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cfe8b8392644351f481cd798316f71e6e4051f3705f9c82d199063d1b4e3ada3 | 2026-02-02T00:00:00-05:00 | Compressed Set Representations based on Set Difference | arXiv:2601.23240v1 Announce Type: new Abstract: We introduce a compressed representation of sets of sets that exploits how much they differ from each other. Our representation supports access, membership, predecessor and successor queries on the sets within logarithmic time. In addition, we give a new MST-based constru... | https://arxiv.org/abs/2601.23240 | Academic Papers | svg |
3866de8181e4476264b8203b157f622437115afccbcb96265fbe9f81bc747051 | 2026-02-02T00:00:00-05:00 | A Primal-Dual Level Set Method for Computing Geodesic Distances | arXiv:2601.23244v1 Announce Type: new Abstract: The numerical computation of shortest paths or geodesics on surfaces, along with the associated geodesic distance, has a wide range of applications. Compared to Euclidean distance computation, these tasks are more complex due to the influence of surface geometry on the be... | https://arxiv.org/abs/2601.23244 | Academic Papers | svg |
9d0ee7d868242ad2dbbbc9bb2597990ff160e1e99712afb6374e94e48567f52b | 2026-02-02T00:00:00-05:00 | The Iterated Local Model for tournaments | arXiv:2601.23246v1 Announce Type: new Abstract: Transitivity is a central, generative principle in social and other complex networks, capturing the tendency for two nodes with a common neighbor to form a direct connection. We propose a new model for highly dense, complex networks based on transitivity, called the Itera... | https://arxiv.org/abs/2601.23246 | Academic Papers | svg |
de633812189a0f49165412966ad6c1293ab00b7b4782a51b7b957731f151eaa0 | 2026-02-02T00:00:00-05:00 | (Doubly) Exponential Lower Bounds for Follow the Regularized Leader in Potential Games | arXiv:2601.23248v1 Announce Type: new Abstract: Follow the regularized leader FTRL is the premier algorithm for online optimization. However, despite decades of research on its convergence in constrained optimization -- and potential games in particular -- its behavior remained hitherto poorly understood. In this paper... | https://arxiv.org/abs/2601.23248 | Academic Papers | svg |
fb5ea75b7cb479fbe8fac8b078b0ba9e8b39630ca05148d8a44dce5048ec4bdb | 2026-02-02T00:00:00-05:00 | Structured Over Scale: Learning Spatial Reasoning from Educational Video | arXiv:2601.23251v1 Announce Type: new Abstract: Vision-language models (VLMs) demonstrate impressive performance on standard video understanding benchmarks yet fail systematically on simple reasoning tasks that preschool children can solve, including counting, spatial reasoning, and compositional understanding. We hypo... | https://arxiv.org/abs/2601.23251 | Academic Papers | svg |
7beef4990dcfb6c568fe75874cf59c95939c6e649fe947dac303764b57d17ca9 | 2026-02-02T00:00:00-05:00 | Training-Free Test-Time Adaptation with Brownian Distance Covariance in Vision-Language Models | arXiv:2601.23253v1 Announce Type: new Abstract: Vision-language models suffer performance degradation under domain shift, limiting real-world applicability. Existing test-time adaptation methods are computationally intensive, rely on back-propagation, and often focus on single modalities. To address these issues, we pr... | https://arxiv.org/abs/2601.23253 | Academic Papers | svg |
a8e8246f7ca8e6b4c484055e2c2173b9710a7990fbbe0c7e2371b8a819a2dae9 | 2026-02-02T00:00:00-05:00 | GrepRAG: An Empirical Study and Optimization of Grep-Like Retrieval for Code Completion | arXiv:2601.23254v1 Announce Type: new Abstract: Repository-level code completion remains challenging for large language models (LLMs) due to cross-file dependencies and limited context windows. Prior work addresses this challenge using Retrieval-Augmented Generation (RAG) frameworks based on semantic indexing or struct... | https://arxiv.org/abs/2601.23254 | Academic Papers | svg |
0d79c20919640bf15bd13adf1b272ad468b499487450bda301c549f853603f90 | 2026-02-02T00:00:00-05:00 | Now You Hear Me: Audio Narrative Attacks Against Large Audio-Language Models | arXiv:2601.23255v1 Announce Type: new Abstract: Large audio-language models increasingly operate on raw speech inputs, enabling more seamless integration across domains such as voice assistants, education, and clinical triage. This transition, however, introduces a distinct class of vulnerabilities that remain largely ... | https://arxiv.org/abs/2601.23255 | Academic Papers | svg |
0181d726fde38b0c3da44f2a7f4adc841b8a542ec3884c005bee810ad0735b16 | 2026-02-02T00:00:00-05:00 | Outcome-Conditioned Reasoning Distillation for Resolving Software Issues | arXiv:2601.23257v1 Announce Type: new Abstract: Software issue resolution in large repositories is a long-range decision process: choices made during localization shape the space of viable edits, and missteps can compound into incorrect patches. Despite this, many LLM-based repair pipelines still operate in a reset-and... | https://arxiv.org/abs/2601.23257 | Academic Papers | svg |
e02c7427248db2f989b2530bcf879e72d377e0b2e92419ce4452b09cc210413c | 2026-02-02T00:00:00-05:00 | Agnostic Language Identification and Generation | arXiv:2601.23258v1 Announce Type: new Abstract: Recent works on language identification and generation have established tight statistical rates at which these tasks can be achieved. These works typically operate under a strong realizability assumption: that the input data is drawn from an unknown distribution necessari... | https://arxiv.org/abs/2601.23258 | Academic Papers | svg |
19285de5350025b6f0004a2db1ed86e861d28a9e75a86987f6c17595e58ebd65 | 2026-02-02T00:00:00-05:00 | TEON: Tensorized Orthonormalization Beyond Layer-Wise Muon for Large Language Model Pre-Training | arXiv:2601.23261v1 Announce Type: new Abstract: The Muon optimizer has demonstrated strong empirical performance in pre-training large language models by performing matrix-level gradient (or momentum) orthogonalization in each layer independently. In this work, we propose TEON, a principled generalization of Muon that ... | https://arxiv.org/abs/2601.23261 | Academic Papers | svg |
691c7e7b6d247cd13300b09fa8982c156131f59a7cf6556036540cfad0635675 | 2026-02-02T00:00:00-05:00 | Particle-Guided Diffusion Models for Partial Differential Equations | arXiv:2601.23262v1 Announce Type: new Abstract: We introduce a guided stochastic sampling method that augments sampling from diffusion models with physics-based guidance derived from partial differential equation (PDE) residuals and observational constraints, ensuring generated samples remain physically admissible. We ... | https://arxiv.org/abs/2601.23262 | Academic Papers | svg |
0a0c322f4e97d44cd6c13e01440005ce2b4ce0f70c1f1d1e15db1cf401005bc8 | 2026-02-02T00:00:00-05:00 | PaperBanana: Automating Academic Illustration for AI Scientists | arXiv:2601.23265v1 Announce Type: new Abstract: Despite rapid advances in autonomous AI scientists powered by language models, generating publication-ready illustrations remains a labor-intensive bottleneck in the research workflow. To lift this burden, we introduce PaperBanana, an agentic framework for automated gener... | https://arxiv.org/abs/2601.23265 | Academic Papers | svg |
