Search: “research”

Identifying Connectivity Distributions from Neural Dynamics Using Flows

arXiv:2603.26506v2 Announce Type: replace-cross Abstract: Connectivity structure shapes neural computation, but inferring this structure from population recordings is degenerate: multiple connectivity

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idSCD: Identifying Training Datasets through Semantic Correlation Descriptors

DGX agent

arXiv:2605.30462v1 Announce Type: cross Abstract: Can a dataset be recognized from the spurious correlations it induces during training? We argue that datasets leave dataset-specific traces in a model

researcharxiv-cs-ai

1 Jun 2026

Immuno-VLM: Immunizing Large Vision-Language Models via Generative Semantic Antibodies for Open-World Trustworthiness

arXiv:2605.30745v1 Announce Type: new Abstract: Large Vision-Language Models have achieved unprecedented success in zero-shot recognition by aligning visual features with broad semantic concepts. Howe

Improved Distribution Estimation in ell_infty

arXiv:2605.30509v1 Announce Type: cross Abstract: We present improved bounds for estimating discrete probability distributions under the ell_infty norm. These include minimax bounds in expectation and

Improved Guarantees for Langevin Monte Carlo with Average Smoothness

arXiv:2605.31413v1 Announce Type: cross Abstract: We establish improved nonasymptotic bounds for Langevin Monte Carlo in the strongly log-concave setting, when the error is measured by the Wasserstein

Improving Selective Classification with Pairwise Queries for Binary Classification

arXiv:2605.30615v1 Announce Type: new Abstract: In selective classification, a model predicts the labels of data samples where it is confident, and abstains from predicting labels for samples on which

Incremental BPE Tokenization

arXiv:2605.30813v1 Announce Type: new Abstract: We propose a novel algorithm for incremental Byte Pair Encoding (BPE) tokenization. The algorithm processes each input byte in worst-case O(log^2 t) tim

Inferring Events from Time Series using Language Models

arXiv:2503.14190v3 Announce Type: replace Abstract: A common goal in analyzing time series data is to understand how events cause observed variations. We study whether Large Language Models (LLMs) can

IntAttention: A Fully Integer Attention Pipeline for Efficient Edge Inference

arXiv:2511.21513v2 Announce Type: replace Abstract: Deploying Transformer models on edge devices is limited by latency and energy budgets. While INT8 quantization effectively accelerates the primary m

'Intelegi Romaneste?'' A Recipe for Romanian Vision-Language Models

arXiv:2605.31401v1 Announce Type: new Abstract: Vision-Language Models (VLMs) largely follow the text-only LLM trajectory, excelling on English benchmarks but sharply degrading on low-resource languag

Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach

arXiv:2605.30903v1 Announce Type: cross Abstract: Inverse reinforcement learning (IRL) typically assumes demonstrations from a single optimal demonstrator, but in many applications data come from mult

Inverting Data Transformations via Diffusion Sampling

arXiv:2602.08267v2 Announce Type: replace-cross Abstract: We study the problem of transformation inversion on general Lie groups: a datum is transformed by an unknown group element, and the goal is to

IRIS: time-structured manifold projections

arXiv:2605.30810v1 Announce Type: new Abstract: High-dimensional biomedical data, such as cell-by-gene matrices, are increasingly generated temporally. However, Manifold Learning algorithms, like t-SN

Is Memorization Helpful or Harmful? Prior Information Sets the Threshold

arXiv:2602.09405v2 Announce Type: replace-cross Abstract: We examine the connection between training error and generalization error for arbitrary estimating procedures, working in an overparameterized

Is the Last Layer Sufficient for Uncertainty Quantification?

arXiv:2605.30741v1 Announce Type: cross Abstract: Epistemic uncertainty quantification (UQ) for deep neural networks (DNNs) is a requirement for safe adoption of AI in mission-critical settings. Sever

Joint angle based learning to refine kinematic human pose estimation

arXiv:2507.11075v2 Announce Type: replace-cross Abstract: Marker-free human pose estimation (HPE) has found increasing applications in various fields. Current HPE suffers from occasional errors in key

