelines in Embedded Software Engineering: Emerging Practices and Challenges | arXiv:2601.10220v1 Announce Type: new Abstract: A new transformation is underway in software engineering, driven by the rapid adoption of generative AI in development workflows. Similar to how version control systems once automated manual coordination, AI tools are now beginning to automate many aspects of programming.... | https://arxiv.org/abs/2601.10220 | Academic Papers | svg |
ab353a9a0a14fcef041ec46e657911ead8125bcb3d7eabbe79e17b9fafb073a7 | 2026-01-16T00:00:00-05:00 | Introduction to optimization methods for training SciML models | arXiv:2601.10222v1 Announce Type: new Abstract: Optimization is central to both modern machine learning (ML) and scientific machine learning (SciML), yet the structure of the underlying optimization problems differs substantially across these domains. Classical ML typically relies on stochastic, sample-separable object... | https://arxiv.org/abs/2601.10222 | Academic Papers | svg |
9eebd0a87abc29534c4fc49b77df6523c98d5da3e40e71472c2ab7649fa164fc | 2026-01-16T00:00:00-05:00 | STEAMROLLER: A Multi-Agent System for Inclusive Automatic Speech Recognition for People who Stutter | arXiv:2601.10223v1 Announce Type: new Abstract: People who stutter (PWS) face systemic exclusion in today's voice-driven society, where access to voice assistants, authentication systems, and remote work tools increasingly depends on fluent speech. Current automatic speech recognition (ASR) systems, trained predominant... | https://arxiv.org/abs/2601.10223 | Academic Papers | svg |
4ac5655bbb37f3d3535711fb92ec243c0dc881973659aacbe84926f799b2efbd | 2026-01-16T00:00:00-05:00 | A Unified Framework for Kinematic Simulation of Rigid Foldable Structures | arXiv:2601.10225v1 Announce Type: new Abstract: Origami-inspired structures with rigid panels now span thick, kirigami, and multi-sheet realizations, making unified kinematic analysis essential. Yet a general method that consolidates their loop constraints has been lacking. We present an automated approach that generat... | https://arxiv.org/abs/2601.10225 | Academic Papers | svg |
5f1f7b13b581a625929ca7825544b2d69326c68eb1901bd0a581116966b19438 | 2026-01-16T00:00:00-05:00 | Optimizing Multimodal LLMs for Egocentric Video Understanding: A Solution for the HD-EPIC VQA Challenge | arXiv:2601.10228v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) struggle with complex video QA benchmarks like HD-EPIC VQA due to ambiguous queries/options, poor long-range temporal reasoning, and non-standardized outputs. We propose a framework integrating query/choice pre-processing, domain-s... | https://arxiv.org/abs/2601.10228 | Academic Papers | svg |
e1823d99b6e2dd2379e6cdf96c1e28f0061f15317dc6cb0778e834aa5f1a835f | 2026-01-16T00:00:00-05:00 | GeoSteer: Faithful Chain-of-Thought Steering via Latent Manifold Gradients | arXiv:2601.10229v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have improved multi-step reasoning. Most approaches rely on Chain-of-Thought (CoT) rationales. Previous studies have shown that LLMs often generate logically inconsistent reasoning steps even when their final answers are cor... | https://arxiv.org/abs/2601.10229 | Academic Papers | svg |
48c3bc0fa1ede611dd347e6e06d851d2c2ecdd3ac18353d3f44584591011fde9 | 2026-01-16T00:00:00-05:00 | Tables or Sankey Diagrams? Investigating User Interaction with Different Representations of Simulation Parameters | arXiv:2601.10232v1 Announce Type: new Abstract: Understanding complex parameter dependencies is critical for effective configuration and maintenance of software systems across diverse domains - from Computer-Aided Engineering (CAE) to cloud infrastructure and database management. However, legacy tabular interfaces crea... | https://arxiv.org/abs/2601.10232 | Academic Papers | svg |
6f8f1e11254e993414a55764dc715f66f03d4ffc346ed4e59206b7ae6064acf0 | 2026-01-16T00:00:00-05:00 | Proactive Local-Minima-Free Robot Navigation: Blending Motion Prediction with Safe Control | arXiv:2601.10233v1 Announce Type: new Abstract: This work addresses the challenge of safe and efficient mobile robot navigation in complex dynamic environments with concave moving obstacles. Reactive safe controllers like Control Barrier Functions (CBFs) design obstacle avoidance strategies based only on the current st... | https://arxiv.org/abs/2601.10233 | Academic Papers | svg |
