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55c3d75cc81d106d42a0e7c69565eb50c525760ac8883f958d708aab66d3c183 | 2026-01-13T00:00:00-05:00 | Mobility Inequity and Risk Response After Hurricane Helene: Evidence from Real-Time Travel and Social Sentiment Data | arXiv:2601.06722v1 Announce Type: new Abstract: Hurricanes severely disrupt infrastructure and restrict access to essential services. While the physical impacts on post-disaster mobility are well studied, less is known about how individual travel behaviors change during and after disasters, and how these responses are ... | https://arxiv.org/abs/2601.06722 | Academic Papers | svg |
2e65e7a4e0e0ef0f2d1857fcb01fc03d98bef9b216d178aa7fbd7f9563435cc4 | 2026-01-13T00:00:00-05:00 | Approximating Matroid Basis Testing for Partition Matroids using Budget-In-Expectation | arXiv:2601.06723v1 Announce Type: new Abstract: We consider the following Stochastic Boolean Function Evaluation problem, which is closely related to several problems from the literature. A matroid $\mathcal{M}$ (in compact representation) on ground set $E$ is given, and each element $i\in E$ is active independently wi... | https://arxiv.org/abs/2601.06723 | Academic Papers | svg |
6bd348b5671f6659029c143caf60da0f493eb0769cb8ce2314e6ced15aeb94e4 | 2026-01-13T00:00:00-05:00 | DS-CIM: Digital Stochastic Computing-In-Memory Featuring Accurate OR-Accumulation via Sample Region Remapping for Edge AI Models | arXiv:2601.06724v1 Announce Type: new Abstract: Stochastic computing (SC) offers hardware simplicity but suffers from low throughput, while high-throughput Digital Computing-in-Memory (DCIM) is bottlenecked by costly adder logic for matrix-vector multiplication (MVM). To address this trade-off, this paper introduces a ... | https://arxiv.org/abs/2601.06724 | Academic Papers | svg |
257fb9c8f780bf3b44e5176ce637025e8c4ef84cc77773e0bd3f6ad23ee70376 | 2026-01-13T00:00:00-05:00 | When Humans Judge Irises: Pupil Size Normalization as an Aid and Synthetic Irises as a Challenge | arXiv:2601.06725v1 Announce Type: new Abstract: Iris recognition is a mature biometric technology offering remarkable precision and speed, and allowing for large-scale deployments to populations exceeding a billion enrolled users (e.g., AADHAAR in India). However, in forensic applications, a human expert may be needed ... | https://arxiv.org/abs/2601.06725 | Academic Papers | svg |
580b9c199083cee236c118668f1c150622e69bbded0957f113ff9885287374f3 | 2026-01-13T00:00:00-05:00 | Vextra: A Unified Middleware Abstraction for Heterogeneous Vector Database Systems | arXiv:2601.06727v1 Announce Type: new Abstract: The rapid integration of vector search into AI applications, particularly for Retrieval Augmented Generation (RAG), has catalyzed the emergence of a diverse ecosystem of specialized vector databases. While this innovation offers a rich choice of features and performance c... | https://arxiv.org/abs/2601.06727 | Academic Papers | svg |
f1e4d9bdd0e65cf160efde585b7ece5e079be7ab8a32ead9704c75e6c745cb56 | 2026-01-13T00:00:00-05:00 | Robust Evacuation for Multi-Drone Failure in Drone Light Shows | arXiv:2601.06728v1 Announce Type: new Abstract: Drone light shows have emerged as a popular form of entertainment in recent years. However, several high-profile incidents involving large-scale drone failures -- where multiple drones simultaneously fall from the sky -- have raised safety and reliability concerns. To ens... | https://arxiv.org/abs/2601.06728 | Academic Papers | svg |
3583de2a1ae1b9e613f3ef31dcb11cdb5b30013103393ce0bf7b13777d363056 | 2026-01-13T00:00:00-05:00 | Predicting Student Success with Heterogeneous Graph Deep Learning and Machine Learning Models | arXiv:2601.06729v1 Announce Type: new Abstract: Early identification of student success is crucial for enabling timely interventions, reducing dropout rates, and promoting on time graduation. In educational settings, AI powered systems have become essential for predicting student performance due to their advanced analy... | https://arxiv.org/abs/2601.06729 | Academic Papers | svg |
4213721a50430755c0bbda46d2d1e754acde6d27d9303d5a4e44557586c3bbc5 | 2026-01-13T00:00:00-05:00 | Why are there many equally good models? An Anatomy of the Rashomon Effect | arXiv:2601.06730v1 Announce Type: new Abstract: The Rashomon effect -- the existence of multiple, distinct models that achieve nearly equivalent predictive performance -- has emerged as a fundamental phenomenon in modern machine learning and statistics. In this paper, we explore the causes underlying the Rashomon effec... | https://arxiv.org/abs/2601.06730 | Academic Papers | svg |
09a184e57f07b5e551e299e87de68db9a5288687c2866d72afeb820f8b7ecbd8 | 2026-01-13T00:00:00-05:00 | Study of Adaptive Reliability-Driven Conditional Innovation Decoding for LDPC Codes | arXiv:2601.06732v1 Announce Type: new Abstract: In this work, we present an adaptive reliability-driven conditional innovation (AR-CID) decoding algorithm for low-density parity check (LDPC) codes. The proposed AR-CID decoding algorithm consists of one stage of message quality checking and another stage of message pass... | https://arxiv.org/abs/2601.06732 | Academic Papers | svg |
b4367fd604f68e85e2371f26daafe4f17e7fde76f38c204d9ba547efd7467bad | 2026-01-13T00:00:00-05:00 | Logic-Driven Semantic Communication for Resilient Multi-Agent Systems | arXiv:2601.06733v1 Announce Type: new Abstract: The advent of 6G networks is accelerating autonomy and intelligence in large-scale, decentralized multi-agent systems (MAS). While this evolution enables adaptive behavior, it also heightens vulnerability to stressors such as environmental changes and adversarial behavior... | https://arxiv.org/abs/2601.06733 | Academic Papers | svg |
11e6dbd186300b61b1ff2dc1fded2bd6e26b123bdc92d3d396eec60131d6f294 | 2026-01-13T00:00:00-05:00 | Deep Recurrent Hidden Markov Learning Framework for Multi-Stage Advanced Persistent Threat Prediction | arXiv:2601.06734v1 Announce Type: new Abstract: Advanced Persistent Threats (APTs) represent hidden, multi\-stage cyberattacks whose long term persistence and adaptive behavior challenge conventional intrusion detection systems (IDS). Although recent advances in machine learning and probabilistic modeling have improved... | https://arxiv.org/abs/2601.06734 | Academic Papers | svg |
9074b6c10b162a243cde76480c94f4260623a72d9390dfc7d028ba9090aa2013 | 2026-01-13T00:00:00-05:00 | Algorithmic Reductions: Network Flow and NP-Completeness in Real-World Scheduling Problems | arXiv:2601.06737v1 Announce Type: new Abstract: This paper presents two real-world scheduling problems and their algorithmic solutions through polynomial-time reductions. First, we address the Hospital Patient-to-Bed Assignment problem, demonstrating its reduction to Maximum Bipartite Matching and solution via Network ... | https://arxiv.org/abs/2601.06737 | Academic Papers | svg |