68f0b19baf3bc7f9edf264789a0b74877edf6a8420e9ac4383aedc05b81334b3 | 2026-02-02T00:00:00-05:00 | IRL-DAL: Safe and Adaptive Trajectory Planning for Autonomous Driving via Energy-Guided Diffusion Models | arXiv:2601.23266v1 Announce Type: new Abstract: This paper proposes a novel inverse reinforcement learning framework using a diffusion-based adaptive lookahead planner (IRL-DAL) for autonomous vehicles. Training begins with imitation from an expert finite state machine (FSM) controller to provide a stable initializatio... | https://arxiv.org/abs/2601.23266 | Academic Papers | svg |
d159b7a4c306bd22656cc2dd88e66ba1b4996b228e9c998ebfda76eddd0b2190 | 2026-02-02T00:00:00-05:00 | TCBench: A Benchmark for Tropical Cyclone Track and Intensity Forecasting at the Global Scale | arXiv:2601.23268v1 Announce Type: new Abstract: TCBench is a benchmark for evaluating global, short to medium-range (1-5 days) forecasts of tropical cyclone (TC) track and intensity. To allow a fair and model-agnostic comparison, TCBench builds on the IBTrACS observational dataset and formulates TC forecasting as predi... | https://arxiv.org/abs/2601.23268 | Academic Papers | svg |
50f1b86c34c3295347834043f7fc15d129ce1a33f2d8f20ee2b24f11c60c0f8c | 2026-02-02T00:00:00-05:00 | Rank Reduction AutoEncoders for Mechanical Design: Advancing Novel and Efficient Data-Driven Topology Optimization | arXiv:2601.23269v1 Announce Type: new Abstract: This work presents a data-driven framework for fast forward and inverse analysis in topology optimization (TO) by combining Rank Reduction Autoencoders (RRAEs) with neural latent-space mappings. The methodology targets the efficient approximation of the relationship betwe... | https://arxiv.org/abs/2601.23269 | Academic Papers | svg |
51de9f834f1c72bc36dad89a70554868f9eff712c4961c851e9beb358e0add92 | 2026-02-02T00:00:00-05:00 | UPA: Unsupervised Prompt Agent via Tree-Based Search and Selection | arXiv:2601.23273v1 Announce Type: new Abstract: Prompt agents have recently emerged as a promising paradigm for automated prompt optimization, framing refinement as a sequential decision-making problem over a structured prompt space. While this formulation enables the use of advanced planning algorithms, these methods ... | https://arxiv.org/abs/2601.23273 | Academic Papers | svg |
08accf25c5bcc689d66879228d692b0a6c83db842aab46dda3875e5e938a3a59 | 2026-02-02T00:00:00-05:00 | FOCUS: DLLMs Know How to Tame Their Compute Bound | arXiv:2601.23278v1 Announce Type: new Abstract: Diffusion Large Language Models (DLLMs) offer a compelling alternative to Auto-Regressive models, but their deployment is constrained by high decoding cost. In this work, we identify a key inefficiency in DLLM decoding: while computation is parallelized over token blocks,... | https://arxiv.org/abs/2601.23278 | Academic Papers | svg |
31fa2355ce6e55c774bdf4d3b0dd459ed9d7817bd930ddf53e292b9ab4a20b45 | 2026-02-02T00:00:00-05:00 | Decoupled Diffusion Sampling for Inverse Problems on Function Spaces | arXiv:2601.23280v1 Announce Type: new Abstract: We propose a data-efficient, physics-aware generative framework in function space for inverse PDE problems. Existing plug-and-play diffusion posterior samplers represent physics implicitly through joint coefficient-solution modeling, requiring substantial paired supervisi... | https://arxiv.org/abs/2601.23280 | Academic Papers | svg |
7dd5b1dcc1c49180886b18c726bf9bc7106466385acd934822f2c332ee186128 | 2026-02-02T00:00:00-05:00 | User Prompting Strategies and Prompt Enhancement Methods for Open-Set Object Detection in XR Environments | arXiv:2601.23281v1 Announce Type: new Abstract: Open-set object detection (OSOD) localizes objects while identifying and rejecting unknown classes at inference. While recent OSOD models perform well on benchmarks, their behavior under realistic user prompting remains underexplored. In interactive XR settings, user-gene... | https://arxiv.org/abs/2601.23281 | Academic Papers | svg |
427b6b178cb70d409513e191fc0123ee8375d56ba73493c68b620af850eabad8 | 2026-02-02T00:00:00-05:00 | End-to-end Optimization of Belief and Policy Learning in Shared Autonomy Paradigms | arXiv:2601.23285v1 Announce Type: new Abstract: Shared autonomy systems require principled methods for inferring user intent and determining appropriate assistance levels. This is a central challenge in human-robot interaction, where systems must be successful while being mindful of user agency. Previous approaches rel... | https://arxiv.org/abs/2601.23285 | Academic Papers | svg |
b4612cb41a8007f5292ae225da280529a7636a505b47f0bdfed2db33615f11b9 | 2026-02-02T00:00:00-05:00 | VideoGPA: Distilling Geometry Priors for 3D-Consistent Video Generation | arXiv:2601.23286v1 Announce Type: new Abstract: While recent video diffusion models (VDMs) produce visually impressive results, they fundamentally struggle to maintain 3D structural consistency, often resulting in object deformation or spatial drift. We hypothesize that these failures arise because standard denoising o... | https://arxiv.org/abs/2601.23286 | Academic Papers | svg |
2bf1247eb48e36797ef254a3df09d023521d03d5f8f6897a3ebf8acc88f11249 | 2026-02-02T00:00:00-05:00 | Smart Routing with Precise Link Estimation: DSEE-Based Anypath Routing for Reliable Wireless Networking | arXiv:2405.10377v1 Announce Type: cross Abstract: In dynamic and resource-constrained environments, such as multi-hop wireless mesh networks, traditional routing protocols often falter by relying on predetermined paths that prove ineffective in unpredictable link conditions. Shortest Anypath routing offers a solution b... | https://arxiv.org/abs/2405.10377 | Academic Papers | svg |
2cb8cdeec265911ed570153c2633c783622a768a91276e6d578fbebe21243782 | 2026-02-02T00:00:00-05:00 | Deep Lightweight Unrolled Network for High Dynamic Range Modulo Imaging | arXiv:2601.12526v1 Announce Type: cross Abstract: Modulo-Imaging (MI) offers a promising alternative for expanding the dynamic range of images by resetting the signal intensity when it reaches the saturation level. Subsequently, high-dynamic range (HDR) modulo imaging requires a recovery process to obtain the HDR image... | https://arxiv.org/abs/2601.12526 | Academic Papers | svg |
d0e735909ca49f1cb7724acba3acfc14c3e64e0c225f3f73ebe04ae82d423f05 | 2026-02-02T00:00:00-05:00 | Formalization of non-Archimedean functional analysis 1: spherically complete spaces | arXiv:2601.21734v1 Announce Type: cross Abstract: In this article, we present a formalization of spherically complete spaces, which is a fundamental notion in non-archimedean functional analysis. This work includes the equivalent definitions of spherically complete spaces, their basic properties, examples and non-examp... | https://arxiv.org/abs/2601.21734 | Academic Papers | svg |
dacb6051b5f7b700bcba29afdffd288519917ecee381c13df6df4d7c21e40e3a | 2026-02-02T00:00:00-05:00 | UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos | arXiv:2601.22162v1 Announce Type: cross Abstract: Multimodal large language models are playing an increasingly significant role in empowering the financial domain, however, the challenges they face, such as multimodal and high-density information and cross-modal multi-hop reasoning, go beyond the evaluation scope of ex... | https://arxiv.org/abs/2601.22162 | Academic Papers | svg |
83c50ffe8b13bca287fbe4b8e2c846fddc268d6fe1e3b9d83214bd6e957bc93b | 2026-02-02T00:00:00-05:00 | Stablecoin Design with Adversarial-Robust Multi-Agent Systems via Trust-Weighted Signal Aggregation | arXiv:2601.22168v1 Announce Type: cross Abstract: Algorithmic stablecoins promise decentralized monetary stability by maintaining a target peg through programmatic reserve management. Yet, their reserve controllers remain vulnerable to regime-blind optimization, calibrating risk parameters on fair-weather data while ig... | https://arxiv.org/abs/2601.22168 | Academic Papers | svg |
e459246573e6defa0197581e5f5bec659148bf5fb7e05c0c6220b7dbfe07108c | 2026-02-02T00:00:00-05:00 | Proliferating series by Jean Barraqu\'e: a study and classification in mathematical terms | arXiv:2601.22176v1 Announce Type: cross Abstract: Barraqu\'e's proliferating series give an interesting turn on the concept of classic serialism by creating a new invariant when it comes to constructing the series: rather than the intervals between consecutive notes, what remains unaltered during the construction of th... | https://arxiv.org/abs/2601.22176 | Academic Papers | svg |
c04f3c682cc220ea660c53724139c9ab303845a1dc386e8aee942d7ef3a67219 | 2026-02-02T00:00:00-05:00 | SCENE: Semantic-aware Codec Enhancement with Neural Embeddings | arXiv:2601.22189v1 Announce Type: cross Abstract: Compression artifacts from standard video codecs often degrade perceptual quality. We propose a lightweight, semantic-aware pre-processing framework that enhances perceptual fidelity by selectively addressing these distortions. Our method integrates semantic embeddings ... | https://arxiv.org/abs/2601.22189 | Academic Papers | svg |
313055fe2bfe7822f04cf78bbd091e92d88378ef80b91906b86f7f0b3e334bc0 | 2026-02-02T00:00:00-05:00 | Practical Evaluation of Quantum Kernel Methods for Radar Micro-Doppler Classification on Noisy Intermediate-Scale Quantum (NISQ) Hardware | arXiv:2601.22194v1 Announce Type: cross Abstract: This paper examines the application of a Quantum Support Vector Machine (QSVM) for radarbased aerial target classification using micro-Doppler signatures. Classical features are extracted and reduced via Principal Component Analysis (PCA) to enable efficient quantum enc... | https://arxiv.org/abs/2601.22194 | Academic Papers | svg |
48f56311c7cdde359f3d00ffa6ccbd8d03fd3cfc7762690a033bd5a7f86e3276 | 2026-02-02T00:00:00-05:00 | Adaptive Benign Overfitting (ABO): Overparameterized RLS for Online Learning in Non-stationary Time-series | arXiv:2601.22200v1 Announce Type: cross Abstract: Overparameterized models have recently challenged conventional learning theory by exhibiting improved generalization beyond the interpolation limit, a phenomenon known as benign overfitting. This work introduces Adaptive Benign Overfitting (ABO), extending the recursive... | https://arxiv.org/abs/2601.22200 | Academic Papers | svg |
f9d95c3ed97f0dacfda938692084cad2c22d19c73278efd0214e64fb95a2a4ba | 2026-02-02T00:00:00-05:00 | A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications | arXiv:2601.22202v1 Announce Type: cross Abstract: Semantic communication (SemCom) emerges as a transformative paradigm for traffic-intensive visual data transmission, shifting focus from raw data to meaningful content transmission and relieving the increasing pressure on communication resources. However, to achieve Sem... | https://arxiv.org/abs/2601.22202 | Academic Papers | svg |
14a961628ede23bd0ebf5795e5ba3b72ff00b58f3337ab9c52944ab0d4e43cb7 | 2026-02-02T00:00:00-05:00 | Beyond Conditional Computation: Retrieval-Augmented Genomic Foundation Models with Gengram | arXiv:2601.22203v1 Announce Type: cross Abstract: Current genomic foundation models (GFMs) rely on extensive neural computation to implicitly approximate conserved biological motifs from single-nucleotide inputs. We propose Gengram, a conditional memory module that introduces an explicit and highly efficient lookup pri... | https://arxiv.org/abs/2601.22203 | Academic Papers | svg |
ef23ada01575e03cb45a7efffaf8033b892fe8a1ea260e942b3c4f25a07a571b | 2026-02-02T00:00:00-05:00 | Transitive Sets of Mutually Orthogonal Latin Squares | arXiv:2601.22205v1 Announce Type: cross Abstract: We investigate MacNeish's conjecture (known to be false in general) in the setting of what we call "transitive" Mutually Orthogonal Latin Squares (MOLS). When we restrict our attention to "simply transitive" MOLS, we find that the conjecture holds. We provide some parti... | https://arxiv.org/abs/2601.22205 | Academic Papers | svg |
0194d9c46043e8ece845b6123973bbadb719696a545879e9259b277c34d26629 | 2026-02-02T00:00:00-05:00 | Forecasting in the presence of scale-free noise | arXiv:2601.22294v1 Announce Type: cross Abstract: The extraction of signals from noise is a common problem in all areas of science and engineering. A particularly useful version is that of forecasting: determining a causal filter that estimates a future value of a hidden process from past observations. Current techniqu... | https://arxiv.org/abs/2601.22294 | Academic Papers | svg |
2c140e4ec178f20ba53c459690a64d7ec9607ae8e9dcadbb422a80e6bbda96a7 | 2026-02-02T00:00:00-05:00 | Sylber 2.0: A Universal Syllable Embedding | arXiv:2601.22306v1 Announce Type: cross Abstract: Scaling spoken language modeling requires speech tokens that are both efficient and universal. Recent work has proposed syllables as promising speech tokens at low temporal resolution, but existing models are constrained to English and fail to capture sufficient acousti... | https://arxiv.org/abs/2601.22306 | Academic Papers | svg |
61d02f0e9ffafccae37ec69afc56506fe2c420542ea714e292ec99aeb0ad482e | 2026-02-02T00:00:00-05:00 | Dependence-Aware Label Aggregation for LLM-as-a-Judge via Ising Models | arXiv:2601.22336v1 Announce Type: cross Abstract: Large-scale AI evaluation increasingly relies on aggregating binary judgments from $K$ annotators, including LLMs used as judges. Most classical methods, e.g., Dawid-Skene or (weighted) majority voting, assume annotators are conditionally independent given the true labe... | https://arxiv.org/abs/2601.22336 | Academic Papers | svg |
