id stringlengths 64 64 | published stringlengths 19 25 | title stringlengths 7 262 | description stringlengths 6 54.4k | link stringlengths 31 227 | category stringclasses 6
values | image stringlengths 3 247 |
|---|---|---|---|---|---|---|
b72ce0fe91336d935ebabe1332eed6fa3e011b6feffd81d009c8fa0d6fbff001 | 2026-01-13T00:00:00-05:00 | SkyNomad: On Using Multi-Region Spot Instances to Minimize AI Batch Job Cost | arXiv:2601.06520v1 Announce Type: new Abstract: AI batch jobs such as model training, inference pipelines, and data analytics require substantial GPU resources and often need to finish before a deadline. Spot instances offer 3-10x lower cost than on-demand instances, but their unpredictable availability makes meeting d... | https://arxiv.org/abs/2601.06520 | Academic Papers | svg |
a642489adc93c648899bceda50b863b65f61df702e79ca8edbb787fbf461c589 | 2026-01-13T00:00:00-05:00 | BabyVision: Visual Reasoning Beyond Language | arXiv:2601.06521v1 Announce Type: new Abstract: While humans develop core visual skills long before acquiring language, contemporary Multimodal LLMs (MLLMs) still rely heavily on linguistic priors to compensate for their fragile visual understanding. We uncovered a crucial fact: state-of-the-art MLLMs consistently fail... | https://arxiv.org/abs/2601.06521 | Academic Papers | svg |
56ea587ddab523dc7da199c988487ebb6ce256d28f9dde22601a2e5055fb1d0a | 2026-01-13T00:00:00-05:00 | Toward Generalizable Deblurring: Leveraging Massive Blur Priors with Linear Attention for Real-World Scenarios | arXiv:2601.06525v1 Announce Type: new Abstract: Image deblurring has advanced rapidly with deep learning, yet most methods exhibit poor generalization beyond their training datasets, with performance dropping significantly in real-world scenarios. Our analysis shows this limitation stems from two factors: datasets face... | https://arxiv.org/abs/2601.06525 | Academic Papers | svg |
108ed9db0bc1e25798e943a17728a62d5ac33d0f968e90dd95fd0ad8a7a1247e | 2026-01-13T00:00:00-05:00 | Visible Light Communication using Led-Based AR Markers for Robot Localization | arXiv:2601.06527v1 Announce Type: new Abstract: A method of information transmission using visual markers has been widely studied. In this approach, information or identifiers (IDs) are encoded in the black-and-white pattern of each marker. By analyzing the geometric properties of the marker frame - such as its size, d... | https://arxiv.org/abs/2601.06527 | Academic Papers | svg |
9f0e56ed3f6848e14b42a48a7dffc39abecc80b66f04c5331f483675ed1e3a6f | 2026-01-13T00:00:00-05:00 | Atomic-SNLI: Fine-Grained Natural Language Inference through Atomic Fact Decomposition | arXiv:2601.06528v1 Announce Type: new Abstract: Current Natural Language Inference (NLI) systems primarily operate at the sentence level, providing black-box decisions that lack explanatory power. While atomic-level NLI offers a promising alternative by decomposing hypotheses into individual facts, we demonstrate that ... | https://arxiv.org/abs/2601.06528 | Academic Papers | svg |
74baacffcfc3bd8d3ede9297ab784bdcd9f992be5358f5e96079fe954c0a9456 | 2026-01-13T00:00:00-05:00 | Improving Day-Ahead Grid Carbon Intensity Forecasting by Joint Modeling of Local-Temporal and Cross-Variable Dependencies Across Different Frequencies | arXiv:2601.06530v1 Announce Type: new Abstract: Accurate forecasting of the grid carbon intensity factor (CIF) is critical for enabling demand-side management and reducing emissions in modern electricity systems. Leveraging multiple interrelated time series, CIF prediction is typically formulated as a multivariate time... | https://arxiv.org/abs/2601.06530 | Academic Papers | svg |
f5c3b575c0202db44fef8d0c0fb3980ea2d5b862afe730387ab67511a7d9e22d | 2026-01-13T00:00:00-05:00 | Short-term electricity load forecasting with multi-frequency reconstruction diffusion | arXiv:2601.06533v1 Announce Type: new Abstract: Diffusion models have emerged as a powerful method in various applications. However, their application to Short-Term Electricity Load Forecasting (STELF) -- a typical scenario in energy systems -- remains largely unexplored. Considering the nonlinear and fluctuating chara... | https://arxiv.org/abs/2601.06533 | Academic Papers | svg |
b96fa48bd7f06450e61e1e1381b205887e23796ea9ac83fc8f589e587c6af2d0 | 2026-01-13T00:00:00-05:00 | Automated dimensional analysis for PDEs | arXiv:2601.06535v1 Announce Type: new Abstract: Physical units are fundamental to scientific computing. However, many finite element frameworks lack built-in support for dimensional analysis. In this work, we present a systematic framework for integrating physical units into the Unified Form Language (UFL). We implemen... | https://arxiv.org/abs/2601.06535 | Academic Papers | svg |
25b6c1aff872d41f861f74bde9a4d399a27d085d30dba28e0039709a93325a4b | 2026-01-13T00:00:00-05:00 | Expos\'ia: Academic Writing Assessment of Expos\'es and Peer Feedback | arXiv:2601.06536v1 Announce Type: new Abstract: We present Expos\'ia, the first public dataset that connects writing and feedback assessment in higher education, enabling research on educationally grounded approaches to academic writing evaluation. Expos\'ia includes student research project proposals and peer and inst... | https://arxiv.org/abs/2601.06536 | Academic Papers | svg |
6bb58a478b8587030cd7f09c79f9aedf4f337bc330e0ae60864a0d9913bdb8ed | 2026-01-13T00:00:00-05:00 | Towards Egocentric 3D Hand Pose Estimation in Unseen Domains | arXiv:2601.06537v1 Announce Type: new Abstract: We present V-HPOT, a novel approach for improving the cross-domain performance of 3D hand pose estimation from egocentric images across diverse, unseen domains. State-of-the-art methods demonstrate strong performance when trained and tested within the same domain. However... | https://arxiv.org/abs/2601.06537 | Academic Papers | svg |
c475ec036f2e5280fbcc32995d382f20b9763d035f53685d985ee21eb6563228 | 2026-01-13T00:00:00-05:00 | Self-Organizing Dual-Buffer Adaptive Clustering Experience Replay (SODASER) for Safe Reinforcement Learning in Optimal Control | arXiv:2601.06540v1 Announce Type: new Abstract: This paper proposes a novel reinforcement learning framework, named Self-Organizing Dual-buffer Adaptive Clustering Experience Replay (SODACER), designed to achieve safe and scalable optimal control of nonlinear systems. The proposed SODACER mechanism consisting of a Fast... | https://arxiv.org/abs/2601.06540 | Academic Papers | svg |
82829845fe9ff83170e3e0f95b353ca749c28c9204e309504a03b993eba94912 | 2026-01-13T00:00:00-05:00 | SimLLM: Fine-Tuning Code LLMs for SimPy-Based Queueing System Simulation | arXiv:2601.06543v1 Announce Type: new Abstract: The Python package SimPy is widely used for modeling queueing systems due to its flexibility, simplicity, and smooth integration with modern data analysis and optimization frameworks. Recent advances in large language models (LLMs) have shown strong ability in generating ... | https://arxiv.org/abs/2601.06543 | Academic Papers | svg |