5dfbe352c3147fca4c6de013b03476d947e92e22b94b39c4ba2cf77d2f2fd035 | 2026-01-13T00:00:00-05:00 | Entropy-based Thermal Sensor Placement and Temperature Reconstruction based on Adaptive Compressive Sensing Theory | arXiv:2601.06740v1 Announce Type: new Abstract: This paper addresses the challenges of thermal sensor allocation and full-chip temperature reconstruction in multi-core systems by leveraging an entropy-based sensor placement strategy and an adaptive compressive sensing approach. By selecting sensor locations that captur... | https://arxiv.org/abs/2601.06740 | Academic Papers | svg |
04961543e589cad64d85de53e84d37e8ec16cf27c109215628cf3c5a0bf5fa2c | 2026-01-13T00:00:00-05:00 | Federated Continual Learning for Privacy-Preserving Hospital Imaging Classification | arXiv:2601.06742v1 Announce Type: new Abstract: Deep learning models for radiology interpretation increasingly rely on multi-institutional data, yet privacy regulations and distribution shift across hospitals limit central data pooling. Federated learning (FL) allows hospitals to collaboratively train models without sh... | https://arxiv.org/abs/2601.06742 | Academic Papers | svg |
13db4b354d9c99fd816221952d760efd956cd0966d9347c6d0ad415323455f1f | 2026-01-13T00:00:00-05:00 | FinForge: Semi-Synthetic Financial Benchmark Generation | arXiv:2601.06747v1 Announce Type: new Abstract: Evaluating Language Models (LMs) in specialized, high-stakes domains such as finance remains a significant challenge due to the scarcity of open, high-quality, and domain-specific datasets. Existing general-purpose benchmarks provide broad coverage but lack the depth and ... | https://arxiv.org/abs/2601.06747 | Academic Papers | svg |
7322d0857955f44ae9450009b74a637bb3ce6b47d5ab6e6275875b85e170c82d | 2026-01-13T00:00:00-05:00 | On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning | arXiv:2601.06748v1 Announce Type: new Abstract: Vision-Language-Action models have recently emerged as a powerful paradigm for general-purpose robot learning, enabling agents to map visual observations and natural-language instructions into executable robotic actions. Though popular, they are primarily trained via supe... | https://arxiv.org/abs/2601.06748 | Academic Papers | svg |
f6aae1d44dd25cdf9df2536b99b48f4984fdc2454b93c6f2548e2e6d19a30cf8 | 2026-01-13T00:00:00-05:00 | Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models | arXiv:2601.06750v1 Announce Type: new Abstract: Medical Multimodal Large Language Models (Med-MLLMs) require egocentric clinical intent understanding for real-world deployment, yet existing benchmarks fail to evaluate this critical capability. To address these challenges, we introduce MedGaze-Bench, the first benchmark... | https://arxiv.org/abs/2601.06750 | Academic Papers | svg |
97d875da838d2eed20b78cb310e9db6e93627ffb76dfc6b8afc3ee58ba08bc61 | 2026-01-13T00:00:00-05:00 | Towards Computational Chinese Paleography | arXiv:2601.06753v1 Announce Type: new Abstract: Chinese paleography, the study of ancient Chinese writing, is undergoing a computational turn powered by artificial intelligence. This position paper charts the trajectory of this emerging field, arguing that it is evolving from automating isolated visual tasks to creatin... | https://arxiv.org/abs/2601.06753 | Academic Papers | svg |
462539c7105e598d44d7ee7601f3ae6785850586c0fa335edddda59f6efef29e | 2026-01-13T00:00:00-05:00 | MTMCS-Bench: Evaluating Contextual Safety of Multimodal Large Language Models in Multi-Turn Dialogues | arXiv:2601.06757v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly deployed as assistants that interact through text and images, making it crucial to evaluate contextual safety when risk depends on both the visual scene and the evolving dialogue. Existing contextual safety benchma... | https://arxiv.org/abs/2601.06757 | Academic Papers | svg |
c9152c0846a701d840eacd135c86c9f7c812dbec85093c324d1bb55b24ef0bb9 | 2026-01-13T00:00:00-05:00 | A Backpropagation-Free Feedback-Hebbian Network for Continual Learning Dynamics | arXiv:2601.06758v1 Announce Type: new Abstract: Feedback-rich neural architectures can regenerate earlier representations and inject temporal context, making them a natural setting for strictly local synaptic plasticity. We ask whether a minimal, backpropagation-free feedback--Hebbian system can already express interpr... | https://arxiv.org/abs/2601.06758 | Academic Papers | svg |
23ae68bcbea57fb43cd1f664088d05c8af9e8d922ad39de2abb6f9c271c15c22 | 2026-01-13T00:00:00-05:00 | Comparative Separation: Evaluating Separation on Comparative Judgment Test Data | arXiv:2601.06761v1 Announce Type: new Abstract: This research seeks to benefit the software engineering society by proposing comparative separation, a novel group fairness notion to evaluate the fairness of machine learning software on comparative judgment test data. Fairness issues have attracted increasing attention ... | https://arxiv.org/abs/2601.06761 | Academic Papers | svg |
cf314a49387e28a0506c2f97a40c4bb434d3298c219eb6d7a63f7fd9add86e07 | 2026-01-13T00:00:00-05:00 | The Complexity of Finding Missing Answer Repairs | arXiv:2601.06764v1 Announce Type: new Abstract: We investigate the problem of identifying database repairs for missing tuples in query answers. We show that when the query is part of the input - the combined complexity setting - determining whether or not a repair exists is polynomial-time is equivalent to the satisfia... | https://arxiv.org/abs/2601.06764 | Academic Papers | svg |
2c08f73bcdfea1c0527c23b6c301dbecfed29ae8a2840e6dd5774863411aacf0 | 2026-01-13T00:00:00-05:00 | Control and Stability of a Multilevel Power System for a Future Distribution Network | arXiv:2601.06766v1 Announce Type: new Abstract: The growing integration of renewable energy sources into distribution networks poses significant challenges to frequency and voltage stability due to their intermittent nature and low-inertia dynamics. This paper proposes a multilevel control framework for a future decarb... | https://arxiv.org/abs/2601.06766 | Academic Papers | svg |
5d37a587925b0d479a186a9c3dacae57a9ac0086c13c556420b3988b3718bc44 | 2026-01-13T00:00:00-05:00 | GanitLLM: Difficulty-Aware Bengali Mathematical Reasoning through Curriculum-GRPO | arXiv:2601.06767v1 Announce Type: new Abstract: We present a Bengali mathematical reasoning model called GanitLLM (named after the Bangla word for mathematics, "Ganit"), together with a new difficulty-aware Bengali math corpus and a curriculum-based GRPO pipeline. Bengali is one of the world's most widely spoken langua... | https://arxiv.org/abs/2601.06767 | Academic Papers | svg |
4199abb8d39845962521bd25cdccdea459aabdca0981698a3f543883a64bb23b | 2026-01-13T00:00:00-05:00 | ALFA: A Safe-by-Design Approach to Mitigate Quishing Attacks Launched via Fancy QR Codes | arXiv:2601.06768v1 Announce Type: new Abstract: Phishing with Quick Response (QR) codes is termed as Quishing. The attackers exploit this method to manipulate individuals into revealing their confidential data. Recently, we see the colorful and fancy representations of QR codes, the 2D matrix of QR codes which does not... | https://arxiv.org/abs/2601.06768 | Academic Papers | svg |
