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dc2ca92b0122e20b880bbccc09d51ce8051b371d6eecbeb01a882e269296cc2e | 2026-01-16T00:00:00-05:00 | Improved Algorithms for Fair Matroid Submodular Maximization | arXiv:2601.09860v1 Announce Type: new Abstract: Submodular maximization subject to matroid constraints is a central problem with many applications in machine learning. As algorithms are increasingly used in decision-making over datapoints with sensitive attributes such as gender or race, it is becoming crucial to enfor... | https://arxiv.org/abs/2601.09860 | Academic Papers | svg |
cd57a203e1b7c339551f8f49a59f63789032e7b71ef4065880de2bfa2d522e1b | 2026-01-16T00:00:00-05:00 | High signal-to-noise ratio asymptotics of entropy-constrained Gaussian channel capacity | arXiv:2601.09864v1 Announce Type: new Abstract: We study the input-entropy-constrained Gaussian channel capacity problem in the asymptotic high signal-to-noise ratio (SNR) regime. We show that the capacity-achieving distribution as SNR goes to infinity is given by a discrete Gaussian distribution supported on a scaled ... | https://arxiv.org/abs/2601.09864 | Academic Papers | svg |
ea884e8cc9ed1f41aa1d89abbbba200756ef01ca64bc32ff7a17ccf33342ecfe | 2026-01-16T00:00:00-05:00 | Advancing Model Refinement: Muon-Optimized Distillation and Quantization for LLM Deployment | arXiv:2601.09865v1 Announce Type: new Abstract: Large Language Models (LLMs) enable advanced natural language processing but face deployment challenges on resource-constrained edge devices due to high computational, memory, and energy demands. Optimizing these models requires addressing three key challenges: acquiring ... | https://arxiv.org/abs/2601.09865 | Academic Papers | svg |
ba51d57318269724107292e4278536724a31a1c484620795ad859cd88020c9ce | 2026-01-16T00:00:00-05:00 | VibrantSR: Sub-Meter Canopy Height Models from Sentinel-2 Using Generative Flow Matching | arXiv:2601.09866v1 Announce Type: new Abstract: We present VibrantSR (Vibrant Super-Resolution), a generative super-resolution framework for estimating 0.5 meter canopy height models (CHMs) from 10 meter Sentinel-2 imagery. Unlike approaches based on aerial imagery that are constrained by infrequent and irregular acqui... | https://arxiv.org/abs/2601.09866 | Academic Papers | svg |
b333d4205079995c236070536174248ff63e2091ca7ce630375600ed9ac4b5ea | 2026-01-16T00:00:00-05:00 | AmbShield: Enhancing Physical Layer Security with Ambient Backscatter Devices against Eavesdroppers | arXiv:2601.09867v1 Announce Type: new Abstract: Passive eavesdropping compromises confidentiality in wireless networks, especially in resource-constrained environments where heavyweight cryptography is impractical. Physical layer security (PLS) exploits channel randomness and spatial selectivity to confine information ... | https://arxiv.org/abs/2601.09867 | Academic Papers | svg |
e1b733ff9615cf058938eea69523bc05a1b7c7f1b55174ae61426a662250eead | 2026-01-16T00:00:00-05:00 | A Scoping Review of the Ethical Perspectives on Anthropomorphising Large Language Model-Based Conversational Agents | arXiv:2601.09869v1 Announce Type: new Abstract: Anthropomorphisation -- the phenomenon whereby non-human entities are ascribed human-like qualities -- has become increasingly salient with the rise of large language model (LLM)-based conversational agents (CAs). Unlike earlier chatbots, LLM-based CAs routinely generate ... | https://arxiv.org/abs/2601.09869 | Academic Papers | svg |
5b160352ab2547231ccba1d86e057c838d23f42cb9ef84786c245fab9ff90a0c | 2026-01-16T00:00:00-05:00 | Epistemology gives a Future to Complementarity in Human-AI Interactions | arXiv:2601.09871v1 Announce Type: new Abstract: Human-AI complementarity is the claim that a human supported by an AI system can outperform either alone in a decision-making process. Since its introduction in the human-AI interaction literature, it has gained traction by generalizing the reliance paradigm and by offeri... | https://arxiv.org/abs/2601.09871 | Academic Papers | svg |
3a5fc49d38798fb44420a4eb6b835e92883a50a6093b6e071acb3f46da243d73 | 2026-01-16T00:00:00-05:00 | Beyond Strict Rules: Assessing the Effectiveness of Large Language Models for Code Smell Detection | arXiv:2601.09873v1 Announce Type: new Abstract: Code smells are symptoms of potential code quality problems that may affect software maintainability, thus increasing development costs and impacting software reliability. Large language models (LLMs) have shown remarkable capabilities for supporting various software engi... | https://arxiv.org/abs/2601.09873 | Academic Papers | svg |
0f1be0a66e15ddd4eb312df7c616a3875b3881189e20dc56458b252791ada22f | 2026-01-16T00:00:00-05:00 | Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL | arXiv:2601.09876v1 Announce Type: new Abstract: Real-world clinical text-to-SQL requires reasoning over heterogeneous EHR tables, temporal windows, and patient-similarity cohorts to produce executable queries. We introduce CLINSQL, a benchmark of 633 expert-annotated tasks on MIMIC-IV v3.1 that demands multi-table join... | https://arxiv.org/abs/2601.09876 | Academic Papers | svg |
a8663734ec6c8650f12fd409ecfec019f74a33f7c79e89e38261dec0d2584f1b | 2026-01-16T00:00:00-05:00 | Who Owns My AI Twin? Data Ownership in a New World of Simulated Identities | arXiv:2601.09877v1 Announce Type: new Abstract: The emergence of AI twins, digital replicas that encapsulate an individual's knowledge, memories, psychological traits, and behavioral patterns, raises novel legal and ethical challenges for data governance and personal identity. Built from personal data, these systems re... | https://arxiv.org/abs/2601.09877 | Academic Papers | svg |
46e2babef61701220b22344c54ecf54be228a55f8c31e33dee641487292d5a62 | 2026-01-16T00:00:00-05:00 | MedVL-SAM2: A unified 3D medical vision-language model for multimodal reasoning and prompt-driven segmentation | arXiv:2601.09879v1 Announce Type: new Abstract: Recent progress in medical vision-language models (VLMs) has achieved strong performance on image-level text-centric tasks such as report generation and visual question answering (VQA). However, achieving fine-grained visual grounding and volumetric spatial reasoning in 3... | https://arxiv.org/abs/2601.09879 | Academic Papers | svg |
c1405a9c91d51438dd33bbd33cfd5a3d5751c0f09c5c4f7930dcc20881463d38 | 2026-01-16T00:00:00-05:00 | Transition Matching Distillation for Fast Video Generation | arXiv:2601.09881v1 Announce Type: new Abstract: Large video diffusion and flow models have achieved remarkable success in high-quality video generation, but their use in real-time interactive applications remains limited due to their inefficient multi-step sampling process. In this work, we present Transition Matching ... | https://arxiv.org/abs/2601.09881 | Academic Papers | svg |