c7e493b5505f754403f536730bc2d0866aa818631c6239dfe07b0475f64d09bf | 2026-01-13T00:00:00-05:00 | LLMTrack: Semantic Multi-Object Tracking with Multi-modal Large Language Models | arXiv:2601.06550v1 Announce Type: new Abstract: Traditional Multi-Object Tracking (MOT) systems have achieved remarkable precision in localization and association, effectively answering \textit{where} and \textit{who}. However, they often function as autistic observers, capable of tracing geometric paths but blind to t... | https://arxiv.org/abs/2601.06550 | Academic Papers | svg |
c0b30fa2c47b53855285b01cf4ca675817b99c0fd8c2922b2adfefb1a433b258 | 2026-01-13T00:00:00-05:00 | L-RAG: Balancing Context and Retrieval with Entropy-Based Lazy Loading | arXiv:2601.06551v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as the predominant paradigm for grounding Large Language Model outputs in factual knowledge, effectively mitigating hallucinations. However, conventional RAG systems operate under a "retrieve-always" assumption, querying ve... | https://arxiv.org/abs/2601.06551 | Academic Papers | svg |
593bbf829e91b52cf16c519737a9b246f1e9d1c6282d4c06abba23979f51ac2a | 2026-01-13T00:00:00-05:00 | Model Reconciliation through Explainability and Collaborative Recovery in Assistive Robotics | arXiv:2601.06552v1 Announce Type: new Abstract: Whenever humans and robots work together, it is essential that unexpected robot behavior can be explained to the user. Especially in applications such as shared control the user and the robot must share the same model of the objects in the world, and the actions that can ... | https://arxiv.org/abs/2601.06552 | Academic Papers | svg |
4c6d0b84219ac0d32b98bc0ae258e219c33e24094e3d6d0712da2c52111c00f5 | 2026-01-13T00:00:00-05:00 | A Bayesian Network-Driven Zero Trust Model for Cyber Risk Quantification in Small-Medium Businesses | arXiv:2601.06553v1 Announce Type: new Abstract: Small-Medium Businesses (SMBs) are essential to global economies yet remain highly vulnerable to cyberattacks due to limited budgets, inadequate cybersecurity expertise, and underestimation of cyber risks. Their increasing reliance on digital infrastructures has expanded ... | https://arxiv.org/abs/2601.06553 | Academic Papers | svg |
8863dcc0fc5e6d4bcc1e1a1273466bb30775a88d1e976ef0664d0f1f949ef641 | 2026-01-13T00:00:00-05:00 | QES-Backed Virtual FIDO2 Authenticators: Architectural Options for Secure, Synchronizable WebAuthn Credentials | arXiv:2601.06554v1 Announce Type: new Abstract: FIDO2 and the WebAuthn standard offer phishing-resistant, public-key based authentication but traditionally rely on device-bound cryptographic keys that are not naturally portable across user devices. Recent passkey deployments address this limitation by enabling multi-de... | https://arxiv.org/abs/2601.06554 | Academic Papers | svg |
889f913207bf319e99ef4e315edacde4a00643bbee43c1476b0077d61b6014c2 | 2026-01-13T00:00:00-05:00 | Modeling Descriptive Norms in Multi-Agent Systems: An Auto-Aggregation PDE Framework with Adaptive Perception Kernels | arXiv:2601.06557v1 Announce Type: new Abstract: This paper presents a PDE-based auto-aggregation model for simulating descriptive norm dynamics in autonomous multi-agent systems, capturing convergence and violation through non-local perception kernels and external potential fields. Extending classical transport equatio... | https://arxiv.org/abs/2601.06557 | Academic Papers | svg |
0b0b26e4416d7cf29b39863671863c8109623aeaa58665fc383fedb4473fe810 | 2026-01-13T00:00:00-05:00 | Hard Thresholding Pursuit Algorithms for Least Absolute Deviations Problem | arXiv:2601.06558v1 Announce Type: new Abstract: Least absolute deviations (LAD) is a statistical optimality criterion widely utilized in scenarios where a minority of measurements are contaminated by outliers of arbitrary magnitudes. In this paper, we delve into the robustness of the variant of adaptive iterative hard ... | https://arxiv.org/abs/2601.06558 | Academic Papers | svg |
1c874c95c4b9369f90f23b5060ff977ce1d0c5740501e7f5ee35557be2c593f2 | 2026-01-13T00:00:00-05:00 | ArrowGEV: Grounding Events in Video via Learning the Arrow of Time | arXiv:2601.06559v1 Announce Type: new Abstract: Grounding events in videos serves as a fundamental capability in video analysis. While Vision-Language Models (VLMs) are increasingly employed for this task, existing approaches predominantly train models to associate events with timestamps in the forward video only. This... | https://arxiv.org/abs/2601.06559 | Academic Papers | svg |
499cb3a5f9c8ff2e5b855ffb0c2c1e312c26363e314b4ff17cad247ad5d9d96b | 2026-01-13T00:00:00-05:00 | Mosaic: Unlocking Long-Context Inference for Diffusion LLMs via Global Memory Planning and Dynamic Peak Taming | arXiv:2601.06562v1 Announce Type: new Abstract: Diffusion-based large language models (dLLMs) have emerged as a promising paradigm, utilizing simultaneous denoising to enable global planning and iterative refinement. While these capabilities are particularly advantageous for long-context generation, deploying such mode... | https://arxiv.org/abs/2601.06562 | Academic Papers | svg |
d407c58583dc13deac0caa2b1848f7712f5ffe3fd3fc2b158dc9a68f8b761ebf | 2026-01-13T00:00:00-05:00 | CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale | arXiv:2601.06564v1 Announce Type: new Abstract: Natural language to SQL translation (Text-to-SQL) is one of the long-standing problems that has recently benefited from advances in Large Language Models (LLMs). While most academic Text-to-SQL benchmarks request schema description as a part of natural language input, ent... | https://arxiv.org/abs/2601.06564 | Academic Papers | svg |
433e99ed32b415dfac5828ae1791a05428064149b9e0c3dc4bb14cf0be749333 | 2026-01-13T00:00:00-05:00 | EVM-QuestBench: An Execution-Grounded Benchmark for Natural-Language Transaction Code Generation | arXiv:2601.06565v1 Announce Type: new Abstract: Large language models are increasingly applied to various development scenarios. However, in on-chain transaction scenarios, even a minor error can cause irreversible loss for users. Existing evaluations often overlook execution accuracy and safety. We introduce EVM-Quest... | https://arxiv.org/abs/2601.06565 | Academic Papers | svg |
7ace90428a4246f087117281e9eedcd42e01f35b7da134c6a38247ca6455cb6e | 2026-01-13T00:00:00-05:00 | QCaption: Video Captioning and Q&A through Fusion of Large Multimodal Models | arXiv:2601.06566v1 Announce Type: new Abstract: This paper introduces QCaption, a novel video captioning and Q&A pipeline that enhances video analytics by fusing three models: key frame extraction, a Large Multimodal Model (LMM) for image-text analysis, and a Large Language Model (LLM) for text analysis. This appro... | https://arxiv.org/abs/2601.06566 | Academic Papers | svg |
6c6706d4db20f0dec2a6b26faee3b59e3a4c70794a0e2debd6136aa8d9dc5ce6 | 2026-01-13T00:00:00-05:00 | Robustness Quantification of MIMO-PI Controller From the Perspective of \(\gamma\)-Dissipativity | arXiv:2601.06568v1 Announce Type: new Abstract: The proportional-integral-derivative (PID) controller and its variants are widely used in control engineering, but they often rely on linearization around equilibrium points and empirical parameter tuning, making them ineffective for multi-input-multi-output (MIMO) system... | https://arxiv.org/abs/2601.06568 | Academic Papers | svg |
8318a4a65896f7cfd8a4cf3ef0bc9ddb51c0a9a78f1a0e25404fdbada15d31d4 | 2026-01-13T00:00:00-05:00 | Hellinger Multimodal Variational Autoencoders | arXiv:2601.06572v1 Announce Type: new Abstract: Multimodal variational autoencoders (VAEs) are widely used for weakly supervised generative learning with multiple modalities. Predominant methods aggregate unimodal inference distributions using either a product of experts (PoE), a mixture of experts (MoE), or their comb... | https://arxiv.org/abs/2601.06572 | Academic Papers | svg |
16f29d9b7f59dac952c926251c72f34fd394bf82c4217ae5c2e7325c4721439c | 2026-01-13T00:00:00-05:00 | QMAVIS: Long Video-Audio Understanding using Fusion of Large Multimodal Models | arXiv:2601.06573v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) for video-audio understanding have traditionally been evaluated only on shorter videos of a few minutes long. In this paper, we introduce QMAVIS (Q Team-Multimodal Audio Video Intelligent Sensemaking), a novel long video-audio understanding ... | https://arxiv.org/abs/2601.06573 | Academic Papers | svg |
e01b175e42bfd3b7a9d437f7c26469a0bf413dbeb0018b467a607f87640c4b5a | 2026-01-13T00:00:00-05:00 | APEX: Learning Adaptive Priorities for Multi-Objective Alignment in Vision-Language Generation | arXiv:2601.06574v1 Announce Type: new Abstract: Multi-objective alignment for text-to-image generation is commonly implemented via static linear scalarization, but fixed weights often fail under heterogeneous rewards, leading to optimization imbalance where models overfit high-variance, high-responsiveness objectives (... | https://arxiv.org/abs/2601.06574 | Academic Papers | svg |
eb0cd72fce8a3da9f39a4edae5cd9cb66ece2f3bbbaf4db99308268cd057aa65 | 2026-01-13T00:00:00-05:00 | Are Emotions Arranged in a Circle? Geometric Analysis of Emotion Representations via Hyperspherical Contrastive Learning | arXiv:2601.06575v1 Announce Type: new Abstract: Psychological research has long utilized circumplex models to structure emotions, placing similar emotions adjacently and opposing ones diagonally. Although frequently used to interpret deep learning representations, these models are rarely directly incorporated into the ... | https://arxiv.org/abs/2601.06575 | Academic Papers | svg |
16d4630196cc1f4b962bb30f67a23f258fd8d8b4d402291bc6205049e90d4670 | 2026-01-13T00:00:00-05:00 | Stylistic Evolution and LLM Neutrality in Singlish Language | arXiv:2601.06580v1 Announce Type: new Abstract: Singlish is a creole rooted in Singapore's multilingual environment and continues to evolve alongside social and technological change. This study investigates the evolution of Singlish over a decade of informal digital text messages. We propose a stylistic similarity fram... | https://arxiv.org/abs/2601.06580 | Academic Papers | svg |
d47708cf441ed713f7170dce98f598c55c3dd289560e5923e9600944b815733e | 2026-01-13T00:00:00-05:00 | Softly Induced Functional Simplicity Implications for Neural Network Generalisation, Robustness, and Distillation | arXiv:2601.06584v1 Announce Type: new Abstract: Learning robust and generalisable abstractions from high-dimensional input data is a central challenge in machine learning and its applications to high-energy physics (HEP). Solutions of lower functional complexity are known to produce abstractions that generalise more ef... | https://arxiv.org/abs/2601.06584 | Academic Papers | svg |
d353100a62d51ddc4cbce0bbad5099d023b037f6bba813254a19854c5e0fca0b | 2026-01-13T00:00:00-05:00 | Detecting LLM-Generated Text with Performance Guarantees | arXiv:2601.06586v1 Announce Type: new Abstract: Large language models (LLMs) such as GPT, Claude, Gemini, and Grok have been deeply integrated into our daily life. They now support a wide range of tasks -- from dialogue and email drafting to assisting with teaching and coding, serving as search engines, and much more. ... | https://arxiv.org/abs/2601.06586 | Academic Papers | svg |
5b53b6b3f35f4131caa380f7d8176382f13f1d8feb8a88db74fc9423bfe6dee2 | 2026-01-13T00:00:00-05:00 | TCLNet: A Hybrid Transformer-CNN Framework Leveraging Language Models as Lossless Compressors for CSI Feedback | arXiv:2601.06588v1 Announce Type: new Abstract: In frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) plays a crucial role in achieving high spectrum and energy efficiency. However, the CSI feedback overhead becomes a major bottleneck as th... | https://arxiv.org/abs/2601.06588 | Academic Papers | svg |
72d34f20d06930cc8618f5be335772365aa55e4d25bc2e5927ae7d705b187fc4 | 2026-01-13T00:00:00-05:00 | Modeling Tradeoffs between mobility, cost, and performance in Edge Computing | arXiv:2601.06591v1 Announce Type: new Abstract: Edge computing provides a cloud-like architecture where small-scale resources are distributed near the network edge, enabling applications on resource-constrained devices to offload latency-critical computations to these resources. While some recent work showed that the r... | https://arxiv.org/abs/2601.06591 | Academic Papers | svg |
82276a7a093e0feacf4fe61b9b7ced8b33fde13931c99e8166d1735893ea7b6e | 2026-01-13T00:00:00-05:00 | Are LLMs Vulnerable to Preference-Undermining Attacks (PUA)? A Factorial Analysis Methodology for Diagnosing the Trade-off between Preference Alignment and Real-World Validity | arXiv:2601.06596v1 Announce Type: new Abstract: Large Language Model (LLM) training often optimizes for preference alignment, rewarding outputs that are perceived as helpful and interaction-friendly. However, this preference-oriented objective can be exploited: manipulative prompts can steer responses toward user-appea... | https://arxiv.org/abs/2601.06596 | Academic Papers | svg |
63a8c6f6a3f1a5062fdb1755bbe3f14b2c99fa757fe42295ff09f0ce9a192daa | 2026-01-13T00:00:00-05:00 | Implicit bias as a Gauge correction: Theory and Inverse Design | arXiv:2601.06597v1 Announce Type: new Abstract: A central problem in machine learning theory is to characterize how learning dynamics select particular solutions among the many compatible with the training objective, a phenomenon, called implicit bias, which remains only partially characterized. In the present work, we... | https://arxiv.org/abs/2601.06597 | Academic Papers | svg |
410710a341e76d01ab4e1a8a6ada0ce2af96d2bca20717affb149d4b8569a211 | 2026-01-13T00:00:00-05:00 | How Context Shapes Truth: Geometric Transformations of Statement-level Truth Representations in LLMs | arXiv:2601.06599v1 Announce Type: new Abstract: Large Language Models (LLMs) often encode whether a statement is true as a vector in their residual stream activations. These vectors, also known as truth vectors, have been studied in prior work, however how they change when context is introduced remains unexplored. We s... | https://arxiv.org/abs/2601.06599 | Academic Papers | svg |
36655046df30d121ce1839e4738c4134e8bb1d266d9694e0f397ce01b9e0dc5d | 2026-01-13T00:00:00-05:00 | Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation | arXiv:2601.06600v1 Announce Type: new Abstract: Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues. While Multimodal Large Language Models (MLLMs) have demonstrated impressive reasoning capabilities, their robustness against... | https://arxiv.org/abs/2601.06600 | Academic Papers | svg |
4a9ef9f30960c7a98a352efbbca4b43f89a613cae3fa19048082eb9da4d0277b | 2026-01-13T00:00:00-05:00 | UMLoc: Uncertainty-Aware Map-Constrained Inertial Localization with Quantified Bounds | arXiv:2601.06602v1 Announce Type: new Abstract: Inertial localization is particularly valuable in GPS-denied environments such as indoors. However, localization using only Inertial Measurement Units (IMUs) suffers from drift caused by motion-process noise and sensor biases. This paper introduces Uncertainty-aware Map-c... | https://arxiv.org/abs/2601.06602 | Academic Papers | svg |
6ea549df531e9a763badf8709eb704be7a2d03168459d6b052b35501e906bca4 | 2026-01-13T00:00:00-05:00 | N2N-GQA: Noise-to-Narrative for Graph-Based Table-Text Question Answering Using LLMs | arXiv:2601.06603v1 Announce Type: new Abstract: Multi-hop question answering over hybrid table-text data requires retrieving and reasoning across multiple evidence pieces from large corpora, but standard Retrieval-Augmented Generation (RAG) pipelines process documents as flat ranked lists, causing retrieval noise to ob... | https://arxiv.org/abs/2601.06603 | Academic Papers | svg |
d222491d63181988efdf54e1e37cab0d309933df836cce6a365c9a817d057b44 | 2026-01-13T00:00:00-05:00 | Object-Centric World Models Meet Monte Carlo Tree Search | arXiv:2601.06604v1 Announce Type: new Abstract: In this paper, we introduce ObjectZero, a novel reinforcement learning (RL) algorithm that leverages the power of object-level representations to model dynamic environments more effectively. Unlike traditional approaches that process the world as a single undifferentiated... | https://arxiv.org/abs/2601.06604 | Academic Papers | svg |
79c61301a642bd40679492c3881a09b9ea829aacbe6db7167393357d1f1e4f70 | 2026-01-13T00:00:00-05:00 | Sissi: Zero-shot Style-guided Image Synthesis via Semantic-style Integration | arXiv:2601.06605v1 Announce Type: new Abstract: Text-guided image generation has advanced rapidly with large-scale diffusion models, yet achieving precise stylization with visual exemplars remains difficult. Existing approaches often depend on task-specific retraining or expensive inversion procedures, which can compro... | https://arxiv.org/abs/2601.06605 | Academic Papers | svg |
54cbcabd3ad3832a6b6ed6a96bdd1d7efa7318b500587a8a185e98481f18f194 | 2026-01-13T00:00:00-05:00 | CEDAR: Context Engineering for Agentic Data Science | arXiv:2601.06606v1 Announce Type: new Abstract: We demonstrate CEDAR, an application for automating data science (DS) tasks with an agentic setup. Solving DS problems with LLMs is an underexplored area that has immense market value. The challenges are manifold: task complexities, data sizes, computational limitations, ... | https://arxiv.org/abs/2601.06606 | Academic Papers | svg |
d91502d14757ad452170661ee96b4b861ed77ff1ba6b18fc3cf399f2e55cef61 | 2026-01-13T00:00:00-05:00 | Pragya: An AI-Based Semantic Recommendation System for Sanskrit Subhasitas | arXiv:2601.06607v1 Announce Type: new Abstract: Sanskrit Subhasitas encapsulate centuries of cultural and philosophical wisdom, yet remain underutilized in the digital age due to linguistic and contextual barriers. In this work, we present Pragya, a retrieval-augmented generation (RAG) framework for semantic recommenda... | https://arxiv.org/abs/2601.06607 | Academic Papers | svg |
059c5230357d94e4ecccb61d60ad3e769eae9f38aa92c4f28da52a666df84a48 | 2026-01-13T00:00:00-05:00 | Symplectic Hulls over a Non-Unital Ring | arXiv:2601.06609v1 Announce Type: new Abstract: This paper presents the study of the symplectic hulls over a non-unital ring $ E= \langle \kappa,\tau \mid 2 \kappa =2 \tau=0,~ \kappa^2=\kappa,~ \tau^2=\tau,~ \kappa \tau=\kappa,~ \tau \kappa=\tau \rangle$. We first identify the residue and torsion codes of the left, rig... | https://arxiv.org/abs/2601.06609 | Academic Papers | svg |
314be1298e1b3762e856788b39c24ef6989e584d922c929564442848a4d7bf70 | 2026-01-13T00:00:00-05:00 | AI Washing and the Erosion of Digital Legitimacy: A Socio-Technical Perspective on Responsible Artificial Intelligence in Business | arXiv:2601.06611v1 Announce Type: new Abstract: The rapid evolution of artificial intelligence (AI) systems, tools, and technologies has opened up novel, unprecedented opportunities for businesses to innovate, differentiate, and compete. However, growing concerns have emerged about the use of AI in businesses, particul... | https://arxiv.org/abs/2601.06611 | Academic Papers | svg |
5ab2192c1830376839686b75ca1deb597cf40d2e0bdb6ac21eae7d305a0eace2 | 2026-01-13T00:00:00-05:00 | Cross-Border Data Security and Privacy Risks in Large Language Models and IoT Systems | arXiv:2601.06612v1 Announce Type: new Abstract: The reliance of Large Language Models and Internet of Things systems on massive, globally distributed data flows creates systemic security and privacy challenges. When data traverses borders, it becomes subject to conflicting legal regimes, such as the EU's General Data P... | https://arxiv.org/abs/2601.06612 | Academic Papers | svg |
90f9af2bffb0b70f50d969c7226e0547f485ec6c7e895f6dfe3b23ad5645bed8 | 2026-01-13T00:00:00-05:00 | Industrial Semantics-Aware Digital Twins: A Hybrid Graph Matching Approach for Asset Administration Shells | arXiv:2601.06613v1 Announce Type: new Abstract: Although the Asset Administration Shell (AAS) standard provides a structured and machine-readable representation of industrial assets, their semantic comparability remains a major challenge, particularly when different vocabularies and modeling practices are used. Enginee... | https://arxiv.org/abs/2601.06613 | Academic Papers | svg |
994f9bc0b761cd9e9c984d81a5a3a0f4153a1400baf0fbcb7770fe1dc5a1d9f8 | 2026-01-13T00:00:00-05:00 | Fixturize: Bridging the Fixture Gap in Test Generation | arXiv:2601.06615v1 Announce Type: new Abstract: Current Large Language Models (LLMs) have advanced automated unit test generation but face a critical limitation: they often neglect to construct the necessary test fixtures, which are the environmental setups required for a test to run. To bridge this gap, this paper pro... | https://arxiv.org/abs/2601.06615 | Academic Papers | svg |
641fd8f1f8bea912657ef3600b83962edd0c8daa678bfef2ac041cdad98b198b | 2026-01-13T00:00:00-05:00 | LLM-Driven Accessible Interface: A Model-Based Approach | arXiv:2601.06616v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into interactive systems opens new opportunities for adaptive user experiences, yet it also raises challenges regarding accessibility, explainability, and normative compliance. This paper presents an implemented model-driven... | https://arxiv.org/abs/2601.06616 | Academic Papers | svg |