457a144c9faca7284410a69fad19f1226a4f146e972db9a457132cc29d2a8f3e | 2026-02-02T00:00:00-05:00 | Quaternionic Perfect Sequences and Hadamard Matrices | arXiv:2601.22337v1 Announce Type: cross Abstract: A finite sequence of numbers is perfect if it has zero periodic autocorrelation after a nontrivial cyclic shift. In this work, we study quaternionic perfect sequences having a one-to-one correspondence with the binary sequences arising in Williamson's construction of qu... | https://arxiv.org/abs/2601.22337 | Academic Papers | svg |
2814f401c145d7fa98e7b61326dab6d43537378216698882604c6bece4d44dbc | 2026-02-02T00:00:00-05:00 | Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation | arXiv:2601.22367v1 Announce Type: cross Abstract: Generalized Bayesian Inference (GBI) tempers a loss with a temperature $\beta>0$ to mitigate overconfidence and improve robustness under model misspecification, but existing GBI methods typically rely on costly MCMC or SDE-based samplers and must be re-run for each new ... | https://arxiv.org/abs/2601.22367 | Academic Papers | svg |
59288755ed2207518b7e44fda46efc82f7851fe5edc453d9836be7181c4879b9 | 2026-02-02T00:00:00-05:00 | It's all the (Exponential) Family: An Equivalence between Maximum Likelihood Estimation and Control Variates for Sketching Algorithms | arXiv:2601.22378v1 Announce Type: cross Abstract: Maximum likelihood estimators (MLE) and control variate estimators (CVE) have been used in conjunction with known information across sketching algorithms and applications in machine learning. We prove that under certain conditions in an exponential family, an optimal CV... | https://arxiv.org/abs/2601.22378 | Academic Papers | svg |
2a5170a05374dccc340b2c1881cad011d3071565b80c1b94074fdc3aaa11ccde | 2026-02-02T00:00:00-05:00 | Spectral Filtering for Learning Quantum Dynamics | arXiv:2601.22400v1 Announce Type: cross Abstract: Learning high-dimensional quantum systems is a fundamental challenge that notoriously suffers from the curse of dimensionality. We formulate the task of predicting quantum evolution in the linear response regime as a specific instance of learning a Complex-Valued Linear... | https://arxiv.org/abs/2601.22400 | Academic Papers | svg |
1c2defeceb41fa8a4ee2cac19f5fd3e35a37377eb74fa47f3c2fd9a5d31a80c5 | 2026-02-02T00:00:00-05:00 | Minimal-Action Discrete Schr\"odinger Bridge Matching for Peptide Sequence Design | arXiv:2601.22408v1 Announce Type: cross Abstract: Generative modeling of peptide sequences requires navigating a discrete and highly constrained space in which many intermediate states are chemically implausible or unstable. Existing discrete diffusion and flow-based methods rely on reversing fixed corruption processes... | https://arxiv.org/abs/2601.22408 | Academic Papers | svg |
15f37743799b579a015244e30464bcc7790ad38661c5f88bb727ca2ccd7f43fe | 2026-02-02T00:00:00-05:00 | On the computability of cofinal Fra\"iss\'e limits | arXiv:2601.22435v1 Announce Type: cross Abstract: For any collection of finite structures closed under isomorphism (i.e., an age) which has the Hereditary Property (HP), the Joint Embedding Property (JEP), and the Cofinal Amalgamation Property (CAP), there is a unique (up to isomorphism) countable structure which is co... | https://arxiv.org/abs/2601.22435 | Academic Papers | svg |
d59b9cf9a5e63cefa9313c79f5bbdfaafb436191cd9376fb2277fbe9fcac1b2c | 2026-02-02T00:00:00-05:00 | Simulation-based Bayesian inference with ameliorative learned summary statistics -- Part I | arXiv:2601.22441v1 Announce Type: cross Abstract: This paper, which is Part 1 of a two-part paper series, considers a simulation-based inference with learned summary statistics, in which such a learned summary statistic serves as an empirical-likelihood with ameliorative effects in the Bayesian setting, when the exact ... | https://arxiv.org/abs/2601.22441 | Academic Papers | svg |
0dfd44c350583bdf0e33c44c2382b49fb33140fbd3a1fbd2462d4b40fe9eda3e | 2026-02-02T00:00:00-05:00 | AI Decodes Historical Chinese Archives to Reveal Lost Climate History | arXiv:2601.22458v1 Announce Type: cross Abstract: Historical archives contain qualitative descriptions of climate events, yet converting these into quantitative records has remained a fundamental challenge. Here we introduce a paradigm shift: a generative AI framework that inverts the logic of historical chroniclers by... | https://arxiv.org/abs/2601.22458 | Academic Papers | svg |
6910b62d6d002945d6a54521b98ee2fd7d48abd42ae91bab1e084b9f1f6d4f15 | 2026-02-02T00:00:00-05:00 | On the undecidability of quantum channel capacities | arXiv:2601.22471v1 Announce Type: cross Abstract: An important distinction in our understanding of capacities of classical versus quantum channels is marked by the following question: is there an algorithm which can compute (or even efficiently compute) the capacity? While there is overwhelming evidence suggesting that... | https://arxiv.org/abs/2601.22471 | Academic Papers | svg |
62a96861b76261c2bfe367787e8a5e4bd74a4fe9d5b678542688fda1b34fe9e1 | 2026-02-02T00:00:00-05:00 | Structural Conditions for Native CCZ Magic-State Fountains in qLDPC Codes | arXiv:2601.22489v1 Announce Type: cross Abstract: Quantum low-density parity-check (qLDPC) codes promise constant-rate, linear-distance families with bounded-weight checks, and recent work has realized transversal or constant-depth non-Clifford gates on various (often non-LDPC) codes. However, no explicit \emph{qubit} ... | https://arxiv.org/abs/2601.22489 | Academic Papers | svg |
0abc53fa2b7ded14ffdc32d346f8be7f176cc90745a2d109ce0118d8dcbe838b | 2026-02-02T00:00:00-05:00 | Corrected Samplers for Discrete Flow Models | arXiv:2601.22519v1 Announce Type: cross Abstract: Discrete flow models (DFMs) have been proposed to learn the data distribution on a finite state space, offering a flexible framework as an alternative to discrete diffusion models. A line of recent work has studied samplers for discrete diffusion models, such as tau-lea... | https://arxiv.org/abs/2601.22519 | Academic Papers | svg |
0aad305ecb6cdf6584b4381c2e769429364ca4f00dc7a3958efe3170d3c3a95b | 2026-02-02T00:00:00-05:00 | EndoCaver: Handling Fog, Blur and Glare in Endoscopic Images via Joint Deblurring-Segmentation | arXiv:2601.22537v1 Announce Type: cross Abstract: Endoscopic image analysis is vital for colorectal cancer screening, yet real-world conditions often suffer from lens fogging, motion blur, and specular highlights, which severely compromise automated polyp detection. We propose EndoCaver, a lightweight transformer with ... | https://arxiv.org/abs/2601.22537 | Academic Papers | svg |
cdc576812d85bbf7de3e7559ce58b3ffae2d21427558530d2936d464a835eb0e | 2026-02-02T00:00:00-05:00 | Bonnet: Ultra-fast whole-body bone segmentation from CT scans | arXiv:2601.22576v1 Announce Type: cross Abstract: This work proposes Bonnet, an ultra-fast sparse-volume pipeline for whole-body bone segmentation from CT scans. Accurate bone segmentation is important for surgical planning and anatomical analysis, but existing 3D voxel-based models such as nnU-Net and STU-Net require ... | https://arxiv.org/abs/2601.22576 | Academic Papers | svg |