Joint Multi-Camera LiDAR Extrinsic Calibration via Learned Pairwise Initialization and Geometric Refinement

arXiv:2605.31576v1 Announce Type: new Abstract: Most learning-based camera-LiDAR calibration methods treat each camera-LiDAR pair independently, ignoring the rigid geometric coupling in multi-camera p

Kalimati Vegetable Price Index Forecasting with a Momentum Corrected Online Stacking Ensemble

arXiv:2605.30720v1 Announce Type: cross Abstract: Forecasting agricultural commodity prices in emerging economies is difficult due to high volatility, frequent supply disruptions, and strong cultural

KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems

arXiv:2605.31596v1 Announce Type: new Abstract: Diffusion models have shown promising performance as data-driven priors for computational imaging, as well as some capacity to detect out-of-distributio

Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study

arXiv:2605.30465v1 Announce Type: new Abstract: Multi-label topic classification without labeled training data is a challenging task, specially when documents contain complex relational information. W

Language Models Can Resolve Reference Compositionally, But It's Not Their Native Strength: The Case of the Personal Relation Task

arXiv:2605.31480v1 Announce Type: new Abstract: Do neural models, such as Large Language Models, genuinely acquire compositional abilities for interpretation of natural language? When we talk about se

Latent Geometric Chords for Query-Efficient Decision-Based Adversarial Attacks

arXiv:2605.31219v1 Announce Type: new Abstract: While decision-based black-box adversarial attacks present a severe security threat, current methodologies suffer from fundamental limitations. Pixel-wi

Latent Space Disentanglement via Activation Steering for Interpretable Attribute Control in Symbolic Music Generation

arXiv:2605.31295v1 Announce Type: cross Abstract: Transformer-based architectures have significantly advanced the generation of complex symbolic sequences, yet a significant gap remains in achieving f

Agents

Learning Agent-Compatible Context Management for Long-Horizon Tasks

arXiv:2605.30785v1 Announce Type: new Abstract: LLM agents increasingly face long-horizon tasks such as web search and deep research in real-world applications, where accumulated context can cause lon

agentsarxiv-cs-ai

Learning Cardiac Latent Representations in Vectorcardiogram Space

arXiv:2605.31249v1 Announce Type: cross Abstract: Electrocardiography (ECG) is a cornerstone of cardiac assessment, making the learning of informative ECG representations fundamental to tasks ranging

Learning effective Sargassum transport dynamics from limited drifter observations

arXiv:2605.30603v1 Announce Type: cross Abstract: Floating-material transport is influenced by unresolved processes that are often absent from available circulation products. We develop a data-driven

Learning from Fine-Grained Visual Discrepancies: Mitigating Multimodal Hallucinations via In-Context Visual Contrastive Optimization

arXiv:2605.31312v1 Announce Type: cross Abstract: Multimodal hallucination remains a persistent challenge for Vision-Language Models (VLMs). Standard textual Direct Preference Optimization (DPO) often

Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction

arXiv:2605.31595v1 Announce Type: new Abstract: Dynamic scene reconstruction from monocular video remains a fundamental challenge in computer vision. Existing feed-forward methods predict 3D Gaussians

Learning Hyperspherical Time-Frequency Representations for Time-Series Out-of-Distribution Detection

arXiv:2605.31155v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection for time-series data remains comparatively underexplored compared to vision and language, with a limited principled

Learning to Bid in FCR Markets: A Best-of-Both-Worlds Approach

arXiv:2605.31070v1 Announce Type: new Abstract: Bidding in the European Frequency Containment Reserve (FCR) market is challenging for flexibility providers because competing offers are hidden and bidd

Let me get this straight. Howard Lutnick, Trump’s Commerce Secretary, got caught lying about his relationship with Jeffrey Epstein. Democrat…

Let me get this straight. Howard Lutnick, Trump’s Commerce Secretary, got caught lying about his relationship with Jeffrey Epstein. Democrats grilled him about it and finally got him to agree to testi

researchyann-lecun--x

Lightweight SAR Ship Detection via Contrastive Distillation

arXiv:2605.30380v1 Announce Type: new Abstract: Deep convolutional and transformer-based detectors achieve strong performance for SAR ship detection but are often computationally prohibitive for real-