d8f00ba482b6445d986e2ac9154207e32bf1d6257fb8d98fb2f57477e08849ef | 2026-01-13T00:00:00-05:00 | Structure-preserving learning and prediction in optimal control of collective motion | arXiv:2601.06770v1 Announce Type: new Abstract: Wide-spread adoption of unmanned vehicle technologies requires the ability to predict the motion of the combined vehicle operation from observations. While the general prediction of such motion for an arbitrary control mechanism is difficult, for a particular choice of co... | https://arxiv.org/abs/2601.06770 | Academic Papers | svg |
dca89e68fcf5c2ab7bb2d801e808d4a6210f13773eb148e755681055e156f79b | 2026-01-13T00:00:00-05:00 | Heterogeneous Interaction Network Analysis (HINA): A New Learning Analytics Approach for Modelling, Analyzing, and Visualizing Complex Interactions in Learning Processes | arXiv:2601.06771v1 Announce Type: new Abstract: Existing learning analytics approaches, which often model learning processes as sequences of learner actions or homogeneous relationships, are limited in capturing the distributed, multi-faceted nature of interactions in contemporary learning environments. To address this... | https://arxiv.org/abs/2601.06771 | Academic Papers | svg |
9cbe90a1bf09e59383a17833b6c888fa38001b51194bbe8782a28a073c48fffe | 2026-01-13T00:00:00-05:00 | The optimal error analysis of nonuniform L1 method for the variable-exponent subdiffusion model | arXiv:2601.06773v1 Announce Type: new Abstract: This work investigates the optimal error estimate of the fully discrete scheme for the variable-exponent subdiffusion model under the nonuniform temporal mesh. We apply the perturbation method to reformulate the original model into its equivalent form, and apply the L1 sc... | https://arxiv.org/abs/2601.06773 | Academic Papers | svg |
42852e2f128640abebecfbffdcdd2f3a92d085de92aec747d0e5e77efbc63bd5 | 2026-01-13T00:00:00-05:00 | ImmuniFraug: A Metacognitive Intervention Anti-Fraud Approach to Enhance Undergraduate Students' Cyber Fraud Awareness | arXiv:2601.06774v1 Announce Type: new Abstract: Cyber fraud now constitutes over half of criminal cases in China, with undergraduate students experiencing a disproportionate rise in victimization. Traditional anti-fraud training remains predominantly passive, yielding limited engagement and retention. This paper introd... | https://arxiv.org/abs/2601.06774 | Academic Papers | svg |
ddd296778f96a93809632c99cfc71a4813282cb510540c8802523cec91a57096 | 2026-01-13T00:00:00-05:00 | From Text to Simulation: A Multi-Agent LLM Workflow for Automated Chemical Process Design | arXiv:2601.06776v1 Announce Type: new Abstract: Process simulation is a critical cornerstone of chemical engineering design. Current automated chemical design methodologies focus mainly on various representations of process flow diagrams. However, transforming these diagrams into executable simulation flowsheets remain... | https://arxiv.org/abs/2601.06776 | Academic Papers | svg |
59108c5c6dbea2171e52c38c00553c09b9dc8a146f2e5115219cf5101c51e0e3 | 2026-01-13T00:00:00-05:00 | The Normalized Difference Layer: A Differentiable Spectral Index Formulation for Deep Learning | arXiv:2601.06777v1 Announce Type: new Abstract: Normalized difference indices have been a staple in remote sensing for decades. They stay reliable under lighting changes produce bounded values and connect well to biophysical signals. Even so, they are usually treated as a fixed pre processing step with coefficients set... | https://arxiv.org/abs/2601.06777 | Academic Papers | svg |
82ae3c8c19d98a4e185c2b0b7bb6adf83265a2863b90de604725abe0d8c7adc8 | 2026-01-13T00:00:00-05:00 | CyberLLM-FINDS 2025: Instruction-Tuned Fine-tuning of Domain-Specific LLMs with Retrieval-Augmented Generation and Graph Integration for MITRE Evaluation | arXiv:2601.06779v1 Announce Type: new Abstract: Large Language Models (LLMs) such as Gemma-2B have shown strong performance in various natural language processing tasks. However, general-purpose models often lack the domain expertise required for cybersecurity applications. This work presents a methodology to fine-tune... | https://arxiv.org/abs/2601.06779 | Academic Papers | svg |
157dda4b5b633dd4f5d6e6be26b2aa00e5db38ec4e18f2375ae6aaa6d1d350bb | 2026-01-13T00:00:00-05:00 | Multi-Stage Evolutionary Model Merging with Meta Data Driven Curriculum Learning for Sentiment-Specialized Large Language Modeling | arXiv:2601.06780v1 Announce Type: new Abstract: The emergence of large language models (LLMs) has significantly transformed natural language processing (NLP), enabling more generalized models to perform various tasks with minimal training. However, traditional sentiment analysis methods, which focus on individual tasks... | https://arxiv.org/abs/2601.06780 | Academic Papers | svg |
391206ed06f2d2e4f351549047415994277085cd41b464df64e803f18bae96f6 | 2026-01-13T00:00:00-05:00 | AutoTour: Automatic Photo Tour Guide with Smartphones and LLMs | arXiv:2601.06781v1 Announce Type: new Abstract: We present AutoTour, a system that enhances user exploration by automatically generating fine-grained landmark annotations and descriptive narratives for photos captured by users. The key idea of AutoTour is to fuse visual features extracted from photos with nearby geospa... | https://arxiv.org/abs/2601.06781 | Academic Papers | svg |
12665a04eb29916831f1b39ef1ce0b002330caa6936180f14b4aa7610ed4e15e | 2026-01-13T00:00:00-05:00 | EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMs | arXiv:2601.06786v1 Announce Type: new Abstract: Improving the reasoning abilities of large language models (LLMs) has largely relied on iterative self-training with model-generated data. While effective at boosting accuracy, existing approaches primarily reinforce successful reasoning paths, incurring a substantial cal... | https://arxiv.org/abs/2601.06786 | Academic Papers | svg |
d399da91534a5ea6f38254e73b6afa4864e8a4a3b9f6f8de796555087bd4e765 | 2026-01-13T00:00:00-05:00 | Garbage Attention in Large Language Models: BOS Sink Heads and Sink-aware Pruning | arXiv:2601.06787v1 Announce Type: new Abstract: Large Language Models (LLMs) are known to contain significant redundancy, yet a systematic explanation for why certain components, particularly in higher layers, are more redundant has remained elusive. In this work, we identify the BOS sink phenomenon as a key mechanism ... | https://arxiv.org/abs/2601.06787 | Academic Papers | svg |
46dd3cabb9d6aa4ef89aadd10927405a8433b23200c5a87c78fa288d235c8a4f | 2026-01-13T00:00:00-05:00 | Artificial Entanglement in the Fine-Tuning of Large Language Models | arXiv:2601.06788v1 Announce Type: new Abstract: Large language models (LLMs) can be adapted to new tasks using parameter-efficient fine-tuning (PEFT) methods that modify only a small number of trainable parameters, often through low-rank updates. In this work, we adopt a quantum-information-inspired perspective to unde... | https://arxiv.org/abs/2601.06788 | Academic Papers | svg |
2290bd7bf7a17e355227a717ecf061c19ce802101907e532f3d7989258a3a290 | 2026-01-13T00:00:00-05:00 | MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences | arXiv:2601.06789v1 Announce Type: new Abstract: While autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a "closed-world" limitation: they attempt to fix bugs from scratch or solely using local context, ignoring the immense historical human experience available ... | https://arxiv.org/abs/2601.06789 | Academic Papers | svg |
4e79eeb903ab831709b91c67dcc423c9fb59fe4424d4ca889b651891d028debe | 2026-01-13T00:00:00-05:00 | SecMoE: Communication-Efficient Secure MoE Inference via Select-Then-Compute | arXiv:2601.06790v1 Announce Type: new Abstract: Privacy-preserving Transformer inference has gained attention due to the potential leakage of private information. Despite recent progress, existing frameworks still fall short of practical model scales, with gaps up to a hundredfold. A possible way to close this gap is t... | https://arxiv.org/abs/2601.06790 | Academic Papers | svg |
db25efa6791d9259c723af78066518d8931626422f8f5a67be79f572d75f79b7 | 2026-01-13T00:00:00-05:00 | Cross-Modal Computational Model of Brain-Heart Interactions via HRV and EEG Feature | arXiv:2601.06792v1 Announce Type: new Abstract: The electroencephalogram (EEG) has been the gold standard for quantifying mental workload; however, due to its complexity and non-portability, it can be constraining. ECG signals, which are feasible on wearable equipment pieces such as headbands, present a promising metho... | https://arxiv.org/abs/2601.06792 | Academic Papers | svg |
74c2f654e3a11dd5a89eed7af12419e04d1ae3486f6b0147709203e2c38fa361 | 2026-01-13T00:00:00-05:00 | CliffordNet: All You Need is Geometric Algebra | arXiv:2601.06793v1 Announce Type: new Abstract: Modern computer vision architectures, from CNNs to Transformers, predominantly rely on the stacking of heuristic modules: spatial mixers (Attention/Conv) followed by channel mixers (FFNs). In this work, we challenge this paradigm by returning to mathematical first princip... | https://arxiv.org/abs/2601.06793 | Academic Papers | svg |
86271eda70ee4f72dbd5dda7d1ee4f76e9414eaac71fd8bdcfd69ef10e02617c | 2026-01-13T00:00:00-05:00 | No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning | arXiv:2601.06794v1 Announce Type: new Abstract: Critique-guided reinforcement learning (RL) has emerged as a powerful paradigm for training LLM agents by augmenting sparse outcome rewards with natural-language feedback. However, current methods often rely on static or offline critic models, which fail to adapt as the p... | https://arxiv.org/abs/2601.06794 | Academic Papers | svg |
6d7f6bdee6673f41549c65a4d7c4e4a7f6468d51169e8b50747b9474ff9a78d3 | 2026-01-13T00:00:00-05:00 | GDEPO: Group Dual-dynamic and Equal-right-advantage Policy Optimization with Enhanced Training Data Utilization for Sample-Constrained Reinforcement Learning | arXiv:2601.06795v1 Announce Type: new Abstract: Automated Theorem Proving (ATP) represents a fundamental challenge in Artificial Intelligence (AI), requiring the construction of machine-verifiable proofs in formal languages such as Lean to evaluate AI reasoning capabilities. Reinforcement learning (RL), particularly th... | https://arxiv.org/abs/2601.06795 | Academic Papers | svg |
898c5107b8ed692ec10635bab066d2e8be65fb406d84f2ded1462ecdda29a6ef | 2026-01-13T00:00:00-05:00 | Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers | arXiv:2601.06798v1 Announce Type: new Abstract: Leveraging the vast open-world knowledge and understanding capabilities of Large Language Models (LLMs) to develop general-purpose, semantically-aware recommender systems has emerged as a pivotal research direction in generative recommendation. However, existing methods f... | https://arxiv.org/abs/2601.06798 | Academic Papers | svg |
8c84b094bb473fd0ba3eafb302681d8fd5b155a365f1158038f1ba030570d9ac | 2026-01-13T00:00:00-05:00 | CIRAG: Construction-Integration Retrieval and Adaptive Generation for Multi-hop Question Answering | arXiv:2601.06799v1 Announce Type: new Abstract: Triple-based Iterative Retrieval-Augmented Generation (iRAG) mitigates document-level noise for multi-hop question answering. However, existing methods still face limitations: (i) greedy single-path expansion, which propagates early errors and fails to capture parallel ev... | https://arxiv.org/abs/2601.06799 | Academic Papers | svg |
36466e18118c48a52e54b8d6c4cb9ab6d747b5568bfcfb5fdee333c555f7747a | 2026-01-13T00:00:00-05:00 | Graph Neural Network with One-side Edge Sampling for Fraud Detection | arXiv:2601.06800v1 Announce Type: new Abstract: Financial fraud is always a major problem in the field of finance, as it can cause significant consequences. As a result, many approaches have been designed to detect it, and lately Graph Neural Networks (GNNs) have been demonstrated as a competent candidate. However, whe... | https://arxiv.org/abs/2601.06800 | Academic Papers | svg |
5b55fae67e7744637018f667b17d19570c759c1b7e1b3f380813cb8b31e8d864 | 2026-01-13T00:00:00-05:00 | Thinking with Deltas: Incentivizing Reinforcement Learning via Differential Visual Reasoning Policy | arXiv:2601.06801v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced reasoning capabilities in Large Language Models. However, adapting RLVR to multimodal domains suffers from a critical \textit{perception-reasoning decoupling}. Existing paradigms, driven by t... | https://arxiv.org/abs/2601.06801 | Academic Papers | svg |
9f468502cdd5a47fd2b0bbdd63b4fb74dbec75790c7ccc8840fb62c874fae12a | 2026-01-13T00:00:00-05:00 | Doing More with Less: Data Augmentation for Sudanese Dialect Automatic Speech Recognition | arXiv:2601.06802v1 Announce Type: new Abstract: Although many Automatic Speech Recognition (ASR) systems have been developed for Modern Standard Arabic (MSA) and Dialectal Arabic (DA), few studies have focused on dialect-specific implementations, particularly for low-resource Arabic dialects such as Sudanese. This pape... | https://arxiv.org/abs/2601.06802 | Academic Papers | svg |
741ddf662c9dad86fcea87220d8340cee7919cb8069327f84716166069b0a671 | 2026-01-13T00:00:00-05:00 | Forest Before Trees: Latent Superposition for Efficient Visual Reasoning | arXiv:2601.06803v1 Announce Type: new Abstract: While Chain-of-Thought empowers Large Vision-Language Models with multi-step reasoning, explicit textual rationales suffer from an information bandwidth bottleneck, where continuous visual details are discarded during discrete tokenization. Recent latent reasoning methods... | https://arxiv.org/abs/2601.06803 | Academic Papers | svg |
bb17417cc2272f36680583745e5897f6d0eb1f2a2d74543e38ee8edb5b891cd3 | 2026-01-13T00:00:00-05:00 | SpatialNav: Leveraging Spatial Scene Graphs for Zero-Shot Vision-and-Language Navigation | arXiv:2601.06806v1 Announce Type: new Abstract: Although learning-based vision-and-language navigation (VLN) agents can learn spatial knowledge implicitly from large-scale training data, zero-shot VLN agents lack this process, relying primarily on local observations for navigation, which leads to inefficient exploratio... | https://arxiv.org/abs/2601.06806 | Academic Papers | svg |