9d16eb6b2cf79f1e2d57273c5de5847c64d3e32b3f8d70dbd993948d42156adc | 2026-01-13T00:00:00-05:00 | Robotic Tele-Operation for Upper Aerodigestive Tract Microsurgery: System Design and Validation | arXiv:2601.06617v1 Announce Type: new Abstract: Upper aerodigestive tract (UADT) treatments frequently employ transoral laser microsurgery (TLM) for procedures such as the removal of tumors or polyps. In TLM, a laser beam is used to cut target tissue, while forceps are employed to grasp, manipulate, and stabilize tissu... | https://arxiv.org/abs/2601.06617 | Academic Papers | svg |
0f2a3806fc0106c4bb4797c894a0e4afb4d5b5fd790f0ed4b55a0f26825f39cb | 2026-01-13T00:00:00-05:00 | Efficient and Reliable Estimation of Named Entity Linking Quality: A Case Study on GutBrainIE | arXiv:2601.06624v1 Announce Type: new Abstract: Named Entity Linking (NEL) is a core component of biomedical Information Extraction (IE) pipelines, yet assessing its quality at scale is challenging due to the high cost of expert annotations and the large size of corpora. In this paper, we present a sampling-based frame... | https://arxiv.org/abs/2601.06624 | Academic Papers | svg |
583425fbe9ea88f28715108dc5b559a35e06a511f0b65a0c963ba33bca5b0077 | 2026-01-13T00:00:00-05:00 | On traces of the derivatives of the $L^2$-projection error | arXiv:2601.06625v1 Announce Type: new Abstract: We provide derivative estimates for the $L^2$ projection of an $H^{k}$ function onto the space of polynomials of degree $\leq p$. The bounds are explicit in the order of differentiation and the polynomial degree $p$. | https://arxiv.org/abs/2601.06625 | Academic Papers | svg |
423ae61218bd4333ff0b3e931f3769780e27df07d7d98d68fa4a6696e6ea77d2 | 2026-01-13T00:00:00-05:00 | Burn-After-Use for Preventing Data Leakage through a Secure Multi-Tenant Architecture in Enterprise LLM | arXiv:2601.06627v1 Announce Type: new Abstract: This study presents a Secure Multi-Tenant Architecture (SMTA) combined with a novel concept Burn-After-Use (BAU) mechanism for enterprise LLM environments to effectively prevent data leakage. As institutions increasingly adopt LLMs across departments, the risks of data le... | https://arxiv.org/abs/2601.06627 | Academic Papers | svg |
5ec2dfb91ebb45a7a1faae9f01b47384ddb905c7164309185c23acf9bb8ac71a | 2026-01-13T00:00:00-05:00 | Lower Bounds for the Algorithmic Complexity of Learned Indexes | arXiv:2601.06629v1 Announce Type: new Abstract: Learned index structures aim to accelerate queries by training machine learning models to approximate the rank function associated with a database attribute. While effective in practice, their theoretical limitations are not fully understood. We present a general framewor... | https://arxiv.org/abs/2601.06629 | Academic Papers | svg |
f9a3a6c6180e064bd679ba6300d6f0425dbbdc1f1708e72ee7f1d8a9e7fd1c7a | 2026-01-13T00:00:00-05:00 | Labels have Human Values: Value Calibration of Subjective Tasks | arXiv:2601.06631v1 Announce Type: new Abstract: Building NLP systems for subjective tasks requires one to ensure their alignment to contrasting human values. We propose the MultiCalibrated Subjective Task Learner framework (MC-STL), which clusters annotations into identifiable human value clusters by three approaches (... | https://arxiv.org/abs/2601.06631 | Academic Papers | svg |
7c17f62bd855dc3036688dff7d0699f1fb23cc61de06d22d197c439b2c3b40c6 | 2026-01-13T00:00:00-05:00 | KASER: Knowledge-Aligned Student Error Simulator for Open-Ended Coding Tasks | arXiv:2601.06633v1 Announce Type: new Abstract: Open-ended tasks, such as coding problems that are common in computer science education, provide detailed insights into student knowledge. However, training large language models (LLMs) to simulate and predict possible student errors in their responses to these problems c... | https://arxiv.org/abs/2601.06633 | Academic Papers | svg |
c1bdd2f3a34e151595eb36666e9eb85a083e3971c17ed830026c3c4b5433c6e6 | 2026-01-13T00:00:00-05:00 | A Framework for Kara-Kichwa Data Sovereignty in Latin America and the Caribbean | arXiv:2601.06634v1 Announce Type: new Abstract: In the high-altitude territories of the Andean-Amazonian-Atlantic pathway, data is not merely a digital resource but an extension of Khipu Panaka, the genealogical and relational memory of the Kara-Kichwa Republics. This perspective paper introduces the Kara-Kichwa Data S... | https://arxiv.org/abs/2601.06634 | Academic Papers | svg |
6f1fbf1bbbd33824f3f05665946dce35934ec64dd7db2963a7f6ea3091c341a1 | 2026-01-13T00:00:00-05:00 | MedEinst: Benchmarking the Einstellung Effect in Medical LLMs through Counterfactual Differential Diagnosis | arXiv:2601.06636v1 Announce Type: new Abstract: Despite achieving high accuracy on medical benchmarks, LLMs exhibit the Einstellung Effect in clinical diagnosis--relying on statistical shortcuts rather than patient-specific evidence, causing misdiagnosis in atypical cases. Existing benchmarks fail to detect this critic... | https://arxiv.org/abs/2601.06636 | Academic Papers | svg |
8e00a95c989f9f99b3bf953b41ae1257ab789ba58abe7e65fd4998b8e684faa8 | 2026-01-13T00:00:00-05:00 | Efficient Aspect Term Extraction using Spiking Neural Network | arXiv:2601.06637v1 Announce Type: new Abstract: Aspect Term Extraction (ATE) identifies aspect terms in review sentences, a key subtask of sentiment analysis. While most existing approaches use energy-intensive deep neural networks (DNNs) for ATE as sequence labeling, this paper proposes a more energy-efficient alterna... | https://arxiv.org/abs/2601.06637 | Academic Papers | svg |
9e14bce610695836a657d5c15a1886dea5abf7dcf106e017991ce39d493c333d | 2026-01-13T00:00:00-05:00 | Attack-Resistant Watermarking for AIGC Image Forensics via Diffusion-based Semantic Deflection | arXiv:2601.06639v1 Announce Type: new Abstract: Protecting the copyright of user-generated AI images is an emerging challenge as AIGC becomes pervasive in creative workflows. Existing watermarking methods (1) remain vulnerable to real-world adversarial threats, often forced to trade off between defenses against spoofin... | https://arxiv.org/abs/2601.06639 | Academic Papers | svg |
cecf35c68c9732cf6b8d8c20d20c3dbd9029c65b0f88558f3b13a86f54170ef6 | 2026-01-13T00:00:00-05:00 | Agentic AI Empowered Intent-Based Networking for 6G | arXiv:2601.06640v1 Announce Type: new Abstract: The transition towards sixth-generation (6G) wireless networks necessitates autonomous orchestration mechanisms capable of translating high-level operational intents into executable network configurations. Existing approaches to Intent-Based Networking (IBN) rely upon eit... | https://arxiv.org/abs/2601.06640 | Academic Papers | svg |