14c57e86d3109485f1e536d5224c820a9b2b9fb4e1b9178662808a6e4a909da6 | 2026-01-16T00:00:00-05:00 | Who Owns the Text? Design Patterns for Preserving Authorship in AI-Assisted Writing | arXiv:2601.10236v1 Announce Type: new Abstract: AI writing assistants can reduce effort and improve fluency, but they may also weaken writers' sense of authorship. We study this tension with an ownership-aware co-writing editor that offers on-demand, sentence-level suggestions and tests two common design choices: perso... | https://arxiv.org/abs/2601.10236 | Academic Papers | svg |
a8ee897f824c008c4381fb14171f6c860e8ee1a0e1bb9b05e0e71f94281fe8ca | 2026-01-16T00:00:00-05:00 | Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD | arXiv:2601.10237v1 Announce Type: new Abstract: Differentially Private Stochastic Gradient Descent (DP-SGD) is the dominant paradigm for private training, but its fundamental limitations under worst-case adversarial privacy definitions remain poorly understood. We analyze DP-SGD in the $f$-differential privacy framewor... | https://arxiv.org/abs/2601.10237 | Academic Papers | svg |
c875aa172bca4a346e3e02d342395e555513071e438ed0d405c84b729731ee9b | 2026-01-16T00:00:00-05:00 | Loop as a Bridge: Can Looped Transformers Truly Link Representation Space and Natural Language Outputs? | arXiv:2601.10242v1 Announce Type: new Abstract: Large Language Models (LLMs) often exhibit a gap between their internal knowledge and their explicit linguistic outputs. In this report, we empirically investigate whether Looped Transformers (LTs)--architectures that increase computational depth by iterating shared layer... | https://arxiv.org/abs/2601.10242 | Academic Papers | svg |
71000317735b7a9317de8fb6a8402fd4a2007a1137054483ee2395f5a3ac3687 | 2026-01-16T00:00:00-05:00 | Attend to what I say: Highlighting relevant content on slides | arXiv:2601.10244v1 Announce Type: new Abstract: Imagine sitting in a presentation, trying to follow the speaker while simultaneously scanning the slides for relevant information. While the entire slide is visible, identifying the relevant regions can be challenging. As you focus on one part of the slide, the speaker mo... | https://arxiv.org/abs/2601.10244 | Academic Papers | svg |
4e41605ebbd57a0d74d00aefa106fad7ffea3ccd58232dd983f1476fd446f3a4 | 2026-01-16T00:00:00-05:00 | TRIM: Hybrid Inference via Targeted Stepwise Routing in Multi-Step Reasoning Tasks | arXiv:2601.10245v1 Announce Type: new Abstract: Multi-step reasoning tasks like mathematical problem solving are vulnerable to cascading failures, where a single incorrect step leads to complete solution breakdown. Current LLM routing methods assign entire queries to one model, treating all reasoning steps as equal. We... | https://arxiv.org/abs/2601.10245 | Academic Papers | svg |
5a65df887b6f124a5e84c6eda189c7079b5fae0b42e93b3f66a694ba4cd8069d | 2026-01-16T00:00:00-05:00 | coTherapist: A Behavior-Aligned Small Language Model to Support Mental Healthcare Experts | arXiv:2601.10246v1 Announce Type: new Abstract: Access to mental healthcare is increasingly strained by workforce shortages and rising demand, motivating the development of intelligent systems that can support mental healthcare experts. We introduce coTherapist, a unified framework utilizing a small language model to e... | https://arxiv.org/abs/2601.10246 | Academic Papers | svg |
16c98e01c63c17823c949e8fe9e3f7bbefbea05b96726c2cd9a15c21be3357a7 | 2026-01-16T00:00:00-05:00 | Restoring similarity in randomized Krylov methods with applications to eigenvalue problems and matrix functions | arXiv:2601.10248v1 Announce Type: new Abstract: The randomized Arnoldi process has been used in large-scale scientific computing because it produces a well-conditioned basis for the Krylov subspace more quickly than the standard Arnoldi process. However, the resulting Hessenberg matrix is generally not similar to the o... | https://arxiv.org/abs/2601.10248 | Academic Papers | svg |