e3c1a5ef7dceabed0af068d5184ce283fe2082fee680c946e146a2dec9154650 | 2026-02-02T00:00:00-05:00 | An Efficient Algorithm for Thresholding Monte Carlo Tree Search | arXiv:2601.22600v1 Announce Type: cross Abstract: We introduce the Thresholding Monte Carlo Tree Search problem, in which, given a tree $\mathcal{T}$ and a threshold $\theta$, a player must answer whether the root node value of $\mathcal{T}$ is at least $\theta$ or not. In the given tree, `MAX' or `MIN' is labeled on e... | https://arxiv.org/abs/2601.22600 | Academic Papers | svg |
4493c88f29313e76a544213832cfe1b8a3745ac810fe7f8478bb47757409ed54 | 2026-02-02T00:00:00-05:00 | RPWithPrior: Label Differential Privacy in Regression | arXiv:2601.22625v1 Announce Type: cross Abstract: With the wide application of machine learning techniques in practice, privacy preservation has gained increasing attention. Protecting user privacy with minimal accuracy loss is a fundamental task in the data analysis and mining community. In this paper, we focus on reg... | https://arxiv.org/abs/2601.22625 | Academic Papers | svg |
3b4f718957b2f6505210a653268876c53fbdf6d2a7dbd54f015b619912d41939 | 2026-02-02T00:00:00-05:00 | Training Beyond Convergence: Grokking nnU-Net for Glioma Segmentation in Sub-Saharan MRI | arXiv:2601.22637v1 Announce Type: cross Abstract: Gliomas are placing an increasingly clinical burden on Sub-Saharan Africa (SSA). In the region, the median survival for patients remains under two years, and access to diagnostic imaging is extremely limited. These constraints highlight an urgent need for automated tool... | https://arxiv.org/abs/2601.22637 | Academic Papers | svg |
dee62dee29312765cccdd35b66ec5bec00dbede3e6be13ab4e1eedfe4ebcc017 | 2026-02-02T00:00:00-05:00 | Generative and Nonparametric Approaches for Conditional Distribution Estimation: Methods, Perspectives, and Comparative Evaluations | arXiv:2601.22650v1 Announce Type: cross Abstract: The inference of conditional distributions is a fundamental problem in statistics, essential for prediction, uncertainty quantification, and probabilistic modeling. A wide range of methodologies have been developed for this task. This article reviews and compares severa... | https://arxiv.org/abs/2601.22650 | Academic Papers | svg |
5aca1d63da40f3aa4b841f11ed188c4e0298484525a4d835e7f3f46a038ae06f | 2026-02-02T00:00:00-05:00 | Spectral Gradient Descent Mitigates Anisotropy-Driven Misalignment: A Case Study in Phase Retrieval | arXiv:2601.22652v1 Announce Type: cross Abstract: Spectral gradient methods, such as the Muon optimizer, modify gradient updates by preserving directional information while discarding scale, and have shown strong empirical performance in deep learning. We investigate the mechanisms underlying these gains through a dyna... | https://arxiv.org/abs/2601.22652 | Academic Papers | svg |
db2feb5235eef27fefd19798c73611bb7b523aaa2b574c160cbcbfcbb4bc8974 | 2026-02-02T00:00:00-05:00 | Parametric vector flows for registration fields in bounded domains with applications to nonlinear interpolation of shock-dominated flows | arXiv:2601.22712v1 Announce Type: cross Abstract: We present a registration procedure for parametric model order reduction (MOR) in two- and three-dimensional bounded domains. In the MOR framework, registration methods exploit solution snapshots to identify a parametric coordinate transformation that improves the appro... | https://arxiv.org/abs/2601.22712 | Academic Papers | svg |
848fa613df4c55304c8909c546b9b0b628b06345849e323f91a82b6cbb97ec6b | 2026-02-02T00:00:00-05:00 | Profunctorial algebras | arXiv:2601.22721v1 Announce Type: cross Abstract: We provide a bicategorical generalization of Barr's landmark 1970 paper, in which he describes how to extend Set-monads to relations and uses this to characterize topological spaces as the relational algebras of the ultrafilter monad. With two-sided discrete fibrations ... | https://arxiv.org/abs/2601.22721 | Academic Papers | svg |
d34530e13838b8541ffc64c205edf4dc1a4e85a75c43f1abe7257168ce97aa70 | 2026-02-02T00:00:00-05:00 | A Cross-Domain Graph Learning Protocol for Single-Step Molecular Geometry Refinement | arXiv:2601.22723v1 Announce Type: cross Abstract: Accurate molecular geometries are a prerequisite for reliable quantum-chemical predictions, yet density functional theory (DFT) optimization remains a major bottleneck for high-throughput molecular screening. Here we present GeoOpt-Net, a multi-branch SE(3)-equivariant ... | https://arxiv.org/abs/2601.22723 | Academic Papers | svg |
90cb8f507f01204238230176ee2de88c19a9a95365ed109c21fa96525b373916 | 2026-02-02T00:00:00-05:00 | Active Learning-Driven Lightweight YOLOv9: Enhancing Efficiency in Smart Agriculture | arXiv:2601.22732v1 Announce Type: cross Abstract: This study addresses the demand for real-time detection of tomatoes and tomato flowers by agricultural robots deployed on edge devices in greenhouse environments. Under practical imaging conditions, object detection systems often face challenges such as large scale vari... | https://arxiv.org/abs/2601.22732 | Academic Papers | svg |
0326e189b22a879ace1a8b8af5f6f04f566145b374ab8add05903b6c4b04705f | 2026-02-02T00:00:00-05:00 | Synthetic Abundance Maps for Unsupervised Super-Resolution of Hyperspectral Remote Sensing Images | arXiv:2601.22755v1 Announce Type: cross Abstract: Hyperspectral single image super-resolution (HS-SISR) aims to enhance the spatial resolution of hyperspectral images to fully exploit their spectral information. While considerable progress has been made in this field, most existing methods are supervised and require gr... | https://arxiv.org/abs/2601.22755 | Academic Papers | svg |
828e1b1b30f7f93e5809a470154f78a70e4fbf9788350a600a195bc3f911849d | 2026-02-02T00:00:00-05:00 | Bayesian Matrix Completion Under Geometric Constraints | arXiv:2601.22765v1 Announce Type: cross Abstract: The completion of a Euclidean distance matrix (EDM) from sparse and noisy observations is a fundamental challenge in signal processing, with applications in sensor network localization, acoustic room reconstruction, molecular conformation, and manifold learning. Traditi... | https://arxiv.org/abs/2601.22765 | Academic Papers | svg |
8382e5034d9456d99402350ef0bcc8641586044248498a9902085ebedb105321 | 2026-02-02T00:00:00-05:00 | GRANITE: A Generalized Regional Framework for Identifying Agreement in Feature-Based Explanations | arXiv:2601.22771v1 Announce Type: cross Abstract: Feature-based explanation methods aim to quantify how features influence the model's behavior, either locally or globally, but different methods often disagree, producing conflicting explanations. This disagreement arises primarily from two sources: how feature interact... | https://arxiv.org/abs/2601.22771 | Academic Papers | svg |