LiMuon: Light and Fast Muon Optimizer for Large Models

arXiv:2509.14562v3 Announce Type: replace Abstract: Large models recently are widely applied in machine learning, so efficient training of large models has received widespread attention. More recently

Linear Ensembles Wash Away Watermarks: On the Fragility of Distributional Perturbations in LLMs

arXiv:2605.30501v1 Announce Type: new Abstract: Watermarking embeds statistical signatures in AI-generated text for detection and attribution. We reveal a fundamental vulnerability: when users access

Linear Scaling Video VLMs for Long Video Understanding

arXiv:2605.31598v1 Announce Type: new Abstract: Video vision-language models (VLMs) are increasingly used in long-horizon and streaming settings, yet most video encoders still rely on spatiotemporal s

LLMs Lean on Priors, Not Programming Language Semantics

arXiv:2510.03415v3 Announce Type: replace-cross Abstract: Recent work asks whether large language models (LLMs) condition their reasoning on explicit rules rather than statistical regularities from pr

Log-Ratio Propagation on the Simplex: A Theory of Cellwise Contamination for Compositional Data

arXiv:2605.31345v1 Announce Type: cross Abstract: Compositional data must be analysed through log-ratios: scale invariance, the defining axiom of the field, leaves no alternative. The centred log-rati

MAECO-Lite: Modular Ontology for Dynamic Malware Analysis

arXiv:2605.31199v1 Announce Type: cross Abstract: Capturing dynamic malware behavior in a practical but still semantically precise manner remains a significant challenge in cyber threat intelligence.

Mathematical Morphology in Machine Learning

arXiv:2605.30700v1 Announce Type: new Abstract: This work introduces mathematical morphology-an established visual computing theory-into machine learning to exploit shape and density aspects often ove

Mechanistic Interpretability as Statistical Estimation: A Variance Analysis

arXiv:2510.00845v4 Announce Type: replace-cross Abstract: Mechanistic Interpretability (MI) aims to reverse-engineer model behaviors by identifying functional sub-networks. Yet, the scientific validit

Meta-Adaptive Beam Search Planning for Transformer-Based Reinforcement Learning Control of UAVs with Overhead Manipulators under Flight Disturbances

arXiv:2603.26612v2 Announce Type: replace Abstract: Drones equipped with overhead manipulators offer unique capabilities for inspection, maintenance, and contact-based interaction. However, the motion

researcharxiv-cs-ro

Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching

arXiv:2411.00759v5 Announce Type: replace Abstract: Discrete flow matching, a recent framework for modeling categorical data, has shown competitive performance with autoregressive models. However, unl

Mitigating Content Shift and Hallucination in GenAI Image Editing via Structural Refinement

arXiv:2605.30437v1 Announce Type: new Abstract: Generative AI (GenAI) image editors, such as Nano Banana, produce visually compelling results for retouching tasks, enabling non-experts to edit images

Mixture of Concept Bottleneck Experts

arXiv:2602.02886v2 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) promote interpretability by grounding predictions in human-understandable concepts. However, existing CBMs ty

Model Monotonicity in Autobidding Auctions: When Do Better Predictions Lead to Better Outcomes?

arXiv:2605.31036v1 Announce Type: cross Abstract: Online advertising platforms rely on machine learning models to predict click-through rates (pCTR) and conversion rates (pCVR) for auction mechanisms.

Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments

arXiv:2605.31443v1 Announce Type: cross Abstract: We present a regression-adjustment framework designed for the estimation of longitudinal treatment effects in randomized experiments under static regi

MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction

arXiv:2605.31302v1 Announce Type: cross Abstract: Undersampled magnetic resonance imaging (MRI) reconstruction seeks to recover temporally or contrast-varying image series from incomplete multicoil k-

MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation

arXiv:2605.31010v1 Announce Type: new Abstract: Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge