research

CGES: Confidence-Guided Early Stopping for Efficient and Accurate Self-Consistency

arXiv:2511.02603v2 Announce Type: replace Abstract: Large language models (LLMs) are often queried multiple times at test time, with predictions aggregated by majority vote. While effective, this self

researcharxiv-cs-cl

Research

@ClementDelangue @Dan_Jeffries1 Everyone, please join Project Tapestry https://thealliance.ai/projects/tapestry

DGX agent

Project Tapestry is an initiative under The Alliance focused on collaborative AI development, promoted by prominent AI researchers including Yann LeCun. The project appears to be soliciting participat

researchyann-lecun--x

10 Jun 2026

ClinReadNet: A clinical reading-inspired network for low-dose abdominal CT image quality assessment

arXiv:2606.10372v1 Announce Type: new Abstract: In abdominal CT imaging, developing a low-dose, no-reference image quality assessment (No-reference IQA) model that mimics doctors' reading habits for e

Compositional Generative Modeling from Decentralized Data

arXiv:2606.10153v1 Announce Type: new Abstract: Learning the compositional nature of the physical world requires joint observation of interacting factors. However, because practical data is often dece

Conformal Risk Prediction for Non-Alcoholic Fatty Liver Disease Using Gradient Boosting with Distribution-Free Coverages

arXiv:2606.09860v1 Announce Type: cross Abstract: Non-alcoholic fatty liver disease (NAFLD) affects roughly 25% of global adults, posing substantial hepatic and cardiovascular risks. Yet, population-l

Conservation Laws from Data Symmetry in Neural Networks

arXiv:2606.10913v1 Announce Type: new Abstract: We explore whether intrinsic symmetries of the training data lead to conserved quantities during gradient-flow training of neural networks. Under the as

Convergence Rates for Neural-Network Estimation with Current-Status Data

arXiv:2606.10119v1 Announce Type: cross Abstract: Current-status data arise when an event time is observed only through an indicator of whether it occurred before an examination time. This paper studi

Correcting Variable Importance Scored by Random Forests

arXiv:2606.10770v1 Announce Type: cross Abstract: Variable importance produced by Random Forests (RF) is used widely in statistical data analysis, and has played an important role in a variety of task

Cost-Aware Routing for Efficient Text-To-Image Generation

arXiv:2506.14753v3 Announce Type: replace Abstract: Diffusion models are well known for their ability to generate a high-fidelity image for an input prompt through an iterative denoising process. Unfo

DAH-Net: A Dual-Attention Hybrid Network for Interpretable and Robust EEG-Based Emotion Recognition

arXiv:2602.06411v2 Announce Type: replace Abstract: EEG-based emotion recognition supports affective brain-computer interfaces and mental health monitoring yet remains challenged by signal complexity,

Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniques

arXiv:2606.11140v1 Announce Type: cross Abstract: Data assimilation (DA) in subsurface flow entails calibrating model parameters to match observed data, typically at wells, while preserving geological

DD-INR: Dynamics-Driven Implicit Neural Representation for Accelerated Whole-Brain Functional MRI Reconstruction

arXiv:2606.10756v1 Announce Type: new Abstract: Accelerated acquisition of fMRI enables enhanced detection of neurovascular (BOLD) activity in the brain, but image reconstruction becomes challenging w

Decision-Making under Combinatorial Risk

arXiv:2606.10092v1 Announce Type: new Abstract: Decision-making under risk is typically studied through single-shot lottery choices. Yet many real decisions involve combinatorial risk, where risk aris

DECSELFMASK: Leveraging Unlabeled Text via Self-Relevance-Guided Masking for Decoder-Only Classification

arXiv:2606.09466v2 Announce Type: replace Abstract: Classification tasks require annotated data, which can often be expensive, time-consuming, or even unfeasible to collect. This is the case of the me

researcharxiv-cs-cl

Deep Generative Model for Human Mobility Behavior

arXiv:2510.06473v3 Announce Type: replace-cross Abstract: Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health. Despite