307e4e6156c30a95e76c008fb1807e1083c1e06d82697580213c1837e4ae7078 | 2026-02-02T00:00:00-05:00 | Streaming Speech Recognition with Decoder-Only Large Language Models and Latency Optimization | arXiv:2601.22779v1 Announce Type: cross Abstract: Recent advances have demonstrated the potential of decoderonly large language models (LLMs) for automatic speech recognition (ASR). However, enabling streaming recognition within this framework remains a challenge. In this work, we propose a novel streaming ASR approach... | https://arxiv.org/abs/2601.22779 | Academic Papers | svg |
a056a6d23c3bed20f6a855e6d23aa95fb882820368bde44d947ff3bbe370d9a5 | 2026-02-02T00:00:00-05:00 | Approximating $f$-Divergences with Rank Statistics | arXiv:2601.22784v1 Announce Type: cross Abstract: We introduce a rank-statistic approximation of $f$-divergences that avoids explicit density-ratio estimation by working directly with the distribution of ranks. For a resolution parameter $K$, we map the mismatch between two univariate distributions $\mu$ and $\nu$ to a... | https://arxiv.org/abs/2601.22784 | Academic Papers | svg |
c60b9a82705383fb6014d770aa453ffb20e9dfdb11104f97082ae9b1469694cb | 2026-02-02T00:00:00-05:00 | CALM: Joint Contextual Acoustic-Linguistic Modeling for Personalization of Multi-Speaker ASR | arXiv:2601.22792v1 Announce Type: cross Abstract: We present CALM, a joint Contextual Acoustic-Linguistic Modeling framework for multi-speaker automatic speech recognition (ASR). In personalized AI scenarios, the joint availability of acoustic and linguistic cues naturally motivates the integration of target-speaker co... | https://arxiv.org/abs/2601.22792 | Academic Papers | svg |
363eff98120f907f36cc0f20a964738ab803ef004c854b935cb59cdade49a58e | 2026-02-02T00:00:00-05:00 | EmoShift: Lightweight Activation Steering for Enhanced Emotion-Aware Speech Synthesis | arXiv:2601.22873v1 Announce Type: cross Abstract: Achieving precise and controllable emotional expression is crucial for producing natural and context-appropriate speech in text-to-speech (TTS) synthesis. However, many emotion-aware TTS systems, including large language model (LLM)-based designs, rely on scaling fixed ... | https://arxiv.org/abs/2601.22873 | Academic Papers | svg |
5481844a5335d6816db9ae7f628f84f0d11f952c4943d2e1fb1553e09bb58ed6 | 2026-02-02T00:00:00-05:00 | Development of Domain-Invariant Visual Enhancement and Restoration (DIVER) Approach for Underwater Images | arXiv:2601.22878v1 Announce Type: cross Abstract: Underwater images suffer severe degradation due to wavelength-dependent attenuation, scattering, and illumination non-uniformity that vary across water types and depths. We propose an unsupervised Domain-Invariant Visual Enhancement and Restoration (DIVER) framework tha... | https://arxiv.org/abs/2601.22878 | Academic Papers | svg |
1df5dc161377ef0964b10be6c793090bbc495dcb25b8b286cfce40a9b4101041 | 2026-02-02T00:00:00-05:00 | Persuasive Privacy | arXiv:2601.22945v1 Announce Type: cross Abstract: We propose a novel framework for measuring privacy from a Bayesian game-theoretic perspective. This framework enables the creation of new, purpose-driven privacy definitions that are rigorously justified, while also allowing for the assessment of existing privacy guaran... | https://arxiv.org/abs/2601.22945 | Academic Papers | svg |
b6805de8d27f067abaedf7d75314a075df3064ff0d36a8349a29c0f94efa724e | 2026-02-02T00:00:00-05:00 | OneFlowSBI: One Model, Many Queries for Simulation-Based Inference | arXiv:2601.22951v1 Announce Type: cross Abstract: We introduce \textit{OneFlowSBI}, a unified framework for simulation-based inference that learns a single flow-matching generative model over the joint distribution of parameters and observations. Leveraging a query-aware masking distribution during training, the same m... | https://arxiv.org/abs/2601.22951 | Academic Papers | svg |
6bcbb4f1bee2b4b7cabcc44e0bb4aabb0d82496f98fb8278fbf4ab661f17a196 | 2026-02-02T00:00:00-05:00 | Neural Backward Filtering Forward Guiding | arXiv:2601.23030v1 Announce Type: cross Abstract: Inference in non-linear continuous stochastic processes on trees is challenging, particularly when observations are sparse (leaf-only) and the topology is complex. Exact smoothing via Doob's $h$-transform is intractable for general non-linear dynamics, while particle-ba... | https://arxiv.org/abs/2601.23030 | Academic Papers | svg |
aadc8cbfa4944d2bd6b1711e91fa8359a217d32a1b72a63098b79f6c95312d24 | 2026-02-02T00:00:00-05:00 | Asymptotic Theory of Iterated Empirical Risk Minimization, with Applications to Active Learning | arXiv:2601.23031v1 Announce Type: cross Abstract: We study a class of iterated empirical risk minimization (ERM) procedures in which two successive ERMs are performed on the same dataset, and the predictions of the first estimator enter as an argument in the loss function of the second. This setting, which arises natur... | https://arxiv.org/abs/2601.23031 | Academic Papers | svg |
8def742d53b3e6aa5356b0074f454f9809350b1242f4ac2c5b4cf6ae39b3e052 | 2026-02-02T00:00:00-05:00 | Scale Equivariance Regularization and Feature Lifting in High Dynamic Range Modulo Imaging | arXiv:2601.23037v1 Announce Type: cross Abstract: Modulo imaging enables high dynamic range (HDR) acquisition by cyclically wrapping saturated intensities, but accurate reconstruction remains challenging due to ambiguities between natural image edges and artificial wrap discontinuities. This work proposes a learning-ba... | https://arxiv.org/abs/2601.23037 | Academic Papers | svg |
97468d6b0a0bb0829421e5c88f6b0b73fd18e104586a55ceb49e0ce888c5437d | 2026-02-02T00:00:00-05:00 | Learning-Based Signal Recovery in Nonlinear Systems with Spectrally Separated Interference | arXiv:2601.23076v1 Announce Type: cross Abstract: Upper Mid-Band (FR3, 7-24 GHz) receivers for 6G must operate over wide bandwidths in dense spectral environments, making them particularly vulnerable to strong adjacent-band interference and front-end nonlinearities. While conventional linear receivers can suppress spec... | https://arxiv.org/abs/2601.23076 | Academic Papers | svg |
0902835b4ef711949c20eb31ad2c26d081ffc3f941d22ceb214afc2d58a351b3 | 2026-02-02T00:00:00-05:00 | Vision-Language Controlled Deep Unfolding for Joint Medical Image Restoration and Segmentation | arXiv:2601.23103v1 Announce Type: cross Abstract: We propose VL-DUN, a principled framework for joint All-in-One Medical Image Restoration and Segmentation (AiOMIRS) that bridges the gap between low-level signal recovery and high-level semantic understanding. While standard pipelines treat these tasks in isolation, our... | https://arxiv.org/abs/2601.23103 | Academic Papers | svg |
937594108ecc0dae8e6d803d81a190b4c55d79a0a3f900c869a09c7b1ad862ca | 2026-02-02T00:00:00-05:00 | Interpolation Techniques for Fast Channel Estimation in Ray Tracing | arXiv:2601.23119v1 Announce Type: cross Abstract: Ray tracing is increasingly utilized in wireless system simulations to estimate channel paths. In large-scale simulations with complex environments, ray tracing at high resolution can be computationally demanding. To reduce the computation, this paper presents a novel m... | https://arxiv.org/abs/2601.23119 | Academic Papers | svg |