6a19d87901bea13f52854b2bccdd2dcf0c5540ba3d8cbf3df89295ca4fb08652 | 2026-01-16T00:00:00-05:00 | An efficient probabilistic scheme for the exit time probability of $\alpha$-stable L\'evy process | arXiv:2601.09882v1 Announce Type: new Abstract: The {\alpha}-stable L\'evy process, commonly used to describe L\'evy flight, is characterized by discontinuous jumps and is widely used to model anomalous transport phenomena. In this study, we investigate the associated exit problem and propose a method to compute the ex... | https://arxiv.org/abs/2601.09882 | Academic Papers | svg |
2b4da1dee7ff8a95a4a3a048b9f358afd83beed311f328ea0f0f11b3abde739b | 2026-01-16T00:00:00-05:00 | Beyond Rule-Based Workflows: An Information-Flow-Orchestrated Multi-Agents Paradigm via Agent-to-Agent Communication from CORAL | arXiv:2601.09883v1 Announce Type: new Abstract: Most existing Large Language Model (LLM)-based Multi-Agent Systems (MAS) rely on predefined workflows, where human engineers enumerate task states in advance and specify routing rules and contextual injections accordingly. Such workflow-driven designs are essentially rule... | https://arxiv.org/abs/2601.09883 | Academic Papers | svg |
fc622b76ea046afbf04603e7eef47d954789c720332d90642c96a43509eabdf9 | 2026-01-16T00:00:00-05:00 | Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze Surprisal | arXiv:2601.09886v1 Announce Type: new Abstract: How predictable a word is can be quantified in two ways: using human responses to the cloze task or using probabilities from language models (LMs).When used as predictors of processing effort, LM probabilities outperform probabilities derived from cloze data. However, it ... | https://arxiv.org/abs/2601.09886 | Academic Papers | svg |
55a622ea97e477f92b088a5dd3319dccec57bc3e0f6fc849f926a965771c087a | 2026-01-16T00:00:00-05:00 | LAMDA: Aiding Visual Exploration of Atomic Displacements in Molecular Dynamics Simulations | arXiv:2601.09887v1 Announce Type: new Abstract: Contemporary materials science research is heavily conducted in silico, involving massive simulations of the atomic-scale evolution of materials. Cataloging basic patterns in the atomic displacements is key to understanding and predicting the evolution of physical propert... | https://arxiv.org/abs/2601.09887 | Academic Papers | svg |
fa39f4e2b037be201b303652bb8129ca2ae182777d611394bd9ac7020057cae5 | 2026-01-16T00:00:00-05:00 | One-Cold Poisson Channel: A Simple Continuous-Time Channel with Zero Dispersion | arXiv:2601.09894v1 Announce Type: new Abstract: We introduce the one-cold Poisson channel (OCPC), where the transmitter chooses one of several frequency bands to attenuate at a time. In particular, the perfect OCPC, where the number of bands is unlimited, is an extremely simple continuous-time memoryless channel. It ha... | https://arxiv.org/abs/2601.09894 | Academic Papers | svg |
a2cd17ff390532093f2185df987b281b07bfaeacade6e36284852de3757c01d5 | 2026-01-16T00:00:00-05:00 | The Algorithmic Gaze: An Audit and Ethnography of the LAION-Aesthetics Predictor Model | arXiv:2601.09896v1 Announce Type: new Abstract: Visual generative AI models are trained using a one-size-fits-all measure of aesthetic appeal. However, what is deemed "aesthetic" is inextricably linked to personal taste and cultural values, raising the question of whose taste is represented in visual generative AI mode... | https://arxiv.org/abs/2601.09896 | Academic Papers | svg |
f87c9a99ab06ed4da6d7fda403c665bae122ec3ac6bf668416468fdce33638bc | 2026-01-16T00:00:00-05:00 | Cooking Up Politeness in Human-AI Information Seeking Dialogue | arXiv:2601.09898v1 Announce Type: new Abstract: Politeness is a core dimension of human communication, yet its role in human-AI information seeking remains underexplored. We investigate how user politeness behaviour shapes conversational outcomes in a cooking-assistance setting. First, we annotated 30 dialogues, identi... | https://arxiv.org/abs/2601.09898 | Academic Papers | svg |
c8a5b89cf5b7aca87a07a580a6c9b16b4adb15e55203e62a5658de4bbf89deec | 2026-01-16T00:00:00-05:00 | Nonlinear numerical schemes using specular differentiation for initial value problems of first-order ordinary differential equations | arXiv:2601.09900v1 Announce Type: new Abstract: This paper proposes specular differentiation in one-dimensional Euclidean space and provides its fundamental analysis, including quasi-Fermat's theorem and the quasi-Mean Value Theorem. As an application, this paper develops several numerical schemes for solving initial v... | https://arxiv.org/abs/2601.09900 | Academic Papers | svg |
913c858186fe7f404e0938df87983fa977d7920476a3f3958ea9e1eb5790b59d | 2026-01-16T00:00:00-05:00 | A Novel Contrastive Loss for Zero-Day Network Intrusion Detection | arXiv:2601.09902v1 Announce Type: new Abstract: Machine learning has achieved state-of-the-art results in network intrusion detection; however, its performance significantly degrades when confronted by a new attack class -- a zero-day attack. In simple terms, classical machine learning-based approaches are adept at ide... | https://arxiv.org/abs/2601.09902 | Academic Papers | svg |
ee03ce3c4881f096378ded0d4190c0b7080511a65c8b4c479c1305fa09022516 | 2026-01-16T00:00:00-05:00 | Forward-only learning in memristor arrays with month-scale stability | arXiv:2601.09903v1 Announce Type: new Abstract: Turning memristor arrays from efficient inference engines into systems capable of on-chip learning has proved difficult. Weight updates have a high energy cost and cause device wear, analog states drift, and backpropagation requires a backward pass with reversed signal fl... | https://arxiv.org/abs/2601.09903 | Academic Papers | svg |
13f49d5671d8e97c837b32732f3e261703c346f7ba152c80a9e4023904d941d0 | 2026-01-16T00:00:00-05:00 | Self-reflection in Automated Qualitative Coding: Improving Text Annotation through Secondary LLM Critique | arXiv:2601.09905v1 Announce Type: new Abstract: Large language models (LLMs) allow for sophisticated qualitative coding of large datasets, but zero- and few-shot classifiers can produce an intolerable number of errors, even with careful, validated prompting. We present a simple, generalizable two-stage workflow: an LLM... | https://arxiv.org/abs/2601.09905 | Academic Papers | svg |
05d6ecac1c1e9d349e51649428d9f0908aec0d10471df0f1566c2cbfab0f67ce | 2026-01-16T00:00:00-05:00 | Continuum Memory Architectures for Long-Horizon LLM Agents | arXiv:2601.09913v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has become the default strategy for providing large language model (LLM) agents with contextual knowledge. Yet RAG treats memory as a stateless lookup table: information persists indefinitely, retrieval is read-only, and temporal conti... | https://arxiv.org/abs/2601.09913 | Academic Papers | svg |
a3f0eef50bf0f4aa62eaa070cc12f576a7c061d4d3da22038ee2a989c3280326 | 2026-01-16T00:00:00-05:00 | Learning-Augmented Perfectly Secure Collaborative Matrix Multiplication | arXiv:2601.09916v1 Announce Type: new Abstract: This paper presents a perfectly secure matrix multiplication (PSMM) protocol for multiparty computation (MPC) of $\mathrm{A}^{\top}\mathrm{B}$ over finite fields. The proposed scheme guarantees correctness and information-theoretic privacy against threshold-bounded, semi-... | https://arxiv.org/abs/2601.09916 | Academic Papers | svg |