a72fffacf2585636ffbd6fc0430e46c8c1b08973b78369d43b95e434a7577b83 | 2026-01-13T00:00:00-05:00 | WFR-FM: Simulation-Free Dynamic Unbalanced Optimal Transport | arXiv:2601.06810v1 Announce Type: new Abstract: The Wasserstein-Fisher-Rao (WFR) metric extends dynamic optimal transport (OT) by coupling displacement with change of mass, providing a principled geometry for modeling unbalanced snapshot dynamics. Existing WFR solvers, however, are often unstable, computationally expen... | https://arxiv.org/abs/2601.06810 | Academic Papers | svg |
9a2d7bc965b811ae8cbd7af6551f6522ce27bfadc4ebfdf3aabb922c73f76937 | 2026-01-13T00:00:00-05:00 | Analyzing the effect of prediction accuracy on the distributionally-robust competitive ratio | arXiv:2601.06813v1 Announce Type: new Abstract: The field of algorithms with predictions aims to improve algorithm performance by integrating machine learning predictions into algorithm design. A central question in this area is how predictions can improve performance, and a key aspect of this analysis is the role of p... | https://arxiv.org/abs/2601.06813 | Academic Papers | svg |
58c918e9024053ef5775f301e9f9117ce7f393255b6d2d26930e7a09a105e671 | 2026-01-13T00:00:00-05:00 | AgentHallu: Benchmarking Automated Hallucination Attribution of LLM-based Agents | arXiv:2601.06818v1 Announce Type: new Abstract: As LLM-based agents operate over sequential multi-step reasoning, hallucinations arising at intermediate steps risk propagating along the trajectory, thus degrading overall reliability. Unlike hallucination detection in single-turn responses, diagnosing hallucinations in ... | https://arxiv.org/abs/2601.06818 | Academic Papers | svg |
6e3ec2cd480f78e6e04c71b3a6f6d60d32282a73dfe46790d4fc75a3ff6de2b5 | 2026-01-13T00:00:00-05:00 | Generative Modeling of Human-Computer Interfaces with Diffusion Processes and Conditional Control | arXiv:2601.06823v1 Announce Type: new Abstract: This study investigates human-computer interface generation based on diffusion models to overcome the limitations of traditional template-based design and fixed rule-driven methods. It first analyzes the key challenges of interface generation, including the diversity of i... | https://arxiv.org/abs/2601.06823 | Academic Papers | svg |
0216c9c7959f903efb6da5989e8d763f57dfe119faf791fe48a2f3f2fd6df8d6 | 2026-01-13T00:00:00-05:00 | PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection | arXiv:2601.06827v1 Announce Type: new Abstract: Detecting pre-training data in Large Language Models (LLMs) is crucial for auditing data privacy and copyright compliance, yet it remains challenging in black-box, zero-shot settings where computational resources and training data are scarce. While existing likelihood-bas... | https://arxiv.org/abs/2601.06827 | Academic Papers | svg |
df33bab88f58e08fe022077a69ed9d3ab6df99bb7a6786a534b76d249a90bcd7 | 2026-01-13T00:00:00-05:00 | Spectral Shadows: When Communication Complexity Meets Linear Invariance Testing | arXiv:2601.06828v1 Announce Type: new Abstract: In this short note, we initiate the study of the Linear Isomorphism Testing Problem in the setting of communication complexity, a natural linear algebraic generalization of the classical Equality problem. Given Boolean functions $f, g : \mathbb{F}_2^n \to \{-1, +1\}$, Ali... | https://arxiv.org/abs/2601.06828 | Academic Papers | svg |
bc846a2141508504fe9204ff3a666a71cc5c17312f6a72670c43869c6e47eb6c | 2026-01-13T00:00:00-05:00 | MoEScore: Mixture-of-Experts-Based Text-Audio Relevance Score Prediction for Text-to-Audio System Evaluation | arXiv:2601.06829v1 Announce Type: new Abstract: Recent advances in generative models have enabled modern Text-to-Audio (TTA) systems to synthesize audio with high perceptual quality. However, TTA systems often struggle to maintain semantic consistency with the input text, leading to mismatches in sound events, temporal... | https://arxiv.org/abs/2601.06829 | Academic Papers | svg |
9ce30ed2a4829e13c904f48714f2c1cd9e238ad18bad2ceeed509df16d7d6860 | 2026-01-13T00:00:00-05:00 | SARA: Scene-Aware Reconstruction Accelerator | arXiv:2601.06831v1 Announce Type: new Abstract: We present SARA (Scene-Aware Reconstruction Accelerator), a geometry-driven pair selection module for Structure-from-Motion (SfM). Unlike conventional pipelines that select pairs based on visual similarity alone, SARA introduces geometry-first pair selection by scoring re... | https://arxiv.org/abs/2601.06831 | Academic Papers | svg |
e64f80df9addb83f1d6b4812a3e2b1985adda5403ddab19fd390788317c86afc | 2026-01-13T00:00:00-05:00 | SPINE Gripper: A Twisted Underactuated Mechanism-based Passive Mode-Transition Gripper | arXiv:2601.06833v1 Announce Type: new Abstract: This paper presents a single-actuator passive gripper that achieves both stable grasping and continuous bidirectional in-hand rotation through mechanically encoded power transmission logic. Unlike conventional multifunctional grippers that require multiple actuators, sens... | https://arxiv.org/abs/2601.06833 | Academic Papers | svg |
bc683a855df38ab23562de11bce46d0a008c0ded0b483d93ae4122173eb94cdc | 2026-01-13T00:00:00-05:00 | Enhancing Low-resolution Image Representation Through Normalizing Flows | arXiv:2601.06834v1 Announce Type: new Abstract: Low-resolution image representation is a special form of sparse representation that retains only low-frequency information while discarding high-frequency components. This property reduces storage and transmission costs and benefits various image processing tasks. However... | https://arxiv.org/abs/2601.06834 | Academic Papers | svg |
9740396879c30d60ab1096c199405ecd12a4f0585419cdf248cdb2f8f5b8d154 | 2026-01-13T00:00:00-05:00 | OSCAR: Optical-aware Semantic Control for Aleatoric Refinement in Sar-to-Optical Translation | arXiv:2601.06835v1 Announce Type: new Abstract: Synthetic Aperture Radar (SAR) provides robust all-weather imaging capabilities; however, translating SAR observations into photo-realistic optical images remains a fundamentally ill-posed problem. Current approaches are often hindered by the inherent speckle noise and ge... | https://arxiv.org/abs/2601.06835 | Academic Papers | svg |
0b416fb6bdffcfc3d2f767432bdd622bd9d030f598245fbdc4a00b11dfe00528 | 2026-01-13T00:00:00-05:00 | Optimal Rate Region for Multi-server Secure Aggregation with User Collusion | arXiv:2601.06836v1 Announce Type: new Abstract: Secure aggregation is a fundamental primitive in privacy-preserving distributed learning systems, where an aggregator aims to compute the sum of users' inputs without revealing individual data. In this paper, we study a multi-server secure aggregation problem in a two-hop... | https://arxiv.org/abs/2601.06836 | Academic Papers | svg |