Deep learning for echo sounder data

arXiv:2606.10811v1 Announce Type: new Abstract: There is no doubt that over the last decade, techniques from the field of machine learning have revolutionized how we process and interpret data, especi

Deep Slice Interpolation for Reducing Through-Plane Anisotropy and Noise in Head CT

arXiv:2606.09953v1 Announce Type: cross Abstract: Head computed tomography (CT) typically uses sub-millimeter in-plane resolution but 2-5 mm through-plane spacing, creating substantial anisotropy that

Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss

arXiv:2402.00152v5 Announce Type: replace Abstract: Constructing the architecture of a neural network is a challenging pursuit for the machine learning community, and the dilemma of whether to go deep

Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals

arXiv:2606.10940v1 Announce Type: cross Abstract: Camera traps have become a cornerstone of biodiversity monitoring, but the artificial intelligence that turns vast quantities of images into usable ec

Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity

arXiv:2606.10198v1 Announce Type: cross Abstract: Hallucination detection in large language and vision-language models is increasingly framed as selective prediction, where a detector assigns a confid

Detecting Speculative Language in Biomedical Texts using Recurrent Neural Tensor Networks

arXiv:2606.10471v1 Announce Type: cross Abstract: In this investigation, we delve into the automated detection of speculative language within biomedical articles by utilizing distributed sentence repr

Do LLMsMakeNeural Distinguishers Wise?

arXiv:2606.10692v1 Announce Type: cross Abstract: Neural distinguishers are a cryptanalysis method for symmetric-key cryptography that trains machine learning models on pairs of plaintexts and ciphert

Do Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017

arXiv:2606.11098v1 Announce Type: cross Abstract: Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transfo

Drawing with Strangers: Population Scaling Drives Zero-Shot Mutual Intelligibility in Emergent Sketching

arXiv:2606.10582v1 Announce Type: cross Abstract: Generalization in emergent communication has largely focused on novel inputs or linguistic structures, yet the capacity for agents to communicate with

Duality for Optimal Multi-Item, Multi-Bidder Auction Design: Revenue Certificates through Deep Learning

arXiv:2606.10112v1 Announce Type: cross Abstract: Characterizing revenue-optimal auctions for multi-item, multi-bidder settings remains a fundamental open problem, with no known closed-form solution e

DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction

arXiv:2606.10243v1 Announce Type: new Abstract: Offsite conversion rate (OCVR) prediction is an important ranking problem in computational recommendation systems. This task presents a modeling challen

Dynamic Linear Attention

arXiv:2606.10650v1 Announce Type: cross Abstract: The scalability of Large Language Models (LLMs) to long contexts is fundamentally constrained by the quadratic complexity of standard attention, motiv

Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines

arXiv:2501.00745v3 Announce Type: replace-cross Abstract: The increasing integration of Large Language Model (LLM) based search engines has transformed the landscape of information retrieval. However,

Efficient AI-Inspired Reduction of Feynman Integrals via Tube Seeding

arXiv:2606.10698v1 Announce Type: cross Abstract: In this paper, we use machine learning to discover a new seeding strategy for integration-by-parts reduction of Feynman integrals, which is a frequent

Efficiently Learning Drifting Halfspaces with Massart Noise

arXiv:2606.11149v1 Announce Type: new Abstract: We study the problem of learning a drifting concept in the presence of Massart noise. In this framework, an online learner has access to a history of in

Embedding Hybrid Systems into Continuous Latent Vector Fields

arXiv:2606.10596v1 Announce Type: cross Abstract: This work proves that an n-dimensional hybrid system can be embedded into an m-dimensional Euclidean space equipped with a continuous vector field on

Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editing

arXiv:2606.10113v1 Announce Type: cross Abstract: This paper investigates whether LLM translations exhibit identifiable emotional profiles and how post-editing reshapes them toward human-like norms. W