c44b3f88164196a63f2212a56f8e9e2faad420c6eff6286c20be9ae5ff6e28cf | 2026-02-02T00:00:00-05:00 | Compressed BC-LISTA via Low-Rank Convolutional Decomposition | arXiv:2601.23148v1 Announce Type: cross Abstract: We study Sparse Signal Recovery (SSR) methods for multichannel imaging with compressed {forward and backward} operators that preserve reconstruction accuracy. We propose a Compressed Block-Convolutional (C-BC) measurement model based on a low-rank Convolutional Neural N... | https://arxiv.org/abs/2601.23148 | Academic Papers | svg |
acf4109283bdfe6bb46a8bfd95e51912809c72fecd2c73f366cd9cdacd3cf9d6 | 2026-02-02T00:00:00-05:00 | Scale-Cascaded Diffusion Models for Super-Resolution in Medical Imaging | arXiv:2601.23201v1 Announce Type: cross Abstract: Diffusion models have been increasingly used as strong generative priors for solving inverse problems such as super-resolution in medical imaging. However, these approaches typically utilize a diffusion prior trained at a single scale, ignoring the hierarchical scale st... | https://arxiv.org/abs/2601.23201 | Academic Papers | svg |
678bccb9687f587b4ddd7242d7c808e842452371da3c37565c170172cfbfe696 | 2026-02-02T00:00:00-05:00 | A Random Matrix Theory of Masked Self-Supervised Regression | arXiv:2601.23208v1 Announce Type: cross Abstract: In the era of transformer models, masked self-supervised learning (SSL) has become a foundational training paradigm. A defining feature of masked SSL is that training aggregates predictions across many masking patterns, giving rise to a joint, matrix-valued predictor ra... | https://arxiv.org/abs/2601.23208 | Academic Papers | svg |
93e198f033c39bb46442f99161ec81a7b50e6a2b6a21c17d3d3d36b8ac60b518 | 2026-02-02T00:00:00-05:00 | Disentangling multispecific antibody function with graph neural networks | arXiv:2601.23212v1 Announce Type: cross Abstract: Multispecific antibodies offer transformative therapeutic potential by engaging multiple epitopes simultaneously, yet their efficacy is an emergent property governed by complex molecular architectures. Rational design is often bottlenecked by the inability to predict ho... | https://arxiv.org/abs/2601.23212 | Academic Papers | svg |
d2b904840f1e59a5ed615bab75ff9c6a40a79817aba96bfd038a255545201799 | 2026-02-02T00:00:00-05:00 | Solving Inverse Problems with Flow-based Models via Model Predictive Control | arXiv:2601.23231v1 Announce Type: cross Abstract: Flow-based generative models provide strong unconditional priors for inverse problems, but guiding their dynamics for conditional generation remains challenging. Recent work casts training-free conditional generation in flow models as an optimal control problem; however... | https://arxiv.org/abs/2601.23231 | Academic Papers | svg |
e66d59a063a8d45c9f93fa711c7fb4b14474791c5d79142006442532b60825d9 | 2026-02-02T00:00:00-05:00 | Graph Attention Network for Node Regression on Random Geometric Graphs with Erd\H{o}s--R\'enyi contamination | arXiv:2601.23239v1 Announce Type: cross Abstract: Graph attention networks (GATs) are widely used and often appear robust to noise in node covariates and edges, yet rigorous statistical guarantees demonstrating a provable advantage of GATs over non-attention graph neural networks~(GNNs) are scarce. We partially address... | https://arxiv.org/abs/2601.23239 | Academic Papers | svg |
645c21d3fe7c8713542173f3afc12dd4f8520e60fbe87bbaae4274a903b4724e | 2026-02-02T00:00:00-05:00 | Nested Slice Sampling: Vectorized Nested Sampling for GPU-Accelerated Inference | arXiv:2601.23252v1 Announce Type: cross Abstract: Model comparison and calibrated uncertainty quantification often require integrating over parameters, but scalable inference can be challenging for complex, multimodal targets. Nested Sampling is a robust alternative to standard MCMC, yet its typically sequential struct... | https://arxiv.org/abs/2601.23252 | Academic Papers | svg |
67f8574de0e8a3dbbbb94c763dd6f5690c01148db25d6e567b4a35270f38555e | 2026-02-02T00:00:00-05:00 | Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging | arXiv:2601.23276v1 Announce Type: cross Abstract: Astronomical imaging remains noise-limited under practical observing constraints, while standard calibration pipelines mainly remove structured artifacts and leave stochastic noise largely unresolved. Learning-based denoising is promising, yet progress is hindered by sc... | https://arxiv.org/abs/2601.23276 | Academic Papers | svg |
12fb07a8908345b4ccd4d0fa4d6b13ffd98e3f42968ba6b0b0462625fb225022 | 2026-02-02T00:00:00-05:00 | Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning | arXiv:2302.02662v5 Announce Type: replace Abstract: Recent works successfully leveraged Large Language Models' (LLM) abilities to capture abstract knowledge about world's physics to solve decision-making problems. Yet, the alignment between LLMs' knowledge and the environment can be wrong and limit functional competenc... | https://arxiv.org/abs/2302.02662 | Academic Papers | svg |
6e8dfd57e8edf65c19266bbf3cb5600b7ff4b8fd3b9a593d8c479e8f19f6e2b0 | 2026-02-02T00:00:00-05:00 | A Cheeger Inequality for Size-Specific Conductance | arXiv:2303.11452v2 Announce Type: replace Abstract: The $\mu$-conductance measure proposed by Lov\'asz and Simonovits is a size-specific conductance score that identifies the set with smallest conductance while disregarding those sets with volume smaller than a $\mu$ fraction of the whole graph. Using $\mu$-conductance... | https://arxiv.org/abs/2303.11452 | Academic Papers | svg |
87d0a06dc45dbf55a5bdc3095223d0c84a189be854ebbb7196b5f2e173d51064 | 2026-02-02T00:00:00-05:00 | On The Relationship Between Continual Learning and Long-Tailed Recognition | arXiv:2306.13275v2 Announce Type: replace Abstract: Real-world datasets often exhibit long-tailed distributions, where a few dominant "Head" classes have abundant samples while most "Tail" classes are severely underrepresented, leading to biased learning and poor generalization for the Tail. We present a theoretical fr... | https://arxiv.org/abs/2306.13275 | Academic Papers | svg |
bbe66026f9d9e71189d3febf7e3b9bf385929a6b7973d61a55a3c64a57678e7b | 2026-02-02T00:00:00-05:00 | The complexity of solving a system of equations of the same degree | arXiv:2309.03855v3 Announce Type: replace Abstract: Many systems of interest in cryptography consist of equations of the same degree. Under the assumption that the degree of regularity is finite, we prove upper bounds on the degree of regularity of a system of equations of the same degree, with or without adding the fi... | https://arxiv.org/abs/2309.03855 | Academic Papers | svg |