112b172b9946da3be6e92c3bf90d2b7fa47ca3682341919b34035955badeae48 | 2026-01-13T00:00:00-05:00 | CHASE: LLM Agents for Dissecting Malicious PyPI Packages | arXiv:2601.06838v1 Announce Type: new Abstract: Modern software package registries like PyPI have become critical infrastructure for software development, but are increasingly exploited by threat actors distributing malicious packages with sophisticated multi-stage attack chains. While Large Language Models (LLMs) offe... | https://arxiv.org/abs/2601.06838 | Academic Papers | svg |
618f4eec5b7ae5c817330afa6f325bad9318fd7d6a371dc9c6de637ac02eee18 | 2026-01-13T00:00:00-05:00 | PRISM: Color-Stratified Point Cloud Sampling | arXiv:2601.06839v1 Announce Type: new Abstract: We present PRISM, a novel color-guided stratified sampling method for RGB-LiDAR point clouds. Our approach is motivated by the observation that unique scene features often exhibit chromatic diversity while repetitive, redundant features are homogeneous in color. Conventio... | https://arxiv.org/abs/2601.06839 | Academic Papers | svg |
7479d22e78171a655b9c68c67dce01c5124b0afe7720007dc417ba3bb8d26e8f | 2026-01-13T00:00:00-05:00 | Efficient Subdivision of B\'{e}zier Curves/Surfaces via Blossoms | arXiv:2601.06841v1 Announce Type: new Abstract: We consider the problem of B\'{e}zier curves/surfaces subdivision using blossoms. We propose closed-form formulae for blossoms evaluation, as needed for the calculation of control points. This approach leads to direct and efficient way to obtain subdivisions for B\'{e}zie... | https://arxiv.org/abs/2601.06841 | Academic Papers | svg |
2ed59b008d3c5a360302be2586f97809ad792c6ea68eab909ee59a38c4389f49 | 2026-01-13T00:00:00-05:00 | Seeing through the Conflict: Transparent Knowledge Conflict Handling in Retrieval-Augmented Generation | arXiv:2601.06842v1 Announce Type: new Abstract: Large language models (LLMs) equipped with retrieval--the Retrieval-Augmented Generation (RAG) paradigm--should combine their parametric knowledge with external evidence, yet in practice they often hallucinate, over-trust noisy snippets, or ignore vital context. We introd... | https://arxiv.org/abs/2601.06842 | Academic Papers | svg |
1a09f5713eadee7d5d6022e24ef25a64bbf94764abbb3266045838d5f4485ccf | 2026-01-13T00:00:00-05:00 | Speak While Watching: Unleashing TRUE Real-Time Video Understanding Capability of Multimodal Large Language Models | arXiv:2601.06843v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved strong performance across many tasks, yet most systems remain limited to offline inference, requiring complete inputs before generating outputs. Recent streaming methods reduce latency by interleaving perception and g... | https://arxiv.org/abs/2601.06843 | Academic Papers | svg |
e71ba185ac735d0c03b4ad752ac6df9440f3d3ea56fa36bfe84c88383a435e44 | 2026-01-13T00:00:00-05:00 | Variational decomposition autoencoding improves disentanglement of latent representations | arXiv:2601.06844v1 Announce Type: new Abstract: Understanding the structure of complex, nonstationary, high-dimensional time-evolving signals is a central challenge in scientific data analysis. In many domains, such as speech and biomedical signal processing, the ability to learn disentangled and interpretable represen... | https://arxiv.org/abs/2601.06844 | Academic Papers | svg |
a622de662d8c40f40b2ffb277de21dea0a58ad6fe9345ba497bf2e7963ff3d17 | 2026-01-13T00:00:00-05:00 | Code Evolution for Control: Synthesizing Policies via LLM-Driven Evolutionary Search | arXiv:2601.06845v1 Announce Type: new Abstract: Designing effective control policies for autonomous systems remains a fundamental challenge, traditionally addressed through reinforcement learning or manual engineering. While reinforcement learning has achieved remarkable success, it often suffers from high sample compl... | https://arxiv.org/abs/2601.06845 | Academic Papers | svg |
c3ea2fdaea14b3acaabc425e3136e849cd2668f4b79a701491884f8c5a0eadc2 | 2026-01-13T00:00:00-05:00 | MedGround: Bridging the Evidence Gap in Medical Vision-Language Models with Verified Grounding Data | arXiv:2601.06847v1 Announce Type: new Abstract: Vision-Language Models (VLMs) can generate convincing clinical narratives, yet frequently struggle to visually ground their statements. We posit this limitation arises from the scarcity of high-quality, large-scale clinical referring-localization pairs. To address this, w... | https://arxiv.org/abs/2601.06847 | Academic Papers | svg |
b0f8e1b4cd8897f0d3505683075cd164edddaac9866350d4ce5ba45770694ce9 | 2026-01-13T00:00:00-05:00 | Explainable Multimodal Aspect-Based Sentiment Analysis with Dependency-guided Large Language Model | arXiv:2601.06848v1 Announce Type: new Abstract: Multimodal aspect-based sentiment analysis (MABSA) aims to identify aspect-level sentiments by jointly modeling textual and visual information, which is essential for fine-grained opinion understanding in social media. Existing approaches mainly rely on discriminative cla... | https://arxiv.org/abs/2601.06848 | Academic Papers | svg |
9259705c6ec3080a16d654525eceb0b95d3bd651eaf7cfcc20fc3d5e1ac1f2e3 | 2026-01-13T00:00:00-05:00 | Analysis and Efficient Sylvester-Based Implementation of a Dimension-Split ETD2RK Scheme for Multidimensional Reaction-Diffusion Equations | arXiv:2601.06849v1 Announce Type: new Abstract: We propose and analyze a second-order, dimension-split exponential time differencing Runge--Kutta scheme (ETD2RK-DS) for multidimensional reaction--diffusion equations in two and three spatial dimensions. Under mild assumptions on the nonlinear source term, we establish u... | https://arxiv.org/abs/2601.06849 | Academic Papers | svg |
47f5b0384e40b8be9e41c367295c8da5ad8aac4a99f376ddd1357884eec89d80 | 2026-01-13T00:00:00-05:00 | A Brain-like Synergistic Core in LLMs Drives Behaviour and Learning | arXiv:2601.06851v1 Announce Type: new Abstract: The independent evolution of intelligence in biological and artificial systems offers a unique opportunity to identify its fundamental computational principles. Here we show that large language models spontaneously develop synergistic cores -- components where information... | https://arxiv.org/abs/2601.06851 | Academic Papers | svg |
d2313d876074963a9b087386bc05b94ffe665a2c3e2ba5353058bd60d3ca386c | 2026-01-13T00:00:00-05:00 | {\dag}DAGGER: Distractor-Aware Graph Generation for Executable Reasoning in Math Problems | arXiv:2601.06853v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting is widely adopted for mathematical problem solving, including in low-resource languages, yet its behavior under irrelevant context remains underexplored. To systematically study this challenge, we introduce DISTRACTMATH-BN, a Bangla benchm... | https://arxiv.org/abs/2601.06853 | Academic Papers | svg |
2118f3a1f113bc948b62e4c632a46cd94460b6c3eda88bfd90c40ffb566d5564 | 2026-01-13T00:00:00-05:00 | Semilinear single-track vehicle models with distributed tyre friction dynamics | arXiv:2601.06854v1 Announce Type: new Abstract: This paper introduces a novel family of single-track vehicle models that incorporate a distributed representation of transient tyre dynamics, whilst simultaneously accounting for nonlinear effects induced by friction. The core of the proposed framework is represented by t... | https://arxiv.org/abs/2601.06854 | Academic Papers | svg |
