research

Decoupling Stability and Plasticity for Multi-Modal Test-Time Adaptation

arXiv:2603.00574v2 Announce Type: replace-cross Abstract: Adapting pretrained multi-modal models to evolving test-time distributions, known as multi-modal test-time adaptation, presents a significant

Research

DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features

DGX agent

arXiv:2605.15009v1 Announce Type: new Abstract: The detection of Alzheimers disease (AD) is considered crucial, as timely intervention can improve patient outcomes. Electroencephalogram (EEG)-based di

researcharxiv-cs-lg

15 May 2026

Diagnosing and Correcting Concept Omission in Multimodal Diffusion Transformers

arXiv:2605.14270v1 Announce Type: new Abstract: Multimodal Diffusion Transformers (MM-DiTs) have achieved remarkable progress in text-to-image generation, yet they frequently suffer from concept omiss

Do Composed Image Retrieval Benchmarks Require Multimodal Composition?

arXiv:2605.14787v1 Announce Type: cross Abstract: Composed Image Retrieval (CIR) is a multimodal retrieval task where a query consists of a reference image and a textual modification, and the goal is

Do We Really Need External Tools to Mitigate Hallucinations? SIRA: Shared-Prefix Internal Reconstruction of Attribution

arXiv:2605.14621v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) often hallucinate when language priors dominate weak or ambiguous visual evidence. Existing contrastive decoding

Does Local News Stay Local?: Online Content Shifts in Sinclair-Acquired Stations

arXiv:2510.07060v2 Announce Type: replace Abstract: Local news stations are often considered to be reliable sources of non-politicized information, particularly local concerns that residents care abou

DRL-STAF: A Deep Reinforcement Learning Framework for State-Aware Forecasting of Complex Multivariate Hidden Markov Processes

arXiv:2605.14632v1 Announce Type: new Abstract: Forecasting multivariate hidden Markov processes is challenging due to nonlinear and nonstationary observations, latent state transitions, and cross-seq

Dual Ascent Diffusion for Inverse Problems

arXiv:2505.17353v2 Announce Type: replace-cross Abstract: Ill-posed inverse problems are fundamental in many domains, ranging from astrophysics to medical imaging. Emerging diffusion models provide a

Dual-Dimensional Consistency: Balancing Budget and Quality in Adaptive Inference-Time Scaling

arXiv:2605.15100v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable abilities in reasoning. However, maximizing their potential through inference-time scaling fac

Dual-Latent Collaborative Decoding for Fidelity-Perception Balanced Image Compression

arXiv:2605.14391v1 Announce Type: new Abstract: Learned image compression (LIC) increasingly requires reconstructions that balance distortion fidelity and perceptual realism across a wide range of bit

DUET: Dual-Paradigm Adaptive Expert Triage with Single-cell Inductive Prior for Spatial Transcriptomics Prediction

arXiv:2605.14104v1 Announce Type: new Abstract: Inferring spatially resolved gene expression from histology images offers a cost-effective complement to spatial transcriptomics (ST). However, existing

Dynamic Latent Routing

arXiv:2605.14323v1 Announce Type: cross Abstract: We investigate the temporal concatenation of sub-policies in Markov Decision Processes (MDP) with time-varying reward functions. We introduce General

EARL: Towards a Unified Analysis-Guided Reinforcement Learning Framework for Egocentric Interaction Reasoning and Pixel Grounding

arXiv:2605.14742v1 Announce Type: new Abstract: Understanding human--environment interactions from egocentric vision is essential for assistive robotics and embodied intelligent agents, yet existing m

EBM are so back! @ylecun has been pointing here for years: AI reasoning needs systems that check structure before they answer. Aleph from @l…

EBM are so back! @ylecun has been pointing here for years: AI reasoning needs systems that check structure before they answer. Aleph from @logic_int now leads the major formal reasoning benchmarks – l

researchyann-lecun--x

EBMs are so back! Aleph now leads the major formal reasoning benchmarks.

Energy-Based Models (EBMs) have demonstrated renewed competitiveness in formal reasoning tasks, with Aleph achieving leading performance on major benchmarks in this domain. This represents a significa

researchyann-lecun--x

Efficient Dense Matching for Enhanced Gaussian Splatting Using AV1 Motion Vectors

arXiv:2605.14629v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) has emerged as a prominent framework for real-time, photorealistic scene reconstruction, offering significant speed-ups o

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits

arXiv:2605.14553v1 Announce Type: cross Abstract: Prompt engineering has become central to eliciting the capabilities of large language models (LLMs). At its core lies prompt selection -- efficiently

Embedding Perturbation may Better Reflect Intermediate-Step Uncertainty in LLM Reasoning

arXiv:2602.02427v2 Announce Type: replace Abstract: Large language Models (LLMs) have achieved significant breakthroughs across diverse domains; however, they can still produce unreliable or misleadin

Emotion-Attended Stateful Memory (EASM):The Architecture for Hyper-Personalization at Scale

arXiv:2605.14833v1 Announce Type: new Abstract: Current language model systems remain fundamentally stateless across sessions, limiting their ability to personalize interactions over time. While retri

Energy-Efficient Quadruped Locomotion with Compliant Feet

arXiv:2605.14411v1 Announce Type: cross Abstract: Quadruped robots are often designed with rigid feet to simplify control and maintain stable contact during locomotion. While this approach is straight

Enjoy Your Layer Normalization with the Computational Efficiency of RMSNorm

arXiv:2605.14521v1 Announce Type: new Abstract: Layer normalization (LN) is a fundamental component in modern deep learning, but its per-sample centering and scaling introduce non-negligible inference

Ergodic Imitation for Adaptive Exploration around Demonstrations

arXiv:2605.13996v1 Announce Type: new Abstract: In robotics, a common challenge in imitation learning is the mismatch between training and deployment conditions, caused, for example, by environmental

researcharxiv-cs-ro

EverAnimate: Minute-Scale Human Animation via Latent Flow Restoration

arXiv:2605.15042v1 Announce Type: cross Abstract: We propose EverAnimate, an efficient post-training method for long-horizon animated video generation that preserves visual quality and character ident

Explainable Detection of Depression Status Shifts from User Digital Traces

arXiv:2605.14995v1 Announce Type: new Abstract: Every day, users generate digital traces (e.g., social media posts, chats, and online interactions) that are inherently timestamped and may reflect aspe

Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers

arXiv:2605.14855v1 Announce Type: cross Abstract: Forecasting within signal processing pipelines is crucial for mitigating delays, particularly in predicting the dynamic movements of objects such as N

Exploring Bottlenecks in VLM-LLM Navigation: How 3D Scene Understanding Capability Impacts Zero-Shot VLN

arXiv:2605.14801v1 Announce Type: new Abstract: Zero-shot vision-and-language navigation (VLN) has gained significant attention due to its minimal data collection costs and inherent generalization. Th

researcharxiv-cs-ro

FaceParts: Segmentation and Editing of Gaussian Splatting

arXiv:2605.13853v1 Announce Type: cross Abstract: Facial editing is an important task with applications in entertainment, virtual reality, and digital avatars. Most existing approaches rely on generat

FactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Grounding

arXiv:2602.03417v2 Announce Type: replace Abstract: Large language models hallucinate factual claims and struggle to ground their outputs in retrievable evidence, particularly in non-English languages

Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding

arXiv:2605.14305v1 Announce Type: new Abstract: Discrete diffusion language models improve generation efficiency through parallel token prediction, but standard X_0 prediction methods introduce factor

FactorizedHMR: A Hybrid Framework for Video Human Mesh Recovery

arXiv:2605.14854v1 Announce Type: cross Abstract: Human Mesh Recovery (HMR) is fundamentally ambiguous: under occlusion or weak depth cues, multiple 3D bodies can explain the same image evidence. This

Fast Adversarial Attacks with Gradient Prediction

arXiv:2605.14868v1 Announce Type: new Abstract: Generating adversarial examples at scale is a core primitive for robustness evaluation, adversarial training, and red-teaming, yet even 'fast' attacks s

Feature Visualization Recovers Known Cortical Selectivity from TRIBE v2

arXiv:2605.13904v1 Announce Type: cross Abstract: Brain encoder models predict cortical fMRI responses from the internal activations of pretrained vision and language networks, and are typically evalu

Few Channels Draw The Whole Picture: Revealing Massive Activations in Diffusion Transformers

arXiv:2605.13974v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) and related flow-based architectures are now among the strongest text-to-image generators, yet the internal mechanisms t

Finite Sample Bounds for Learning with Score Matching

arXiv:2605.14168v1 Announce Type: new Abstract: Learning of continuous exponential family distributions with unbounded support remains an important area of research for both theory and applications in

Frequency-adaptive tensor neural networks for high-dimensional multi-scale problems

arXiv:2508.15198v2 Announce Type: replace Abstract: Tensor neural networks (TNNs) have demonstrated their superiority in solving high-dimensional problems. However, similar to conventional neural netw

From Data to Action: Accelerating Refinery Optimization with AI

arXiv:2605.15085v1 Announce Type: cross Abstract: Nowadays refinery optimization utilizes sheer amounts of data, which can be handled with modern Linear Programming (LP) software, but the interpreting

From Table to Cell: Attention for Better Reasoning with TABALIGN

arXiv:2605.14465v1 Announce Type: new Abstract: Multi-step LLM reasoning over structured tables fails because planning and execution share no explicit cell-grounding contract. Existing methods constra

From User Preferences to Base Score Extraction Functions in Gradual Argumentation (with Appendix)

arXiv:2602.14674v4 Announce Type: replace Abstract: Gradual argumentation is a field of symbolic AI which is attracting attention for its ability to support transparent and contestable AI systems. It

FU-MPC: Frontier- and Uncertainty-Aware Model Predictive Control for Efficient and Accurate UAV Exploration with Motorized LiDAR

arXiv:2605.14920v1 Announce Type: new Abstract: Efficient UAV exploration in unknown environments requires rapid coverage expansion while maintaining accurate and reliable localization, since safe nav

researcharxiv-cs-ro

Fun interview with Jacob Effron on the Unsupervised Learning podcast.

Fun interview with Jacob Effron on the Unsupervised Learning podcast. It’s hard to imagine more of a dream Unsupervised Learning guest than @ylecun. Yann is one of the godfathers of AI, and he has som

researchyann-lecun--x

Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs

arXiv:2410.06431v5 Announce Type: replace Abstract: Accurate uncertainty quantification in large language models (LLMs) is essential for reliable confidence estimation, yet fine-tuned LLMs often becom

Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability

researchmicrosoft-research

Our recent paper, “LLMs Corrupt Your Documents When You Delegate”, has generated discussion about the reliability of AI systems in delegated workflows. We appreciate the interest in this work and want

Generalized Priority-Aware Shapley Value

arXiv:2605.15018v1 Announce Type: cross Abstract: Shapley value and its priority-aware extensions are widely used for valuation in machine learning, but existing methods require pairwise priority to b

Generalizing Score-based generative models for Heavy-tailed Distributions

arXiv:2603.00772v2 Announce Type: replace-cross Abstract: Score-based generative models (SGMs) have achieved remarkable empirical success, motivating their application to a broad range of data distrib

Generating HDR Video from SDR Video

arXiv:2605.14703v1 Announce Type: new Abstract: The high dynamic range (HDR) video ecosystem is approaching maturity, but the problem of upconverting legacy standard dynamic range (SDR) videos persist

Generative Bayesian Optimization: Generative Models as Acquisition Functions

arXiv:2510.25240v3 Announce Type: replace-cross Abstract: We present a general strategy for turning generative models into candidate solution samplers for batch Bayesian optimization (BO). The use of

Generative Floor Plan Design with LLMs via Reinforcement Learning with Verifiable Rewards

arXiv:2605.14117v1 Announce Type: cross Abstract: An AI system for professional floor plan design must precisely control room dimensions and areas while respecting the desired connectivity between roo

Geometry-Aware Decoding with Wasserstein-Regularized Truncation and Mass Penalties for Large Language Models

arXiv:2602.10346v2 Announce Type: replace Abstract: Large language models (LLMs) must balance diversity and creativity against logical coherence in open-ended generation. Existing truncation-based sam