548e3157b50876fe9f28667fb5dfd0ee4c501a74bf54daec971942e237eeefa5 | 2026-01-13T00:00:00-05:00 | Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning | arXiv:2601.06641v1 Announce Type: new Abstract: Membership inference attack (MIA) poses a significant privacy threat in federated learning (FL) as it allows adversaries to determine whether a client's private dataset contains a specific data sample. While defenses against membership inference attacks in standard FL hav... | https://arxiv.org/abs/2601.06641 | Academic Papers | svg |
58be013de97761503012be74a37cc22a823447bb3b2dc49cc8c5fc990cd686b9 | 2026-01-13T00:00:00-05:00 | Boosting Overlapping Organoid Instance Segmentation Using Pseudo-Label Unmixing and Synthesis-Assisted Learning | arXiv:2601.06642v1 Announce Type: new Abstract: Organoids, sophisticated in vitro models of human tissues, are crucial for medical research due to their ability to simulate organ functions and assess drug responses accurately. Accurate organoid instance segmentation is critical for quantifying their dynamic behaviors, ... | https://arxiv.org/abs/2601.06642 | Academic Papers | svg |
5fef45fab9916b01487ccf5d766749cfd18ed2c2feb075869c895ef69b85c88c | 2026-01-13T00:00:00-05:00 | Do Language Models Reason Across Languages? | arXiv:2601.06644v1 Announce Type: new Abstract: The real-world information sources are inherently multilingual, which naturally raises a question about whether language models can synthesize information across languages. In this paper, we introduce a simple two-hop question answering setting, where answering a question... | https://arxiv.org/abs/2601.06644 | Academic Papers | svg |
f65f92d460c6f642308a7cff309ca7e0aba89eccac6e76830b063420064d485a | 2026-01-13T00:00:00-05:00 | eSkiTB: A Synthetic Event-based Dataset for Tracking Skiers | arXiv:2601.06647v1 Announce Type: new Abstract: Tracking skiers in RGB broadcast footage is challenging due to motion blur, static overlays, and clutter that obscure the fast-moving athlete. Event cameras, with their asynchronous contrast sensing, offer natural robustness to such artifacts, yet a controlled benchmark f... | https://arxiv.org/abs/2601.06647 | Academic Papers | svg |
f2a5a69cef3d221f238a1406f912c45307e52c394825948aebbfaa24d568e515 | 2026-01-13T00:00:00-05:00 | Revisiting Training Scale: An Empirical Study of Token Count, Power Consumption, and Parameter Efficiency | arXiv:2601.06649v1 Announce Type: new Abstract: Research in machine learning has questioned whether increases in training token counts reliably produce proportional performance gains in large language models. Building on prior work introducing an energy-aware parameter efficiency metric, this study empirically examines... | https://arxiv.org/abs/2601.06649 | Academic Papers | svg |
46c7ea60e05cb0d5ce8a10b36223e3e8a9beb88cb80db2da28254c5786d21415 | 2026-01-13T00:00:00-05:00 | Learning Password Best Practices Through In-Task Instruction | arXiv:2601.06650v1 Announce Type: new Abstract: Users often make security- and privacy-relevant decisions without a clear understanding of the rules that govern safe behavior. We introduce pedagogical friction, a design approach that introduces brief, instructional interactions at the moment of action. We evaluate this... | https://arxiv.org/abs/2601.06650 | Academic Papers | svg |
0ce896e540ac08a23891ccaf4499aefb93d499f81ea303b476186608bfab8866 | 2026-01-13T00:00:00-05:00 | Follow the Signs: Using Textual Cues and LLMs to Guide Efficient Robot Navigation | arXiv:2601.06652v1 Announce Type: new Abstract: Autonomous navigation in unfamiliar environments often relies on geometric mapping and planning strategies that overlook rich semantic cues such as signs, room numbers, and textual labels. We propose a novel semantic navigation framework that leverages large language mode... | https://arxiv.org/abs/2601.06652 | Academic Papers | svg |
d7ad67511a8ea84b1dbcbfdde4e00b6594db907cf590c26d1c74c1348561c517 | 2026-01-13T00:00:00-05:00 | Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves | arXiv:2601.06655v1 Announce Type: new Abstract: A physics-constrained Gaussian Process regression framework is developed for predicting shocked material states along the Hugoniot curve using data from a small number of shockwave simulations. The proposed Gaussian process employs a probabilistic Taylor series expansion ... | https://arxiv.org/abs/2601.06655 | Academic Papers | svg |
43a00c86c97b802d824522079a07f5a6a4f0e41a82ca4eb7bca3d1b44e65f7e9 | 2026-01-13T00:00:00-05:00 | What makes for an enjoyable protagonist? An analysis of character warmth and competence | arXiv:2601.06658v1 Announce Type: new Abstract: Drawing on psychological and literary theory, we investigated whether the warmth and competence of movie protagonists predict IMDb ratings, and whether these effects vary across genres. Using 2,858 films and series from the Movie Scripts Corpus, we identified protagonists... | https://arxiv.org/abs/2601.06658 | Academic Papers | svg |
3756df0db791ccd86d8108098660a2811d5f49ac5af82f6bdf6033ad0293f21c | 2026-01-13T00:00:00-05:00 | SafePro: Evaluating the Safety of Professional-Level AI Agents | arXiv:2601.06663v1 Announce Type: new Abstract: Large language model-based agents are rapidly evolving from simple conversational assistants into autonomous systems capable of performing complex, professional-level tasks in various domains. While these advancements promise significant productivity gains, they also intr... | https://arxiv.org/abs/2601.06663 | Academic Papers | svg |
417dd5e2b71703a227aef1d1b94a8a952c9cdeeb08f5d1d537e65d95e460eb0d | 2026-01-13T00:00:00-05:00 | Reinforcement Learning-Guided Dynamic Multi-Graph Fusion for Evacuation Traffic Prediction | arXiv:2601.06664v1 Announce Type: new Abstract: Real-time traffic prediction is critical for managing transportation systems during hurricane evacuations. Although data-driven graph-learning models have demonstrated strong capabilities in capturing the complex spatiotemporal dynamics of evacuation traffic at a network ... | https://arxiv.org/abs/2601.06664 | Academic Papers | svg |
cabec06591337d3c6166c9ea286e52b64db516cb5ad4a7f000a1fb5d479a3144 | 2026-01-13T00:00:00-05:00 | InFi-Check: Interpretable and Fine-Grained Fact-Checking of LLMs | arXiv:2601.06666v1 Announce Type: new Abstract: Large language models (LLMs) often hallucinate, yet most existing fact-checking methods treat factuality evaluation as a binary classification problem, offering limited interpretability and failing to capture fine-grained error types. In this paper, we introduce InFi-Chec... | https://arxiv.org/abs/2601.06666 | Academic Papers | svg |
d93ade2ecdcb26401bb5b8bda0a0f4c43cea2f530ec2a6a25c4aec14a952b2ee | 2026-01-13T00:00:00-05:00 | zkRansomware: Proof-of-Data Recoverability and Multi-round Game Theoretic Modeling of Ransomware Decisions | arXiv:2601.06667v1 Announce Type: new Abstract: Ransomware is still one of the most serious cybersecurity threats. Victims often pay but fail to regain access to their data, while also facing the danger of losing data privacy. These uncertainties heavily shape the attacker-victim dynamics in decision-making. In this pa... | https://arxiv.org/abs/2601.06667 | Academic Papers | svg |