bca2919c59a11cbff1cd74dabdcce5298ccff424fa9233f0243540fb4addbdd6 | 2026-01-13T00:00:00-05:00 | MoE-DisCo:Low Economy Cost Training Mixture-of-Experts Models | arXiv:2601.06857v1 Announce Type: new Abstract: Training large-scale Mixture-of-Experts (MoE) models typically requires high-memory, high-bandwidth GPUs (e.g., A100), and their high cost has become a major barrier to large-model training. In contrast, affordable hardware is low-cost but constrained by memory capacity a... | https://arxiv.org/abs/2601.06857 | Academic Papers | svg |
f9081b3a9255975cb25f715e01be70a8b57ee3219110d357d06717f638c63b24 | 2026-01-13T00:00:00-05:00 | ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration | arXiv:2601.06860v1 Announce Type: new Abstract: Large Language Models (LLMs) can extend their parameter knowledge limits by adopting the Tool-Integrated Reasoning (TIR) paradigm. However, existing LLM-based agent training framework often focuses on answers' accuracy, overlooking specific alignment for behavior patterns... | https://arxiv.org/abs/2601.06860 | Academic Papers | svg |
dca246955f7c9df535901b314e19dd8000bcb9e724e23b3fbb306b0a57fdfdb6 | 2026-01-13T00:00:00-05:00 | BiasLab: A Multilingual, Dual-Framing Framework for Robust Measurement of Output-Level Bias in Large Language Models | arXiv:2601.06861v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in high-stakes contexts where their outputs influence real-world decisions. However, evaluating bias in LLM outputs remains methodologically challenging due to sensitivity to prompt wording, limited multilingual cover... | https://arxiv.org/abs/2601.06861 | Academic Papers | svg |
1c98a02d6847204b34b69ef87f47cd4e7cc7dad9a0010de400edbf15dcf7fa4e | 2026-01-13T00:00:00-05:00 | qAttCNN - Self Attention Mechanism for Video QoE Prediction in Encrypted Traffic | arXiv:2601.06862v1 Announce Type: new Abstract: The rapid growth of multimedia consumption, driven by major advances in mobile devices since the mid-2000s, has led to widespread use of video conferencing applications (VCAs) such as Zoom and Google Meet, as well as instant messaging applications (IMAs) like WhatsApp and... | https://arxiv.org/abs/2601.06862 | Academic Papers | svg |
5751f155644b35579783abf5618b713924b264b04dea14186c97438532ac88b9 | 2026-01-13T00:00:00-05:00 | United We Defend: Collaborative Membership Inference Defenses in Federated Learning | arXiv:2601.06866v1 Announce Type: new Abstract: Membership inference attacks (MIAs), which determine whether a specific data point was included in the training set of a target model, have posed severe threats in federated learning (FL). Unfortunately, existing MIA defenses, typically applied independently to each clien... | https://arxiv.org/abs/2601.06866 | Academic Papers | svg |
2694a7006451c9562101164a7f7a06bbdf288ba22e391d7207fc5669747df4ce | 2026-01-13T00:00:00-05:00 | U-MASK: User-adaptive Spatio-Temporal Masking for Personalized Mobile AI Applications | arXiv:2601.06867v1 Announce Type: new Abstract: Personalized mobile artificial intelligence applications are widely deployed, yet they are expected to infer user behavior from sparse and irregular histories under a continuously evolving spatio-temporal context. This setting induces a fundamental tension among three req... | https://arxiv.org/abs/2601.06867 | Academic Papers | svg |
182d929a4128097d041ddaedbb9c3d8afa6f7870bcf5ac40568d2271d134653f | 2026-01-13T00:00:00-05:00 | DaQ-MSA: Denoising and Qualifying Diffusion Augmentations for Multimodal Sentiment Analysis | arXiv:2601.06870v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated strong performance on vision-language tasks, yet their effectiveness on multimodal sentiment analysis remains constrained by the scarcity of high-quality training data, which limits accurate multimodal understandi... | https://arxiv.org/abs/2601.06870 | Academic Papers | svg |
0e8ab5cc6914871ce3bf94b9570529853af8d89c2e62ce40031f45ff7f633af8 | 2026-01-13T00:00:00-05:00 | Applying Embedding-Based Retrieval to Airbnb Search | arXiv:2601.06873v1 Announce Type: new Abstract: The goal of Airbnb search is to match guests with the ideal accommodation that fits their travel needs. This is a challenging problem, as popular search locations can have around a hundred thousand available homes, and guests themselves have a wide variety of preferences.... | https://arxiv.org/abs/2601.06873 | Academic Papers | svg |
cd3043250d58993cdac85cfb75a27a10c8e67a8200eb3ccdf628d0003395436a | 2026-01-13T00:00:00-05:00 | MVGGT: Multimodal Visual Geometry Grounded Transformer for Multiview 3D Referring Expression Segmentation | arXiv:2601.06874v1 Announce Type: new Abstract: Most existing 3D referring expression segmentation (3DRES) methods rely on dense, high-quality point clouds, while real-world agents such as robots and mobile phones operate with only a few sparse RGB views and strict latency constraints. We introduce Multi-view 3D Referr... | https://arxiv.org/abs/2601.06874 | Academic Papers | svg |
d0d54d320d17b0f99be8cea0ba3abdc94a3ac7defec8457caa59aa0559cc67ee | 2026-01-13T00:00:00-05:00 | An Ubuntu-Guided Large Language Model Framework for Cognitive Behavioral Mental Health Dialogue | arXiv:2601.06875v1 Announce Type: new Abstract: South Africa's escalating mental health crisis, compounded by limited access to culturally responsive care, calls for innovative and contextually grounded interventions. While large language models show considerable promise for mental health support, their predominantly W... | https://arxiv.org/abs/2601.06875 | Academic Papers | svg |
eedaeb88e5b41f618a40f9128cc6ffe7384ddaf070f83698ecd7389a55318260 | 2026-01-13T00:00:00-05:00 | Personality-Aware Reinforcement Learning for Persuasive Dialogue with LLM-Driven Simulation | arXiv:2601.06877v1 Announce Type: new Abstract: Effective persuasive dialogue agents adapt their strategies to individual users, accounting for the evolution of their psychological states and intentions throughout conversations. We present a personality-aware reinforcement learning approach comprising three main module... | https://arxiv.org/abs/2601.06877 | Academic Papers | svg |
d278b6382958f671b94807fe6fba9dd3315397160df8c34f0a316ccdf8c9e1aa | 2026-01-13T00:00:00-05:00 | Fast frequency response with heterogeneous communication delay management under the SCION Internet architecture | arXiv:2601.06879v1 Announce Type: new Abstract: System operators can increasingly exploit distributed energy resources (DERs) and controllable loads (CLs) to provide frequency response services. In conventional practice, communication between the system operator and flexible devices relies on the Border Gateway Protoco... | https://arxiv.org/abs/2601.06879 | Academic Papers | svg |
dd91e9bc5cc5dc40dc352e4a8bef301894838fc0183ee19dcab7e402990502b5 | 2026-01-13T00:00:00-05:00 | Unsupervised Domain Adaptation with SAM-RefiSeR for Enhanced Brain Tumor Segmentation | arXiv:2601.06882v1 Announce Type: new Abstract: Unsupervised Domain Adaptation with SAM-RefiSeR for Enhanced Brain Tumor Segmentation | https://arxiv.org/abs/2601.06882 | Academic Papers | svg |
26e7493f6b124593570fa48b0b5ca2acf48f9ddf6466bc784253d046f345c04b | 2026-01-13T00:00:00-05:00 | MixRI: Mixing Features of Reference Images for Novel Object Pose Estimation | arXiv:2601.06883v1 Announce Type: new Abstract: We present MixRI, a lightweight network that solves the CAD-based novel object pose estimation problem in RGB images. It can be instantly applied to a novel object at test time without finetuning. We design our network to meet the demands of real-world applications, empha... | https://arxiv.org/abs/2601.06883 | Academic Papers | svg |
4541e3bfbe2be633ae855b08a1ea060bacb14e0904e43c18c97f71bbb614f339 | 2026-01-13T00:00:00-05:00 | Paraphrasing Adversarial Attack on LLM-as-a-Reviewer | arXiv:2601.06884v1 Announce Type: new Abstract: The use of large language models (LLMs) in peer review systems has attracted growing attention, making it essential to examine their potential vulnerabilities. Prior attacks rely on prompt injection, which alters manuscript content and conflates injection susceptibility w... | https://arxiv.org/abs/2601.06884 | Academic Papers | svg |
4af622502c3b864ede9dde05bcc8e22b5fe6b9fba024c10f04d7cb754f293422 | 2026-01-13T00:00:00-05:00 | Understanding the Performance Behaviors of End-to-End Protein Design Pipelines on GPUs | arXiv:2601.06885v1 Announce Type: new Abstract: Recent computational advances enable protein design pipelines to run end-to-end on GPUs, yet their heterogeneous computational behaviors remain undercharacterized at the system level. We implement and profile a representative pipeline at both component and full-pipeline g... | https://arxiv.org/abs/2601.06885 | Academic Papers | svg |
47c098f45b6ba41ac4b98bbaa478c6a45a1a8e32ea8baa5c85ac2c6337e2f46b | 2026-01-13T00:00:00-05:00 | Learning-Augmented Performance Model for Tensor Product Factorization in High-Order FEM | arXiv:2601.06886v1 Announce Type: new Abstract: Accurate performance prediction is essential for optimizing scientific applications on modern high-performance computing (HPC) architectures. Widely used performance models primarily focus on cache and memory bandwidth, which is suitable for many memory-bound workloads. H... | https://arxiv.org/abs/2601.06886 | Academic Papers | svg |
1452310b28ea5fd588b26d38c461e0877600bcc9f650b16f95d5ec12dff24ec0 | 2026-01-13T00:00:00-05:00 | Observability-Enhanced Target Motion Estimation via Bearing-Box: Theory and MAV Applications | arXiv:2601.06887v1 Announce Type: new Abstract: Monocular vision-based target motion estimation is a fundamental challenge in numerous applications. This work introduces a novel bearing-box approach that fully leverages modern 3D detection measurements that are widely available nowadays but have not been well explored ... | https://arxiv.org/abs/2601.06887 | Academic Papers | svg |
1479d2e2190ed12dbcfbcdf0d00a631f8867743cc2930bf1271dd8941ef6df9b | 2026-01-13T00:00:00-05:00 | CLIMP: Contrastive Language-Image Mamba Pretraining | arXiv:2601.06891v1 Announce Type: new Abstract: Contrastive Language-Image Pre-training (CLIP) relies on Vision Transformers whose attention mechanism is susceptible to spurious correlations, and scales quadratically with resolution. To address these limitations, We present CLIMP, the first fully Mamba-based contrastiv... | https://arxiv.org/abs/2601.06891 | Academic Papers | svg |
e063feb79818cc9c215b493bf9fad0e00178b21bfa0ce6abf0b84832c41662d4 | 2026-01-13T00:00:00-05:00 | How Do Ports Organise Innovation? Linking Port Governance, Ownership, and Living Labs | arXiv:2601.06894v1 Announce Type: new Abstract: Ports are pivotal to decarbonisation and resilience, yet port studies rarely examine how ownership and decision rights shape the process and outcomes of sustainability and digital pilots. Living Lab (LL) scholarship offers strong concepts, but limited sector-grounded expl... | https://arxiv.org/abs/2601.06894 | Academic Papers | svg |
34663c7124cf91af319935d76041aa72454c38e882462cd1096261c375703eb8 | 2026-01-13T00:00:00-05:00 | Resilience by Design: A KPI for Heavy-Duty Megawatt Charging | arXiv:2601.06898v1 Announce Type: new Abstract: We introduce a stressor-agnostic Resilience Key Performance Indicator (Resilience KPI) for megawatt charging stations (MSC) serving heavy-duty vehicles. Beyond routine performance statistics (e.g., availability, throughput), the KPI quantifies a site's ability to anticipa... | https://arxiv.org/abs/2601.06898 | Academic Papers | svg |
9fdcdf12c1ba1a80df80ab20e18d00c84f3c9dc74bf278f7b79eb12e06232339 | 2026-01-13T00:00:00-05:00 | V2P: Visual Attention Calibration for GUI Grounding via Background Suppression and Center Peaking | arXiv:2601.06899v1 Announce Type: new Abstract: Precise localization of GUI elements is crucial for the development of GUI agents. Traditional methods rely on bounding box or center-point regression, neglecting spatial interaction uncertainty and visual-semantic hierarchies. Recent methods incorporate attention mechani... | https://arxiv.org/abs/2601.06899 | Academic Papers | svg |
25dbf246fed42edae521cc45d129f2d7ea5656308482793dbf9bb0d75bbda123 | 2026-01-13T00:00:00-05:00 | Santa Clara 3D: Digital Reconstruction and Storytelling of a Francoist Concentration Camp | arXiv:2601.06902v1 Announce Type: new Abstract: This paper explores the potential of digital reconstruction and interactive storytelling to preserve historically suppressed sites. The main objective of an interdisciplinary team of data scientists from the MEMORISE project and associates of the memory association Asocia... | https://arxiv.org/abs/2601.06902 | Academic Papers | svg |
decae0afb065eab5db0a68c62b27d70b58d626209354a6532d0089668c1a6783 | 2026-01-13T00:00:00-05:00 | Divergence-Based Adaptive Aggregation for Byzantine Robust Federated Learning | arXiv:2601.06903v1 Announce Type: new Abstract: Inherent client drifts caused by data heterogeneity, as well as vulnerability to Byzantine attacks within the system, hinder effective model training and convergence in federated learning (FL). This paper presents two new frameworks, named DiveRgence-based Adaptive aGgreg... | https://arxiv.org/abs/2601.06903 | Academic Papers | svg |
96e9ecd4259711835cdac3ce43cf7079981c89e719c14832d15acb02a527963d | 2026-01-13T00:00:00-05:00 | Large Artificial Intelligence Models for Future Wireless Communications | arXiv:2601.06906v1 Announce Type: new Abstract: The anticipated integration of large artificial intelligence (AI) models with wireless communications is estimated to usher a transformative wave in the forthcoming information age. As wireless networks grow in complexity, the traditional methodologies employed for optimi... | https://arxiv.org/abs/2601.06906 | Academic Papers | svg |
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