374c539b02e311bebb8a63c6268c15072209a7d599f86da61f424863205866a3 | 2026-01-16T00:00:00-05:00 | X-SAM: Boosting Sharpness-Aware Minimization with Dominant-Eigenvector Gradient Correction | arXiv:2601.10251v1 Announce Type: new Abstract: Sharpness-Aware Minimization (SAM) aims to improve generalization by minimizing a worst-case perturbed loss over a small neighborhood of model parameters. However, during training, its optimization behavior does not always align with theoretical expectations, since both s... | https://arxiv.org/abs/2601.10251 | Academic Papers | svg |
7db9596004a70a8392290c24653e6e80c51fcb426de4c7e7a349171ea7d82886 | 2026-01-16T00:00:00-05:00 | Developer Interaction Patterns with Proactive AI: A Five-Day Field Study | arXiv:2601.10253v1 Announce Type: new Abstract: Current in-IDE AI coding tools typically rely on time-consuming manual prompting and context management, whereas proactive alternatives that anticipate developer needs without explicit invocation remain underexplored. Understanding when humans are receptive to such proact... | https://arxiv.org/abs/2601.10253 | Academic Papers | svg |
552fb567d380b661e836f86e5a5576077097cec1acf2a7bed37ba3c164fc097c | 2026-01-16T00:00:00-05:00 | NoReGeo: Non-Reasoning Geometry Benchmark | arXiv:2601.10254v1 Announce Type: new Abstract: We present NoReGeo, a novel benchmark designed to evaluate the intrinsic geometric understanding of large language models (LLMs) without relying on reasoning or algebraic computation. Unlike existing benchmarks that primarily assess models' proficiency in reasoning-based ... | https://arxiv.org/abs/2601.10254 | Academic Papers | svg |
dce1d9c5cdadbd14b2e27d66375acdb5f472720a1ef29a76de5f14390f7ed115 | 2026-01-16T00:00:00-05:00 | Error-Correcting Codes for the Sum Channel | arXiv:2601.10256v1 Announce Type: new Abstract: We introduce the sum channel, a new channel model motivated by applications in distributed storage and DNA data storage. In the error-free case, it takes as input an $\ell$-row binary matrix and outputs an $(\ell+1)$-row matrix whose first $\ell$ rows equal the input and ... | https://arxiv.org/abs/2601.10256 | Academic Papers | svg |
183a89df42571c6bf8f599771a5bac2d4d904515aeb971c65bda56c8423ebdc2 | 2026-01-16T00:00:00-05:00 | Untangling Input Language from Reasoning Language: A Diagnostic Framework for Cross-Lingual Moral Alignment in LLMs | arXiv:2601.10257v1 Announce Type: new Abstract: When LLMs judge moral dilemmas, do they reach different conclusions in different languages, and if so, why? Two factors could drive such differences: the language of the dilemma itself, or the language in which the model reasons. Standard evaluation conflates these by tes... | https://arxiv.org/abs/2601.10257 | Academic Papers | svg |
53046e3447e608ba76dd0ce5cb11514939a5eb14747ddfe9dd0da65d7bc8f904 | 2026-01-16T00:00:00-05:00 | Evolving with AI: A Longitudinal Analysis of Developer Logs | arXiv:2601.10258v1 Announce Type: new Abstract: AI-powered coding assistants are rapidly becoming fixtures in professional IDEs, yet their sustained influence on everyday development remains poorly understood. Prior research has focused on short-term use or self-reported perceptions, leaving open questions about how su... | https://arxiv.org/abs/2601.10258 | Academic Papers | svg |
01106bb74f40facde486684d2b492e83fc509fe4a63ebc790aee4958535745ee | 2026-01-16T00:00:00-05:00 | Transmission Mask Analysis for Range-Doppler Sensing in Half-Duplex ISAC | arXiv:2601.10259v1 Announce Type: new Abstract: In this paper, we analyze the periodic transmission masks for MASked Modulation (MASM) in half-duplex integrated sensing and communication (ISAC), and derive their closed-form expected range-Doppler response $\mathbb{E}\{r(k,l,\nu)\}$. We show that range sidelobes ($k\neq... | https://arxiv.org/abs/2601.10259 | Academic Papers | svg |
6de42a679692e0c76dc3c61e64d7b65192c0522bdff55969b6dfc96445600e1a | 2026-01-16T00:00:00-05:00 | XuanJia: A Comprehensive Virtualization-Based Code Obfuscator for Binary Protection | arXiv:2601.10261v1 Announce Type: new Abstract: Virtualization-based binary obfuscation is widely adopted to protect software intellectual property, yet existing approaches leave exception-handling (EH) metadata unprotected to preserve ABI compatibility. This exposed metadata leaks rich structural information, such as ... | https://arxiv.org/abs/2601.10261 | Academic Papers | svg |
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