6503f1c6934b8be105c55b77db1b6cc21ef74651c03c7fdac11c8310a66d31d3 | 2026-02-02T00:00:00-05:00 | Exploring and Analyzing the Effect of Avatar's Realism on Anxiety of English as Second Language (ESL) Speakers | arXiv:2311.05126v2 Announce Type: replace Abstract: Virtual avatars are increasingly used to support cross-cultural communication, yet their impact on communication anxiety among English as a Second Language (ESL) speakers remains underexplored. This study examines how avatar realism influences anxiety during English i... | https://arxiv.org/abs/2311.05126 | Academic Papers | svg |
e48ebe6418bec6b02aa856b00d29826b5dd3f478682b6d5d2f5b4b483b915908 | 2026-02-02T00:00:00-05:00 | Symmetry-Enforced Quadratic Degradability Beyond Low Dimensions | arXiv:2401.16312v5 Announce Type: replace Abstract: Approximate degradability provides a powerful framework for bounding the quantum and private capacities of noisy quantum channels in regimes where exact degradability fails. While generic low-noise channels exhibit a non-degradability parameter that decays as a fracti... | https://arxiv.org/abs/2401.16312 | Academic Papers | svg |
12b16deaa66e367f4a2d31794e8cfb25c683903c5e29bcd92424dce59e9ece5b | 2026-02-02T00:00:00-05:00 | Estimating the Decoding Failure Rate of Binary Regular Codes Using Iterative Decoding | arXiv:2401.16919v4 Announce Type: replace Abstract: Providing closed-form estimates of the decoding failure rate of iterative decoders for low- and moderate-density binary parity-check codes has attracted significant interest in the research community. Recently, interest in this topic has increased due to the use of it... | https://arxiv.org/abs/2401.16919 | Academic Papers | svg |
b17454c2fb4c1aacecaf2ecfc2ce9ad4087c8787a133b3f5fd979d5caf6052c1 | 2026-02-02T00:00:00-05:00 | XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics | arXiv:2402.02452v3 Announce Type: replace Abstract: With the rise of complex cyber devices Cyber Forensics (CF) is facing many new challenges. For example, there are dozens of systems running on smartphones, each with more than millions of downloadable applications. Sifting through this large amount of data and making ... | https://arxiv.org/abs/2402.02452 | Academic Papers | svg |
4c39572a6d27690ed36a8f0ce24ea2f1090707a477069bfd78679e3ddb9560d2 | 2026-02-02T00:00:00-05:00 | TorchCP: A Python Library for Conformal Prediction | arXiv:2402.12683v5 Announce Type: replace Abstract: Conformal prediction (CP) is a powerful statistical framework that generates prediction intervals or sets with guaranteed coverage probability. While CP algorithms have evolved beyond traditional classifiers and regressors to sophisticated deep learning models like de... | https://arxiv.org/abs/2402.12683 | Academic Papers | svg |
bf48511b30b8d89e77f9576ff209eadafcfe1a85976456ab89c4a398d6a6c384 | 2026-02-02T00:00:00-05:00 | OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Models | arXiv:2402.13524v2 Announce Type: replace Abstract: Modern large language models (LLMs) should generally benefit individuals from various cultural backgrounds around the world. However, most recent advanced generative evaluation benchmarks tailed for LLMs mainly focus on English. To this end, we introduce OMGEval, the ... | https://arxiv.org/abs/2402.13524 | Academic Papers | svg |
5d97d45238e52b301cef0cfa04c552c0d9e09cbec8ffda73ecc6336116b12ed6 | 2026-02-02T00:00:00-05:00 | Can Distillation Mitigate Backdoor Attacks in Pre-trained Encoders? | arXiv:2403.03846v2 Announce Type: replace Abstract: Self-Supervised Learning (SSL) has become a prominent paradigm for pre-training encoders to learning general-purpose representations from unlabeled data and releasing them on third-party platforms for broad downstream deep learning tasks. However, SSL is vulnerable to... | https://arxiv.org/abs/2403.03846 | Academic Papers | svg |
69c3776f354046ae6e6e09eaba5a61a27558eae434b0847450585ba256e35c2a | 2026-02-02T00:00:00-05:00 | FlashFace: Human Image Personalization with High-fidelity Identity Preservation | arXiv:2403.17008v2 Announce Type: replace Abstract: This work presents FlashFace, a practical tool with which users can easily personalize their own photos on the fly by providing one or a few reference face images and a text prompt. Our approach is distinguishable from existing human photo customization methods by hig... | https://arxiv.org/abs/2403.17008 | Academic Papers | svg |
3f628e137b14bf92e055f91f7e5c47aea544f82552428f184b5c99eec6d97fc1 | 2026-02-02T00:00:00-05:00 | Parameterized Algorithms for Coordinated Motion Planning: Minimizing Energy | arXiv:2404.15950v3 Announce Type: replace Abstract: We study the parameterized complexity of a generalization of the coordinated motion planning problem on graphs, where the goal is to route a specified subset of a given set of $k$ robots to their destinations with the aim of minimizing the total energy (i.e., the tota... | https://arxiv.org/abs/2404.15950 | Academic Papers | svg |
0d964c49445e2d2b3bb7e92886e9092a2992bbe2bf6a9f9f5d5696bf39e4c28d | 2026-02-02T00:00:00-05:00 | Implications of computer science theory for the simulation hypothesis | arXiv:2404.16050v4 Announce Type: replace Abstract: The simulation hypothesis has recently excited renewed interest in the physics and philosophy communities. However, the hypothesis specifically concerns {\textit{computers}} that simulate physical universes. So to formally investigate the hypothesis, we need to unders... | https://arxiv.org/abs/2404.16050 | Academic Papers | svg |
872c84e6ad332bb46465d9f6a7c768e4bb5203b3740d3e6755e7a741c3e4a394 | 2026-02-02T00:00:00-05:00 | Finding patterns of meaning: Reassessing Construal Clustering via Bipolar Class Analysis | arXiv:2404.17042v3 Announce Type: replace Abstract: Empirical research on \textit{construals}--social affinity groups that share similar patterns of meaning--has advanced significantly in recent years. This progress is largely driven by the development of \textit{Construal Clustering Methods} (CCMs), which group survey... | https://arxiv.org/abs/2404.17042 | Academic Papers | svg |
b0cb5a31b7f4d850c06ef446bec2acf5a53bc25117e2eeac2be042996db08f9e | 2026-02-02T00:00:00-05:00 | Complexity Classes for Online Problems with and without Predictions | arXiv:2406.18265v3 Announce Type: replace Abstract: With the developments in machine learning, there has been a surge in interest and results focused on algorithms utilizing predictions, not least in online algorithms where most new results incorporate the prediction aspect for concrete online problems. While the struc... | https://arxiv.org/abs/2406.18265 | Academic Papers | svg |
11d33e905b8dc685d1fa544dddb915ff3cc9de57744110dd42a4c287852ae640 | 2026-02-02T00:00:00-05:00 | Monocular pose estimation of articulated open surgery tools -- in the wild | arXiv:2407.12138v3 Announce Type: replace Abstract: This work presents a framework for monocular 6D pose estimation of surgical instruments in open surgery, addressing challenges such as object articulations, specularity, occlusions, and synthetic-to-real domain adaptation. The proposed approach consists of three main ... | https://arxiv.org/abs/2407.12138 | Academic Papers | svg |
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