cd32770ea81645fa0a35b7f4fa79292b62cece13a4956a70c75f096c961c24fb | 2026-01-13T00:00:00-05:00 | Otimizando A Aloca\c{c}\~ao De Salas De Aula Com Foco Na Acessibilidade Para Pessoas Com Defici\^encia | arXiv:2601.06670v1 Announce Type: new Abstract: This paper addresses the challenge of classroom allocation in higher education institutions, with an explicit emphasis on accessibility for Persons with Disabilities (PwDs). Employing a case study of a university's computer science department, the paper proposes an Intege... | https://arxiv.org/abs/2601.06670 | Academic Papers | svg |
4aead134c539c5500c93b7408eb20f7c23df133a43a622dd31bf3e6cdb589d97 | 2026-01-13T00:00:00-05:00 | Will it Merge? On The Causes of Model Mergeability | arXiv:2601.06672v1 Announce Type: new Abstract: Model merging has emerged as a promising technique for combining multiple fine-tuned models into a single multitask model without retraining. However, the factors that determine whether merging will succeed or fail remain poorly understood. In this work, we investigate wh... | https://arxiv.org/abs/2601.06672 | Academic Papers | svg |
54c2dd4d8c9164653133b10a41960bde379d632cb989b6207d87fe0c02aef565 | 2026-01-13T00:00:00-05:00 | Quantification and Classification of Carbon Nanotubes in Electron Micrographs using Vision Foundation Models | arXiv:2601.06673v1 Announce Type: new Abstract: Accurate characterization of carbon nanotube morphologies in electron microscopy images is vital for exposure assessment and toxicological studies, yet current workflows rely on slow, subjective manual segmentation. This work presents a unified framework leveraging vision... | https://arxiv.org/abs/2601.06673 | Academic Papers | svg |
5c526b21933274f98cbd0001951a71486b557ebdda8d0cccfe38b987b48a0ce5 | 2026-01-13T00:00:00-05:00 | Evaluating Cross-Lingual Unlearning in Multilingual Language Models | arXiv:2601.06675v1 Announce Type: new Abstract: We present the first comprehensive evaluation of cross-lingual unlearning in multilingual LLMs. Using translated TOFU benchmarks in seven language/script variants, we test major unlearning algorithms and show that most fail to remove facts outside the training language, e... | https://arxiv.org/abs/2601.06675 | Academic Papers | svg |
d188c8b574e23d328b52b3d365c59ce24d207d43a1c4f2524c23310e7d5411f5 | 2026-01-13T00:00:00-05:00 | IDRBench: Interactive Deep Research Benchmark | arXiv:2601.06676v1 Announce Type: new Abstract: Deep research agents powered by Large Language Models (LLMs) can perform multi-step reasoning, web exploration, and long-form report generation. However, most existing systems operate in an autonomous manner, assuming fully specified user intent and evaluating only final ... | https://arxiv.org/abs/2601.06676 | Academic Papers | svg |
77827f66b0889210a30a4fd7f2788498119c50805609220f18668584e1c85b04 | 2026-01-13T00:00:00-05:00 | Plasticity vs. Rigidity: The Impact of Low-Rank Adapters on Reasoning on a Micro-Budget | arXiv:2601.06677v1 Announce Type: new Abstract: Recent advances in mathematical reasoning typically rely on massive scale, yet the question remains: can strong reasoning capabilities be induced in small language models ($\leq1.5\text{B}$) under extreme constraints? We investigate this by training models on a single A40... | https://arxiv.org/abs/2601.06677 | Academic Papers | svg |
4c9593b4c85b36af623f7037abd6dc427d6e40b8a4b695d84b2416adf04ad57c | 2026-01-13T00:00:00-05:00 | Reflective Reasoning for SQL Generation | arXiv:2601.06678v1 Announce Type: new Abstract: Robust text-to-SQL over complex, real-world databases remains brittle even with modern LLMs: iterative refinement often introduces syntactic and semantic drift, corrections tend to be non-transferable across queries, and naive use of large context windows scales poorly. W... | https://arxiv.org/abs/2601.06678 | Academic Papers | svg |
28edbb52b917f5ec95948d842ab0257d7b2108375c1d3dd8e50a524c06981c14 | 2026-01-13T00:00:00-05:00 | A Power Electronic Converter Control Framework Based on Graph Neural Networks - An Early Proof-of-Concept | arXiv:2601.06686v1 Announce Type: new Abstract: Power electronic converter control is typically tuned per topology, limiting transfer across heterogeneous designs. This letter proposes a topology-agnostic meta-control framework that encodes converter netlists as typed bipartite graphs and uses a task-conditioned graph ... | https://arxiv.org/abs/2601.06686 | Academic Papers | svg |
00ae7e98fabef9446d27ee3cfe4b6de377d287a8ed89ad8e5ae5b083d42dda3c | 2026-01-13T00:00:00-05:00 | The Case for Strategic Data Stewardship: Re-imagining Data Governance to Make Responsible Data Re-use Possible | arXiv:2601.06687v1 Announce Type: new Abstract: As societal challenges grow more complex, access to data for public interest use is paradoxically becoming more constrained. This emerging data winter is not simply a matter of scarcity, but of shrinking legitimate and trusted pathways for responsible data reuse. Concerns... | https://arxiv.org/abs/2601.06687 | Academic Papers | svg |
8d34686ef96bceebd1b1802c667aa15b1c6758d83cdf068a0c36eb09010c8cfb | 2026-01-13T00:00:00-05:00 | The Sample Complexity of Lossless Data Compression | arXiv:2601.06688v1 Announce Type: new Abstract: A new framework is introduced for examining and evaluating the fundamental limits of lossless data compression, that emphasizes genuinely non-asymptotic results. The {\em sample complexity} of compressing a given source is defined as the smallest blocklength at which it i... | https://arxiv.org/abs/2601.06688 | Academic Papers | svg |
e627f4b483fb3476ada168c552e1e8b25e6b191f0151bc0f30538aac95379bee | 2026-01-13T00:00:00-05:00 | An Exploratory Pilot Survey on Technical Quality Control Practices in Agile R&D Projects | arXiv:2601.06689v1 Announce Type: new Abstract: Managing technical quality in agile Research and Development (R&D) software projects represents a persistent challenge, particularly in contexts characterized by high technical uncertainty and experimental pressure. This exploratory pilot survey explores how agile R&a... | https://arxiv.org/abs/2601.06689 | Academic Papers | svg |
467f7f6c801ea833047d9d1f25e94a636ee2c8d07b7f3aca26eeececc11ba53b | 2026-01-13T00:00:00-05:00 | S-DAPT-2026: A Stage-Aware Synthetic Dataset for Advanced Persistent Threat Detection | arXiv:2601.06690v1 Announce Type: new Abstract: The detection of advanced persistent threats (APTs) remains a crucial challenge due to their stealthy, multistage nature and the limited availability of realistic, labeled datasets for systematic evaluation. Synthetic dataset generation has emerged as a practical approach... | https://arxiv.org/abs/2601.06690 | Academic Papers | svg |
a55fb17df46c2c4d422d5f62b38f68367f432fe31915a5eae4dc82aedccf2c5b | 2026-01-13T00:00:00-05:00 | The Axiom of Consent: Friction Dynamics in Multi-Agent Coordination | arXiv:2601.06692v1 Announce Type: new Abstract: Multi-agent systems face a fundamental coordination problem: agents must coordinate despite heterogeneous preferences, asymmetric stakes, and imperfect information. When coordination fails, friction emerges: measurable resistance manifesting as deadlock, thrashing, commun... | https://arxiv.org/abs/2601.06692 | Academic Papers | svg |