Equanimity in HRI: Applying Calm Technology Principles to Human-Robot Interaction

arXiv:2606.09836v1 Announce Type: cross Abstract: This paper explores how {extit{Calm Technology}} can be integrated into Human-Robot Interaction (HRI), with a particular focus on the household enviro

researcharxiv-cs-ro

Exact Functional ANOVA Decomposition for Categorical Inputs Models

arXiv:2603.02673v2 Announce Type: replace-cross Abstract: Functional ANOVA offers a principled framework for interpretability by decomposing a model's prediction into main effects and higher-order int

Exploration of Foundation Model-Based Robots in Patient and Elderly Care

arXiv:2606.10208v1 Announce Type: cross Abstract: Demand for older-adult and patient care is growing rapidly as populations age worldwide. Foundation models are increasingly being integrated into robo

Exploring the Design Space of Reward Backpropagation for Flow Matching

arXiv:2606.11075v1 Announce Type: new Abstract: Aligning text-to-image flow matching models with human preferences via direct reward backpropagation is sample-efficient but hampered by two well-known

Express Language Modeling

arXiv:2606.10944v1 Announce Type: new Abstract: We introduce a new tool, Express, for converting a non-causal attention approximation into a causal approximation with matching approximation guarantees

FadeMem: Distance-Aware Memory Consolidation for Autoregressive Video Diffusion

arXiv:2606.10671v1 Announce Type: new Abstract: Autoregressive video generators synthesize long videos by generating successive temporal segments, but their historical KV cache grows with video length

FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching

arXiv:2606.10124v1 Announce Type: cross Abstract: Federated learning (FL) is often subject to aggregation variance if clients do not consistently participate in training rounds. While reusing stale mo

Few-step Generative Models as Lossy Compression

arXiv:2606.10450v1 Announce Type: new Abstract: DiffC provides a principled way to reuse pre-trained diffusion models for lossy compression, but its encoding and decoding procedures remain slow becaus

FG-Attn: Leveraging Fine-Grained Sparse Attention in Video Diffusion Models

arXiv:2509.16518v2 Announce Type: replace Abstract: Using diffusion transformers for media generation may require evaluating attention over extremely long sequences, with attention layers accounting f

First-Order Trajectory Matching: Fast Ensemble Predictions of Chaotic, Turbulent, Stochastic Systems

arXiv:2606.11138v1 Announce Type: new Abstract: We introduce First-Order Trajectory Matching (FTM), a surrogate-modeling method that learns the first-order local transport of probability mass from tra

Flexible Flows for Biological Sequence Design

arXiv:2606.10543v1 Announce Type: cross Abstract: Designing functional biological sequences requires navigating vast discrete spaces under strict evolutionary and biophysical constraints. Discrete Flo

FlexPath: Learned Semantic Path Priors for Image-Based Planning

arXiv:2606.10167v1 Announce Type: new Abstract: Recent learning-based path planners use neural networks to process visual map representations and approximate heuristics for classical search algorithms

FOGO: Forgetting-aware Orthogonalization Optimizer

arXiv:2606.10406v1 Announce Type: cross Abstract: We argue that forgetting is not confined to continual learning but is a general optimization phenomenon: during standard training, dominant mini-batch

From Context-Aware to Conflict-Aware: Generalizing Contrastive Decoding for Knowledge Conflict in LLMs

arXiv:2606.10298v1 Announce Type: new Abstract: When large language models generate from retrieved or augmented contexts, conflicts between external context and parametric priors remain a central reli

From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning

arXiv:2606.10595v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning. This paradigm enables privacy with multiple clien

From Genes to Tokens: a GWAS-inspired Approach for Interpretable Stylometric Analysis

arXiv:2606.09543v2 Announce Type: replace Abstract: This short paper introduces a stylometric interpretation method inspired by genome-wide association studies (GWAS). Each 'gene' token's association

researcharxiv-cs-cl