82c331a67cd3ce305776c62597fbabf3830be20e8e723663ef7d36cfb67bf21f | 2026-01-16T00:00:00-05:00 | Collision Avoidance for Non-Cooperative Multi-Swarm Coverage Control with Bounded Disturbance Measurements | arXiv:2601.09917v1 Announce Type: new Abstract: This paper proposes a new algorithm for collision-free coverage control of multiple non-cooperating swarms in the presence of bounded disturbances. A new methodology is introduced that accounts for uncertainties in disturbance measurements. The proposed methodology is use... | https://arxiv.org/abs/2601.09917 | Academic Papers | svg |
7ea04a37b5153b70465000b0c4cd5165f41470687125382205b036b42afbffca | 2026-01-16T00:00:00-05:00 | SyncTwin: Fast Digital Twin Construction and Synchronization for Safe Robotic Grasping | arXiv:2601.09920v1 Announce Type: new Abstract: Accurate and safe grasping under dynamic and visually occluded conditions remains a core challenge in real-world robotic manipulation. We present SyncTwin, a digital twin framework that unifies fast 3D scene reconstruction and real-to-sim synchronization for robust and sa... | https://arxiv.org/abs/2601.09920 | Academic Papers | svg |
abf8daf5c506016f0dbd70684431168f46669e42ac1891ac16ccc75c2292ac09 | 2026-01-16T00:00:00-05:00 | CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents | arXiv:2601.09923v1 Announce Type: new Abstract: AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss. The only known robust defense is architectural isolation that strictly separates trusted task planning from untrusted environ... | https://arxiv.org/abs/2601.09923 | Academic Papers | svg |
02ee6ad84c906ea9deaf33c7cbc47f3fbdf36525f901fb10a4b701c2c5bdcb16 | 2026-01-16T00:00:00-05:00 | The PROPER Approach to Proactivity: Benchmarking and Advancing Knowledge Gap Navigation | arXiv:2601.09926v1 Announce Type: new Abstract: Most language-based assistants follow a reactive ask-and-respond paradigm, requiring users to explicitly state their needs. As a result, relevant but unexpressed needs often go unmet. Existing proactive agents attempt to address this gap either by eliciting further clarif... | https://arxiv.org/abs/2601.09926 | Academic Papers | svg |
99f9a7aeb404083c38749c500019a369cf3ca4f6714ff33959671f9529e5737f | 2026-01-16T00:00:00-05:00 | In-Browser Agents for Search Assistance | arXiv:2601.09928v1 Announce Type: new Abstract: A fundamental tension exists between the demand for sophisticated AI assistance in web search and the need for user data privacy. Current centralized models require users to transmit sensitive browsing data to external services, which limits user control. In this paper, w... | https://arxiv.org/abs/2601.09928 | Academic Papers | svg |
4441fcd5926601af4ef116cbcdf5a9eff4cfbce5a47c18eb4d1439b6a6fd4d48 | 2026-01-16T00:00:00-05:00 | Hallucination Detection and Mitigation in Large Language Models | arXiv:2601.09929v1 Announce Type: new Abstract: Large Language Models (LLMs) and Large Reasoning Models (LRMs) offer transformative potential for high-stakes domains like finance and law, but their tendency to hallucinate, generating factually incorrect or unsupported content, poses a critical reliability risk. This pa... | https://arxiv.org/abs/2601.09929 | Academic Papers | svg |
12bd65a3fccfff159a4e565acba73be1a407e4d52242c0325bdc2c48710c4d23 | 2026-01-16T00:00:00-05:00 | Diffusion-based Frameworks for Unsupervised Speech Enhancement | arXiv:2601.09931v1 Announce Type: new Abstract: This paper addresses $\textit{unsupervised}$ diffusion-based single-channel speech enhancement (SE). Prior work in this direction combines a score-based diffusion model trained on clean speech with a Gaussian noise model whose covariance is structured by non-negative matr... | https://arxiv.org/abs/2601.09931 | Academic Papers | svg |
86c9d8cae3b1e1df0ad1ea51005cfcbcb5d7202be9805c9d2178ad590cc74606 | 2026-01-16T00:00:00-05:00 | Malware Classification using Diluted Convolutional Neural Network with Fast Gradient Sign Method | arXiv:2601.09933v1 Announce Type: new Abstract: Android malware has become an increasingly critical threat to organizations, society and individuals, posing significant risks to privacy, data security and infrastructure. As malware continues to evolve in terms of complexity and sophistication, the mitigation and detect... | https://arxiv.org/abs/2601.09933 | Academic Papers | svg |
1068275bb4a46829e261f54db71dc74b7f41de7232bf1164c74f0c6c65d0c03e | 2026-01-16T00:00:00-05:00 | From SERPs to Agents: A Platform for Comparative Studies of Information Interaction | arXiv:2601.09937v1 Announce Type: new Abstract: The diversification of information access systems, from RAG to autonomous agents, creates a critical need for comparative user studies. However, the technical overhead to deploy and manage these distinct systems is a major barrier. We present UXLab, an open-source system ... | https://arxiv.org/abs/2601.09937 | Academic Papers | svg |
2087118eca7d06bc3d837e3fddd046ccc5ed07a26f78cb33e049f9c6e3899240 | 2026-01-16T00:00:00-05:00 | How Diplomacy Reshapes Online Discourse:Asymmetric Persistence in Online Framing of North Korea | arXiv:2601.09942v1 Announce Type: new Abstract: Public opinion toward foreign adversaries shapes and constrains diplomatic options. Prior research has largely relied on sentiment analysis and survey based measures, providing limited insight into how sustained narrative changes (beyond transient emotional reactions) mig... | https://arxiv.org/abs/2601.09942 | Academic Papers | svg |
1b41e074afda3bab3680086f3ed531e1ed049fbbcc1d142ba073699f0823daf7 | 2026-01-16T00:00:00-05:00 | Modeling conflicting incentives in engineering senior capstone projects: A multi-player game theory approach | arXiv:2601.09944v1 Announce Type: new Abstract: University engineering capstone projects involve sustained interaction among students, faculty, and industry sponsors whose objectives are only partially aligned. While capstones are widely used in engineering education, existing analyses typically treat stakeholder behav... | https://arxiv.org/abs/2601.09944 | Academic Papers | svg |
e46bbdc347f453f38f685c054ddaec257b49b90f2ca09ca51c23c91acdedc04b | 2026-01-16T00:00:00-05:00 | Interpolation-Based Optimization for Enforcing lp-Norm Metric Differential Privacy in Continuous and Fine-Grained Domains | arXiv:2601.09946v1 Announce Type: new Abstract: Metric Differential Privacy (mDP) generalizes Local Differential Privacy (LDP) by adapting privacy guarantees based on pairwise distances, enabling context-aware protection and improved utility. While existing optimization-based methods reduce utility loss effectively in ... | https://arxiv.org/abs/2601.09946 | Academic Papers | svg |
5cd0a85149204d352d7ec1fa23748c659f3e2d578e0123cb02da14fb51853cfd | 2026-01-16T00:00:00-05:00 | Reconstructing Reed-Solomon Codes from Multiple Noisy Channel Outputs | arXiv:2601.09947v1 Announce Type: new Abstract: The sequence reconstruction problem, introduced by Levenshtein in 2001, considers a communication setting in which a sender transmits a codeword and the receiver observes K independent noisy versions of this codeword. In this work, we study the problem of efficient recons... | https://arxiv.org/abs/2601.09947 | Academic Papers | svg |
3e10b4ec45a74f12d0d618acc330acd77ab7098fdc771606510abfe8c9b03a08 | 2026-01-16T00:00:00-05:00 | Kinematic Tokenization: Optimization-Based Continuous-Time Tokens for Learnable Decision Policies in Noisy Time Series | arXiv:2601.09949v1 Announce Type: new Abstract: Transformers are designed for discrete tokens, yet many real-world signals are continuous processes observed through noisy sampling. Discrete tokenizations (raw values, patches, finite differences) can be brittle in low signal-to-noise regimes, especially when downstream ... | https://arxiv.org/abs/2601.09949 | Academic Papers | svg |
566425418285e1e255c89efc5656a20aaef274735ce1627cae7a0b106d8db909 | 2026-01-16T00:00:00-05:00 | OT-Drive: Out-of-Distribution Off-Road Traversable Area Segmentation via Optimal Transport | arXiv:2601.09952v1 Announce Type: new Abstract: Reliable traversable area segmentation in unstructured environments is critical for planning and decision-making in autonomous driving. However, existing data-driven approaches often suffer from degraded segmentation performance in out-of-distribution (OOD) scenarios, con... | https://arxiv.org/abs/2601.09952 | Academic Papers | svg |
6f52401f12be27fc1060a6d3f9a895b60f76ea76d395d80daabbb50ce13a2bf0 | 2026-01-16T00:00:00-05:00 | Take Out Your Calculators: Estimating the Real Difficulty of Question Items with LLM Student Simulations | arXiv:2601.09953v1 Announce Type: new Abstract: Standardized math assessments require expensive human pilot studies to establish the difficulty of test items. We investigate the predictive value of open-source large language models (LLMs) for evaluating the difficulty of multiple-choice math questions for real-world st... | https://arxiv.org/abs/2601.09953 | Academic Papers | svg |
ca0702936f343c0925edc4285bc7c2c4ea82b3c0072662cf1b5ae552f0b2e208 | 2026-01-16T00:00:00-05:00 | The Spatial Blindspot of Vision-Language Models | arXiv:2601.09954v1 Announce Type: new Abstract: Vision-language models (VLMs) have advanced rapidly, but their ability to capture spatial relationships remains a blindspot. Current VLMs are typically built with contrastive language-image pretraining (CLIP) style image encoders. The training recipe often flattens images... | https://arxiv.org/abs/2601.09954 | Academic Papers | svg |
aebb3cf43cb70ee9f5afd4004c6a76985584a0affc76c38c2e88a984dbd91b90 | 2026-01-16T00:00:00-05:00 | Private Information Retrieval for Graph-based Replication with Minimal Subpacketization | arXiv:2601.09957v1 Announce Type: new Abstract: We design new minimal-subpacketization schemes for information-theoretic private information retrieval on graph-based replicated databases. In graph-based replication, the system consists of $K$ files replicated across $N$ servers according to a graph with $N$ vertices an... | https://arxiv.org/abs/2601.09957 | Academic Papers | svg |
67bf0872054bef72d7102f86df29634220f2555fb2bf871f93df2bb26d77b8f2 | 2026-01-16T00:00:00-05:00 | On the Leaky Private Information Retrieval with Side Information | arXiv:2601.09960v1 Announce Type: new Abstract: This paper investigates the problem of leaky-private Private Information Retrieval with Side Information (L-PIR-SI), which relaxes the requirement of perfect privacy to achieve improved communication efficiency in the presence of side information. While the capacities of ... | https://arxiv.org/abs/2601.09960 | Academic Papers | svg |
e6b156b7153836598dfd68ca373f841cca2a172654245f5024745a82cd7ad3bf | 2026-01-16T00:00:00-05:00 | A Control Theoretic Approach to Decentralized AI Economy Stabilization via Dynamic Buyback-and-Burn Mechanisms | arXiv:2601.09961v1 Announce Type: new Abstract: The democratization of artificial intelligence through decentralized networks represents a paradigm shift in computational provisioning, yet the long-term viability of these ecosystems is critically endangered by the extreme volatility of their native economic layers. Cur... | https://arxiv.org/abs/2601.09961 | Academic Papers | svg |
3c94a78bda34b9e8510579973230a4c5611a5409f9b1af76e8d0e2d44d3e8a40 | 2026-01-16T00:00:00-05:00 | A Sustainable AI Economy Needs Data Deals That Work for Generators | arXiv:2601.09966v1 Announce Type: new Abstract: We argue that the machine learning value chain is structurally unsustainable due to an economic data processing inequality: each state in the data cycle from inputs to model weights to synthetic outputs refines technical signal but strips economic equity from data generat... | https://arxiv.org/abs/2601.09966 | Academic Papers | svg |
4e57e559b62d5e0b721003b4b3a2349f5d60326c3cf2568f758d0fe7e9c561e4 | 2026-01-16T00:00:00-05:00 | An Exploratory Study to Repurpose LLMs to a Unified Architecture for Time Series Classification | arXiv:2601.09971v1 Announce Type: new Abstract: Time series classification (TSC) is a core machine learning problem with broad applications. Recently there has been growing interest in repurposing large language models (LLMs) for TSC, motivated by their strong reasoning and generalization ability. Prior work has primar... | https://arxiv.org/abs/2601.09971 | Academic Papers | svg |
4f6636af1142245201ffb77f001cdcb6fff08a6285bee7da1502a6a0919de381 | 2026-01-16T00:00:00-05:00 | Chinese Labor Law Large Language Model Benchmark | arXiv:2601.09972v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have led to substantial progress in domain-specific applications, particularly within the legal domain. However, general-purpose models such as GPT-4 often struggle with specialized subdomains that require precise legal know... | https://arxiv.org/abs/2601.09972 | Academic Papers | svg |
9248e1d3ea6041afa933c7785300b671ebcb3b644cf3ae5f5dd4e8563c45fac1 | 2026-01-16T00:00:00-05:00 | Correspondences in computational and dynamical complexity II: forcing complex reductions | arXiv:2601.09973v1 Announce Type: new Abstract: An algebraic telic problem is a decision problem in $\textsf{NP}_\mathbb{R}$ formalizing finite-time reachability questions for one-dimensional dynamical systems. We prove that the existence of "natural" mapping reductions between algebraic telic problems coming from dist... | https://arxiv.org/abs/2601.09973 | Academic Papers | svg |
a1be380d65d6d4b88a19d075559843a42cea539a47685e3dab8dfefa1042443b | 2026-01-16T00:00:00-05:00 | SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation | arXiv:2601.09974v1 Announce Type: new Abstract: Personalizing Large Language Models typically relies on static retrieval or one-time adaptation, assuming user preferences remain invariant over time. However, real-world interactions are dynamic, where user interests continuously evolve, posing a challenge for models to ... | https://arxiv.org/abs/2601.09974 | Academic Papers | svg |
294fd19dc4b4e8c3779477c1ca047bb1025e62288de5448b7def2f89c18b0d90 | 2026-01-16T00:00:00-05:00 | Federated Unlearning in Edge Networks: A Survey of Fundamentals, Challenges, Practical Applications and Future Directions | arXiv:2601.09978v1 Announce Type: new Abstract: The proliferation of connected devices and privacy-sensitive applications has accelerated the adoption of Federated Learning (FL), a decentralized paradigm that enables collaborative model training without sharing raw data. While FL addresses data locality and privacy con... | https://arxiv.org/abs/2601.09978 | Academic Papers | svg |
2489c4fb6b01fadba59af213c56f89bae90e0661430dfb61c02d7174b2ebff8b | 2026-01-16T00:00:00-05:00 | In-Context Operator Learning on the Space of Probability Measures | arXiv:2601.09979v1 Announce Type: new Abstract: We introduce \emph{in-context operator learning on probability measure spaces} for optimal transport (OT). The goal is to learn a single solution operator that maps a pair of distributions to the OT map, using only few-shot samples from each distribution as a prompt and \... | https://arxiv.org/abs/2601.09979 | Academic Papers | svg |
eb418a1df0b7a8d3afaa33e437f50e77e966221353b38e9a29267376d5e5a399 | 2026-01-16T00:00:00-05:00 | DR$^2$Seg: Decomposed Two-Stage Rollouts for Efficient Reasoning Segmentation in Multimodal Large Language Models | arXiv:2601.09981v1 Announce Type: new Abstract: Reasoning segmentation is an emerging vision-language task that requires reasoning over intricate text queries to precisely segment objects. However, existing methods typically suffer from overthinking, generating verbose reasoning chains that interfere with object locali... | https://arxiv.org/abs/2601.09981 | Academic Papers | svg |
bea87e0b149dc16f30c51f45cba6417caa397dff71f3702e814a06aefa1627fc | 2026-01-16T00:00:00-05:00 | Context Volume Drives Performance: Tackling Domain Shift in Extremely Low-Resource Translation via RAG | arXiv:2601.09982v1 Announce Type: new Abstract: Neural Machine Translation (NMT) models for low-resource languages suffer significant performance degradation under domain shift. We quantify this challenge using Dhao, an indigenous language of Eastern Indonesia with no digital footprint beyond the New Testament (NT). Wh... | https://arxiv.org/abs/2601.09982 | Academic Papers | svg |
1e423de0c399cf33d635d33c814af795981d09cbd285beb2f1f58c941667dbb3 | 2026-01-16T00:00:00-05:00 | FaTRQ: Tiered Residual Quantization for LLM Vector Search in Far-Memory-Aware ANNS Systems | arXiv:2601.09985v1 Announce Type: new Abstract: Approximate Nearest-Neighbor Search (ANNS) is a key technique in retrieval-augmented generation (RAG), enabling rapid identification of the most relevant high-dimensional embeddings from massive vector databases. Modern ANNS engines accelerate this process using prebuilt ... | https://arxiv.org/abs/2601.09985 | Academic Papers | svg |
e593dc73001c519b7c676e8ab0cd9076bcf33454b75024ae4c1760fbf36787d2 | 2026-01-16T00:00:00-05:00 | Outrunning Big KATs: Efficient Decision Procedures for Variants of GKAT | arXiv:2601.09986v1 Announce Type: new Abstract: This paper presents several efficient decision procedures for trace equivalence of GKAT automata, which make use of on-the-fly symbolic techniques via SAT solvers. To demonstrate applicability of our algorithms, we designed symbolic derivatives for CF-GKAT, a practical sy... | https://arxiv.org/abs/2601.09986 | Academic Papers | svg |
f919228e53a5adff3d0edf0adec9b0da6cf5cec9f86c00007fc1260eb275061c | 2026-01-16T00:00:00-05:00 | In-the-Wild Compliant Manipulation with UMI-FT | arXiv:2601.09988v1 Announce Type: new Abstract: Many manipulation tasks require careful force modulation. With insufficient force the task may fail, while excessive force could cause damage. The high cost, bulky size and fragility of commercial force/torque (F/T) sensors have limited large-scale, force-aware policy lea... | https://arxiv.org/abs/2601.09988 | Academic Papers | svg |
c9f215b9e3541012419bc5743dce253b8eaff47528d6cf31f685d02e92e9c844 | 2026-01-16T00:00:00-05:00 | Brief but Impactful: How Human Tutoring Interactions Shape Engagement in Online Learning | arXiv:2601.09994v1 Announce Type: new Abstract: Learning analytics can guide human tutors to efficiently address motivational barriers to learning that AI systems struggle to support. Students become more engaged when they receive human attention. However, what occurs during short interventions, and when are they most ... | https://arxiv.org/abs/2601.09994 | Academic Papers | svg |
c6041a9a1b3c1f43e394cebf54eb76dd4ac7a1e4b4b554dd5106bc5dcaf537fa | 2026-01-16T00:00:00-05:00 | Extremum Seeking Nonovershooting Control of Strict-Feedback Systems Under Unknown Control Direction | arXiv:2601.09998v1 Announce Type: new Abstract: This paper addresses the nonovershooting control problem for strict-feedback nonlinear systems with unknown control direction. We propose a method that integrates extremum seeking with Lie bracket-based design to achieve approximately nonovershooting tracking. The approac... | https://arxiv.org/abs/2601.09998 | Academic Papers | svg |
d55930376c3fd73211ed34334cde98b38b4d17a2b2cdcc79a107a33766986e98 | 2026-01-16T00:00:00-05:00 | EditEmoTalk: Controllable Speech-Driven 3D Facial Animation with Continuous Expression Editing | arXiv:2601.10000v1 Announce Type: new Abstract: Speech-driven 3D facial animation aims to generate realistic and expressive facial motions directly from audio. While recent methods achieve high-quality lip synchronization, they often rely on discrete emotion categories, limiting continuous and fine-grained emotional co... | https://arxiv.org/abs/2601.10000 | Academic Papers | svg |
d2ef92cf096699f3e49c1c17f8277104de651d24f29bb49d4b4e79c40b89fd9a | 2026-01-16T00:00:00-05:00 | DW-DGAT: Dynamically Weighted Dual Graph Attention Network for Neurodegenerative Disease Diagnosis | arXiv:2601.10001v1 Announce Type: new Abstract: Parkinson's disease (PD) and Alzheimer's disease (AD) are the two most prevalent and incurable neurodegenerative diseases (NDs) worldwide, for which early diagnosis is critical to delay their progression. However, the high dimensionality of multi-metric data with diverse ... | https://arxiv.org/abs/2601.10001 | Academic Papers | svg |
94e979972cef107b171547cbadfa4d24a8e26d2871e825de2722271c1bc1a0be | 2026-01-16T00:00:00-05:00 | SocraticKG: Knowledge Graph Construction via QA-Driven Fact Extraction | arXiv:2601.10003v1 Announce Type: new Abstract: Constructing Knowledge Graphs (KGs) from unstructured text provides a structured framework for knowledge representation and reasoning, yet current LLM-based approaches struggle with a fundamental trade-off: factual coverage often leads to relational fragmentation, while p... | https://arxiv.org/abs/2601.10003 | Academic Papers | svg |
7a8c5cb605c3af065ff0c74e143d1ffc215cc10c9e50758b0396c60583b97685 | 2026-01-16T00:00:00-05:00 | SoK: Privacy-aware LLM in Healthcare: Threat Model, Privacy Techniques, Challenges and Recommendations | arXiv:2601.10004v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly adopted in healthcare to support clinical decision-making, summarize electronic health records (EHRs), and enhance patient care. However, this integration introduces significant privacy and security challenges, driven by the s... | https://arxiv.org/abs/2601.10004 | Academic Papers | svg |
62c4ff4db73c735d67c5ea0905fdff6c91ea7312299e9480eea7b60033d9dee2 | 2026-01-16T00:00:00-05:00 | Continuous-Depth Transformers with Learned Control Dynamics | arXiv:2601.10007v1 Announce Type: new Abstract: We present a hybrid transformer architecture that replaces discrete middle layers with a continuous-depth Neural Ordinary Differential Equation (ODE) block, enabling inference-time control over generation attributes via a learned steering signal. Unlike standard transform... | https://arxiv.org/abs/2601.10007 | Academic Papers | svg |
f9f8f256e8cc858e95f0908c98e42da4a123bbb111bfff49ea76d1928ebe1f24 | 2026-01-16T00:00:00-05:00 | The "I" in FAIR: Translating from Interoperability in Principle to Interoperation in Practice | arXiv:2601.10008v1 Announce Type: new Abstract: The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles [1] promote the interoperability of scientific data by encouraging the use of persistent identifiers, standardized vocabularies, and formal metadata structures. Many resources are created using v... | https://arxiv.org/abs/2601.10008 | Academic Papers | svg |
e41dc84b9a2ffdbd75dd8b8ac8576232bd624cbe27e4266a6a71c2520b0a1e38 | 2026-01-16T00:00:00-05:00 | VERHallu: Evaluating and Mitigating Event Relation Hallucination in Video Large Language Models | arXiv:2601.10010v1 Announce Type: new Abstract: Video Large Language Models (VideoLLMs) exhibit various types of hallucinations. Existing research has primarily focused on hallucinations involving the presence of events, objects, and scenes in videos, while largely neglecting event relation hallucination. In this paper... | https://arxiv.org/abs/2601.10010 | Academic Papers | svg |
21202ae12770302031d1390590c0125bdfb395819cb366c7359c1b79944e88e1 | 2026-01-16T00:00:00-05:00 | Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL | arXiv:2601.10011v1 Announce Type: new Abstract: Existing NL2SQL systems face two critical limitations: (1) they rely on in-context learning with only correct examples, overlooking the rich signal in historical error-fix pairs that could guide more robust self-correction; and (2) test-time scaling approaches often decom... | https://arxiv.org/abs/2601.10011 | Academic Papers | svg |
941c1f85460ffe7e4696a135aa1f5948b0e3e63f6b69bf734723d6c740c3d040 | 2026-01-16T00:00:00-05:00 | PID-Guided Partial Alignment for Multimodal Decentralized Federated Learning | arXiv:2601.10012v1 Announce Type: new Abstract: Multimodal decentralized federated learning (DFL) is challenging because agents differ in available modalities and model architectures, yet must collaborate over peer-to-peer (P2P) networks without a central coordinator. Standard multimodal pipelines learn a single shared... | https://arxiv.org/abs/2601.10012 | Academic Papers | svg |
9462c0f0084355ae4c153bad30c42c62d670a1c9935cfbec2bf08ba651faa804 | 2026-01-16T00:00:00-05:00 | CAFEDistill: Learning Personalized and Dynamic Models through Federated Early-Exit Network Distillation | arXiv:2601.10015v1 Announce Type: new Abstract: Personalized Federated Learning (PFL) enables collaboratively model training on decentralized, heterogeneous data while tailoring them to each client's unique distribution. However, existing PFL methods produce static models with a fixed tradeoff between accuracy and effi... | https://arxiv.org/abs/2601.10015 | Academic Papers | svg |
dfd23f5436f7883d5a1e5e16c79724a5943894a2ff7d7c8f9eb1c4d5fc572472 | 2026-01-16T00:00:00-05:00 | Empowering Older Adults in Digital Technology Use with Foundation Models | arXiv:2601.10018v1 Announce Type: new Abstract: While high-quality technology support can assist older adults in using digital applications, many struggle to articulate their issues due to unfamiliarity with technical terminology and age-related cognitive changes. This study examines these communication challenges and ... | https://arxiv.org/abs/2601.10018 | Academic Papers | svg |
bc28970aecadbfaf0013b63c60bd07adfba6d9f2df6f2f465d9c5ce3ffccd97c | 2026-01-16T00:00:00-05:00 | Time Aggregation Features for XGBoost Models | arXiv:2601.10019v1 Announce Type: new Abstract: This paper studies time aggregation features for XGBoost models in click-through rate prediction. The setting is the Avazu click-through rate prediction dataset with strict out-of-time splits and a no-lookahead feature constraint. Features for hour H use only impressions ... | https://arxiv.org/abs/2601.10019 | Academic Papers | svg |
09e34950c09fbf03f93b914035135944df173525a7cb2986b883f79de9f54f22 | 2026-01-16T00:00:00-05:00 | EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records | arXiv:2601.10020v1 Announce Type: new Abstract: Clinical decision-making increasingly relies on timely and context-aware access to patient information within Electronic Health Records (EHRs), yet most existing natural language question-answering (QA) systems are evaluated solely on benchmark datasets, limiting their pr... | https://arxiv.org/abs/2601.10020 | Academic Papers | svg |
d81e593eddfa2abeb30a6948dc8f0a176de4fb6fffed8e7953e694c8a5ca0c14 | 2026-01-16T00:00:00-05:00 | BPE: Behavioral Profiling Ensemble | arXiv:2601.10024v1 Announce Type: new Abstract: Ensemble learning is widely recognized as a pivotal strategy for pushing the boundaries of predictive performance. Traditional static ensemble methods, such as Stacking, typically assign weights by treating each base learner as a holistic entity, thereby overlooking the f... | https://arxiv.org/abs/2601.10024 | Academic Papers | svg |
f4b8910a218381c0cdfc058173be3ab47a7091b31dd113805314df1c1cf4f022 | 2026-01-16T00:00:00-05:00 | Structured Personality Control and Adaptation for LLM Agents | arXiv:2601.10025v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI), from personalized assistants to social simulations. Beyond language competence, researchers are exploring whether LLMs can exhibit human-like characteristics that influence engagement,... | https://arxiv.org/abs/2601.10025 | Academic Papers | svg |
2e67d17d32e58308e705a2c0920c432f550b32ddb498d9ba9e582ee08bd00518 | 2026-01-16T00:00:00-05:00 | STCRank: Spatio-temporal Collaborative Ranking for Interactive Recommender System at Kuaishou E-shop | arXiv:2601.10027v1 Announce Type: new Abstract: As a popular e-commerce platform, Kuaishou E-shop provides precise personalized product recommendations to tens of millions of users every day. To better respond real-time user feedback, we have deployed an interactive recommender system (IRS) alongside our core homepage ... | https://arxiv.org/abs/2601.10027 | Academic Papers | svg |
8946efcc8c5c770e3935da3ca042bff595caa6b88ba7b1990d24191895235b9a | 2026-01-16T00:00:00-05:00 | Fundamental Limits of Coded Polynomial Aggregation | arXiv:2601.10028v1 Announce Type: new Abstract: Coded polynomial aggregation (CPA) enables the master to directly recover a weighted aggregation of polynomial evaluations without individually decoding each term, thereby reducing the number of required worker responses. In this paper, we extend CPA to straggler-aware di... | https://arxiv.org/abs/2601.10028 | Academic Papers | svg |
afcf6ddbe8a447452281ef1fd3971cc05056311eddf3c1b3d0f5a9e5cf6afdba | 2026-01-16T00:00:00-05:00 | PaperScout: An Autonomous Agent for Academic Paper Search with Process-Aware Sequence-Level Policy Optimization | arXiv:2601.10029v1 Announce Type: new Abstract: Academic paper search is a fundamental task in scientific research, yet most existing approaches rely on rigid, predefined workflows that struggle with complex, conditional queries. To address this limitation, we propose PaperScout, an autonomous agent that reformulates p... | https://arxiv.org/abs/2601.10029 | Academic Papers | svg |
4d0532ce7c710732f39b699958630351b57c8cefdd549191b3c21bd32b2794a3 | 2026-01-16T00:00:00-05:00 | FilDeep: Learning Large Deformations of Elastic-Plastic Solids with Multi-Fidelity Data | arXiv:2601.10031v1 Announce Type: new Abstract: The scientific computation of large deformations in elastic-plastic solids is crucial in various manufacturing applications. Traditional numerical methods exhibit several inherent limitations, prompting Deep Learning (DL) as a promising alternative. The effectiveness of c... | https://arxiv.org/abs/2601.10031 | Academic Papers | svg |
07a2d6d9486be8823057b799fcee529f5239cde6fd32c393b049f6bb5981840d | 2026-01-16T00:00:00-05:00 | EmplifAI: a Fine-grained Dataset for Japanese Empathetic Medical Dialogues in 28 Emotion Labels | arXiv:2601.10033v1 Announce Type: new Abstract: This paper introduces EmplifAI, a Japanese empathetic dialogue dataset designed to support patients coping with chronic medical conditions. They often experience a wide range of positive and negative emotions (e.g., hope and despair) that shift across different stages of ... | https://arxiv.org/abs/2601.10033 | Academic Papers | svg |
2747478941273fb6cba884eb958b3988be33351513415aa56d0858793611fb31 | 2026-01-16T00:00:00-05:00 | A Compute and Communication Runtime Model for Loihi 2 | arXiv:2601.10035v1 Announce Type: new Abstract: Neuromorphic computers hold the potential to vastly improve the speed and efficiency of a wide range of computational kernels with their asynchronous, compute-memory co-located, spatially distributed, and scalable nature. However, performance models that are simple yet su... | https://arxiv.org/abs/2601.10035 | Academic Papers | svg |
06a990e023ddba9f62fbdb9b57af5031d3de8adc7932c091054ef4c399cc335b | 2026-01-16T00:00:00-05:00 | Resistive Memory based Efficient Machine Unlearning and Continual Learning | arXiv:2601.10037v1 Announce Type: new Abstract: Resistive memory (RM) based neuromorphic systems can emulate synaptic plasticity and thus support continual learning, but they generally lack biologically inspired mechanisms for active forgetting, which are critical for meeting modern data privacy requirements. Algorithm... | https://arxiv.org/abs/2601.10037 | Academic Papers | svg |
c4af46b3c87172160466863d5a43923f448dc80fc9a3448c75ae822c099a58b3 | 2026-01-16T00:00:00-05:00 | Emergency Department Patient Flow Optimization with an Alternative Care Threshold Policy | arXiv:2601.10041v1 Announce Type: new Abstract: Emergency department (ED) overcrowding and patient boarding represent critical systemic challenges that compromise care quality. We propose a threshold-based admission policy that redirects non-urgent patients to alternative care pathways, such as telemedicine, during pea... | https://arxiv.org/abs/2601.10041 | Academic Papers | svg |
ffcdfa0ef68af7e9bf1e30b3cf86dff523da58aa95843b35692d149aa45d5090 | 2026-01-16T00:00:00-05:00 | Event-Driven Deep RL Dispatcher for Post-Storm Distribution System Restoration | arXiv:2601.10044v1 Announce Type: new Abstract: Natural hazards such as hurricanes and floods damage power grid equipment, forcing operators to replan restoration repeatedly as new information becomes available. This paper develops a deep reinforcement learning (DRL) dispatcher that serves as a real-time decision engin... | https://arxiv.org/abs/2601.10044 | Academic Papers | svg |
11200950836c8572c958bec531c2f4e39f67a843d50b23d8b0eb8ef4cd382d53 | 2026-01-16T00:00:00-05:00 | Privacy Enhanced PEFT: Tensor Train Decomposition Improves Privacy Utility Tradeoffs under DP-SGD | arXiv:2601.10045v1 Announce Type: new Abstract: Fine-tuning large language models on sensitive data poses significant privacy risks, as membership inference attacks can reveal whether individual records were used during training. While Differential Privacy (DP) provides formal protection, applying DP to conventional Pa... | https://arxiv.org/abs/2601.10045 | Academic Papers | svg |
e644fa8978cad103369a8787128319076cfd75a6d3b64abcf40501ca028a5f8a | 2026-01-16T00:00:00-05:00 | Optimal Proximity Gap for Folded Reed--Solomon Codes via Subspace Designs | arXiv:2601.10047v1 Announce Type: new Abstract: A collection of sets satisfies a $(\delta,\varepsilon)$-proximity gap with respect to some property if for every set in the collection, either (i) all members of the set are $\delta$-close to the property in (relative) Hamming distance, or (ii) only a small $\varepsilon$-... | https://arxiv.org/abs/2601.10047 | Academic Papers | svg |
ecdf8b710105f610f9f8ddc0939db58b42685de55222f8706fd6312a2de2b912 | 2026-01-16T00:00:00-05:00 | Disentangled Concept Representation for Text-to-image Person Re-identification | arXiv:2601.10053v1 Announce Type: new Abstract: Text-to-image person re-identification (TIReID) aims to retrieve person images from a large gallery given free-form textual descriptions. TIReID is challenging due to the substantial modality gap between visual appearances and textual expressions, as well as the need to m... | https://arxiv.org/abs/2601.10053 | Academic Papers | svg |
5c51d4f325d81a30635bd2f1c95e5a134b699bf7b265090a5323e70c6f2dee39 | 2026-01-16T00:00:00-05:00 | UEOF: A Benchmark Dataset for Underwater Event-Based Optical Flow | arXiv:2601.10054v1 Announce Type: new Abstract: Underwater imaging is fundamentally challenging due to wavelength-dependent light attenuation, strong scattering from suspended particles, turbidity-induced blur, and non-uniform illumination. These effects impair standard cameras and make ground-truth motion nearly impos... | https://arxiv.org/abs/2601.10054 | Academic Papers | svg |
63c4e11a212127eaa8af39514cf70c817b1ae3e9de757fd8791379aa235f37ba | 2026-01-16T00:00:00-05:00 | An Efficient Constant-Coefficient MSAV Scheme for Computing Vesicle Growth and Shrinkage | arXiv:2601.10057v1 Announce Type: new Abstract: We present a fast, unconditionally energy-stable numerical scheme for simulating vesicle deformation under osmotic pressure using a phase-field approach. The model couples an Allen-Cahn equation for the biomembrane interface with a variable-mobility Cahn-Hilliard equation... | https://arxiv.org/abs/2601.10057 | Academic Papers | svg |
66973904e4537e90c9affef6c5fc78b4037945b357a868889979264809bd89f7 | 2026-01-16T00:00:00-05:00 | Unlabeled Data Can Provably Enhance In-Context Learning of Transformers | arXiv:2601.10058v1 Announce Type: new Abstract: Large language models (LLMs) exhibit impressive in-context learning (ICL) capabilities, yet the quality of their predictions is fundamentally limited by the few costly labeled demonstrations that can fit into a prompt. Meanwhile, there exist vast and continuously growing ... | https://arxiv.org/abs/2601.10058 | Academic Papers | svg |
e6d522fbeb688623f4711553880b149a2a844ccd83f4eaab9f5ae33a780e56bd | 2026-01-16T00:00:00-05:00 | CoF-T2I: Video Models as Pure Visual Reasoners for Text-to-Image Generation | arXiv:2601.10061v1 Announce Type: new Abstract: Recent video generation models have revealed the emergence of Chain-of-Frame (CoF) reasoning, enabling frame-by-frame visual inference. With this capability, video models have been successfully applied to various visual tasks (e.g., maze solving, visual puzzles). However,... | https://arxiv.org/abs/2601.10061 | Academic Papers | svg |
ddc73eccf35ecb3b3468742e78a3b19ffe245844a4b8bcdad221aad8f26ca145 | 2026-01-16T00:00:00-05:00 | Long-Chain Reasoning Distillation via Adaptive Prefix Alignment | arXiv:2601.10064v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, particularly in solving complex mathematical problems. Recent studies show that distilling long reasoning trajectories can effectively enhance the reasoning performance of small-scale studen... | https://arxiv.org/abs/2601.10064 | Academic Papers | svg |
d7c60296fc3d194395262982440e5a7ff04e33ba0cd3490e2f8f5707d7aeb79f | 2026-01-16T00:00:00-05:00 | Efficient Content-based Recommendation Model Training via Noise-aware Coreset Selection | arXiv:2601.10067v1 Announce Type: new Abstract: Content-based recommendation systems (CRSs) utilize content features to predict user-item interactions, serving as essential tools for helping users navigate information-rich web services. However, ensuring the effectiveness of CRSs requires large-scale and even continuou... | https://arxiv.org/abs/2601.10067 | Academic Papers | svg |
f3eea7b503d45d044bb3e43af005d8d1d3d39df63728f4cdbcc7095483b85062 | 2026-01-16T00:00:00-05:00 | S$^2$F: Principled Hybrid Testing With Fuzzing, Symbolic Execution, and Sampling | arXiv:2601.10068v1 Announce Type: new Abstract: Hybrid testing that integrates fuzzing, symbolic execution, and sampling has demonstrated superior testing efficiency compared to individual techniques. However, the state-of-the-art (SOTA) hybrid testing tools do not fully exploit the capabilities of symbolic execution a... | https://arxiv.org/abs/2601.10068 | Academic Papers | svg |
c3e028d2ab6cda95d1dd066cd8b43df6e45a17cf3c685182acb4396e023908e1 | 2026-01-16T00:00:00-05:00 | Comparative Evaluation of Deep Learning-Based and WHO-Informed Approaches for Sperm Morphology Assessment | arXiv:2601.10070v1 Announce Type: new Abstract: Assessment of sperm morphological quality remains a critical yet subjective component of male fertility evaluation, often limited by inter-observer variability and resource constraints. This study presents a comparative biomedical artificial intelligence framework evaluat... | https://arxiv.org/abs/2601.10070 | Academic Papers | svg |
6a402f5b10bf573c9ee9fbc1aff758e6ca4451677948496d444148032471825f | 2026-01-16T00:00:00-05:00 | ReaMIL: Reasoning- and Evidence-Aware Multiple Instance Learning for Whole-Slide Histopathology | arXiv:2601.10073v1 Announce Type: new Abstract: We introduce ReaMIL (Reasoning- and Evidence-Aware MIL), a multiple instance learning approach for whole-slide histopathology that adds a light selection head to a strong MIL backbone. The head produces soft per-tile gates and is trained with a budgeted-sufficiency object... | https://arxiv.org/abs/2601.10073 | Academic Papers | svg |
680d988e53d0ca2d2078c3405d1d76759e0130a544dfcfdadd3b26c74a1d5811 | 2026-01-16T00:00:00-05:00 | Thinking Like Van Gogh: Structure-Aware Style Transfer via Flow-Guided 3D Gaussian Splatting | arXiv:2601.10075v1 Announce Type: new Abstract: In 1888, Vincent van Gogh wrote, "I am seeking exaggeration in the essential." This principle, amplifying structural form while suppressing photographic detail, lies at the core of Post-Impressionist art. However, most existing 3D style transfer methods invert this philos... | https://arxiv.org/abs/2601.10075 | Academic Papers | svg |
a80f10af707f3969acea63e222e004a4413937459ec9a6255883b771368e240a | 2026-01-16T00:00:00-05:00 | Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts | arXiv:2601.10079v1 Announce Type: new Abstract: Reinforcement Learning (RL) has become essential for eliciting complex reasoning capabilities in Large Language Models (LLMs). However, the substantial memory overhead of storing Key-Value (KV) caches during long-horizon rollouts acts as a critical bottleneck, often prohi... | https://arxiv.org/abs/2601.10079 | Academic Papers | svg |
89c8e73e16e08fb6c6809c5bc83bed00b640692a5da9605f22528004b3044c27 | 2026-01-16T00:00:00-05:00 | Deriving Character Logic from Storyline as Codified Decision Trees | arXiv:2601.10080v1 Announce Type: new Abstract: Role-playing (RP) agents rely on behavioral profiles to act consistently across diverse narrative contexts, yet existing profiles are largely unstructured, non-executable, and weakly validated, leading to brittle agent behavior. We propose Codified Decision Trees (CDT), a... | https://arxiv.org/abs/2601.10080 | Academic Papers | svg |
b0afba20cc8cfea3c1cb6cc5a4b13a7f3387ff86f22a20952c0d785a984f4115 | 2026-01-16T00:00:00-05:00 | Is MT Ready for the Next Crisis or Pandemic? | arXiv:2601.10082v1 Announce Type: new Abstract: Communication in times of crisis is essential. However, there is often a mismatch between the language of governments, aid providers, doctors, and those to whom they are providing aid. Commercial MT systems are reasonable tools to turn to in these scenarios. But how effec... | https://arxiv.org/abs/2601.10082 | Academic Papers | svg |
7fdbaa02b1c023bfc9c7c51914b58a7f93e167e817919abbf1a262bd466578f5 | 2026-01-16T00:00:00-05:00 | Starfield: Demand-Aware Satellite Topology Design for Low-Earth Orbit Mega Constellations | arXiv:2601.10083v1 Announce Type: new Abstract: Low-Earth orbit (LEO) mega-constellations are emerging as high-capacity backbones for next-generation Internet. Deployment of laser terminals enables high-bandwidth, low-latency inter-satellite links (ISLs); however, their limited number, slow acquisition, and instability... | https://arxiv.org/abs/2601.10083 | Academic Papers | svg |
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