fa166f9d243038753e1af2177c1db9f200970d8b4ca462efacb9b1e02b142d03 | 2026-01-13T00:00:00-05:00 | Incentive Mechanism Design for Privacy-Preserving Decentralized Blockchain Relayers | arXiv:2601.06699v1 Announce Type: new Abstract: Public blockchains, though renowned for their transparency and immutability, suffer from significant privacy concerns. Network-level analysis and long-term observation of publicly available transactions can often be used to infer user identities. To mitigate this, several... | https://arxiv.org/abs/2601.06699 | Academic Papers | svg |
f7c545f363edbf483fcac7cc26f767babbcd6319cc32a69f2f833266f9f7fd94 | 2026-01-13T00:00:00-05:00 | Characterising Toxicity in Generative Large Language Models | arXiv:2601.06700v1 Announce Type: new Abstract: In recent years, the advent of the attention mechanism has significantly advanced the field of natural language processing (NLP), revolutionizing text processing and text generation. This has come about through transformer-based decoder-only architectures, which have beco... | https://arxiv.org/abs/2601.06700 | Academic Papers | svg |
4043feb012d80e250a1b4282696d2eed159e1ff8885bd9b2edfe4ab2bfd2596a | 2026-01-13T00:00:00-05:00 | Explainability of Complex AI Models with Correlation Impact Ratio | arXiv:2601.06701v1 Announce Type: new Abstract: Complex AI systems make better predictions but often lack transparency, limiting trustworthiness, interpretability, and safe deployment. Common post hoc AI explainers, such as LIME, SHAP, HSIC, and SAGE, are model agnostic but are too restricted in one significant regard:... | https://arxiv.org/abs/2601.06701 | Academic Papers | svg |
78a781d3146e03fecc8cd7730182d6087e7620f4e47c182abcd26d8573675e11 | 2026-01-13T00:00:00-05:00 | GRASP LoRA: GRPO Guided Adapter Sparsity Policy for Cross Lingual Transfer | arXiv:2601.06702v1 Announce Type: new Abstract: Parameter efficient fine tuning is a way to adapt LLMs to new languages when compute or data are limited, yet adapter pipelines usually choose a global prune ratio by grid search. This practice is computationally expensive and development set intensive, since it repeats t... | https://arxiv.org/abs/2601.06702 | Academic Papers | svg |
a25a7b984b8d98d5ece79eaa6a03f796ac17c372820ea3aa7c247384294f9605 | 2026-01-13T00:00:00-05:00 | Mapping and Comparing Climate Equity Policy Practices Using RAG LLM-Based Semantic Analysis and Recommendation Systems | arXiv:2601.06703v1 Announce Type: new Abstract: This study investigates the use of large language models to enhance the policymaking process. We first analyze planning-related job postings to revisit the evolving roles of planners in the era of AI. We then examine climate equity plans across the U.S. and apply ChatGPT ... | https://arxiv.org/abs/2601.06703 | Academic Papers | svg |
ef50194ad1c14b00f270a8917f07d0cd5a77a823eb36432f9882d2fa82caf05d | 2026-01-13T00:00:00-05:00 | Beyond Perfect Scores: Proof-by-Contradiction for Trustworthy Machine Learning | arXiv:2601.06704v1 Announce Type: new Abstract: Machine learning (ML) models show strong promise for new biomedical prediction tasks, but concerns about trustworthiness have hindered their clinical adoption. In particular, it is often unclear whether a model relies on true clinical cues or on spurious hierarchical corr... | https://arxiv.org/abs/2601.06704 | Academic Papers | svg |
116304c1480064f9b2ea7e967ad75d02ea2a70301419a62603c7aaac730415be | 2026-01-13T00:00:00-05:00 | Algorithm Support for Graph Databases, Done Right | arXiv:2601.06705v1 Announce Type: new Abstract: Graph database query languages cannot express algorithms like PageRank, forcing costly data wrangling, while existing solutions such as algorithm libraries, vertex-centric APIs, and recursive CTEs lack the necessary combination of expressiveness, performance, and usabilit... | https://arxiv.org/abs/2601.06705 | Academic Papers | svg |
263652e7f961b94224d48799159d483946376784a44920e25705dd60bcc45ba6 | 2026-01-13T00:00:00-05:00 | Resource-Aware Task Allocator Design: Insights and Recommendations for Distributed Satellite Constellations | arXiv:2601.06706v1 Announce Type: new Abstract: We present the design of a Resource-Aware Task Allocator (RATA) and an empirical analysis in handling real-time tasks for processing on Distributed Satellite Systems (DSS). We consider task processing performance across low Earth orbit (LEO) to Low-Medium Earth Orbit (Low... | https://arxiv.org/abs/2601.06706 | Academic Papers | svg |
c8da53fd25de2099d9d611881f8c8d3957b94fabf39f6491fdf8b09948b6b890 | 2026-01-13T00:00:00-05:00 | Evaluating Accounting Reasoning Capabilities of Large Language Models | arXiv:2601.06707v1 Announce Type: new Abstract: Large language models are transforming learning, cognition, and research across many fields. Effectively integrating them into professional domains, such as accounting, is a key challenge for enterprise digital transformation. To address this, we define vertical domain ac... | https://arxiv.org/abs/2601.06707 | Academic Papers | svg |
2c53bc315e81094e969bf9a6f2e3603376a76cd9d094d6d1182266d7129e9ae5 | 2026-01-13T00:00:00-05:00 | Behavioral Analytics for Continuous Insider Threat Detection in Zero-Trust Architectures | arXiv:2601.06708v1 Announce Type: new Abstract: Insider threats are a particularly tricky cybersecurity issue, especially in zero-trust architectures (ZTA) where implicit trust is removed. Although the rule of thumb is never trust, always verify, attackers can still use legitimate credentials and impersonate the standa... | https://arxiv.org/abs/2601.06708 | Academic Papers | svg |
27620b5fc72aef32cf088fee9856e9045fe3f06f5aafa96874ddc3944a2d8e22 | 2026-01-13T00:00:00-05:00 | Privacy-Preserving Data Processing in Cloud : From Homomorphic Encryption to Federated Analytics | arXiv:2601.06710v1 Announce Type: new Abstract: Privacy-preserving data processing refers to the methods and models that allow computing and analyzing sensitive data with a guarantee of confidentiality. As cloud computing and applications that rely on data continue to expand, there is an increasing need to protect pers... | https://arxiv.org/abs/2601.06710 | Academic Papers | svg |
8ab61f3c4f25a0cafb113213e0a456f2881d6c3d3b2bb3e78a32d7c1892c622f | 2026-01-13T00:00:00-05:00 | FO-Complete Program Verification for Heap Logics | arXiv:2601.06719v1 Announce Type: new Abstract: We develop the first two heap logics that have implicit heaplets and that admit FO-complete program verification. The notion of FO-completeness is a theoretical guarantee that all theorems that are valid when recursive definitions are interpreted as fixpoint definitions (... | https://arxiv.org/abs/2601.06719 | Academic Papers | svg |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.