Search: “research”

Cross-Modal Action Recognition in Egocentric Video Using Mamba: Integrating RGB and Hand Skeleton Streams via CLS Token Fusion Strategies

arXiv:2605.24302v2 Announce Type: replace Abstract: Egocentric action recognition is a challenging task due to erratic camera motion, frequent hand occlusion, and the difficulty of maintaining consist

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CubePart: An Open-Vocabulary Part-Controllable 3D Generator

DGX agent

arXiv:2605.28763v1 Announce Type: new Abstract: Interactive 3D assets used in games and simulation are typically decomposed into specific semantic parts to support animation, physics, and scripted beh

researcharxiv-cs-ai

28 May 2026

CuriosAI Submission to the CASTLE Challenge at EgoVis 2026

arXiv:2605.27800v1 Announce Type: new Abstract: CASTLE 2026 asks 185 multiple-choice questions over 600+ hours of synchronized multi-view egocentric video. We explore two approaches on top of a shared

DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants

arXiv:2512.00252v4 Announce Type: replace-cross Abstract: Data assimilation (DA) is a cornerstone of scientific and engineering applications, combining model forecasts with sparse and noisy observatio

Debate Helps Weak Judges Reward Stronger Models

arXiv:2605.27483v1 Announce Type: cross Abstract: Despite theoretical promise, debate as a scalable oversight protocol has produced mixed empirical results: gains in some settings, and null effects in

Decoupling Variance and Scale-Invariant Updates in Adaptive Gradient Descent for Unified Vector and Matrix Optimization

arXiv:2602.06880v2 Announce Type: replace Abstract: Adaptive methods like Adam have become the extit{de facto} standard for large-scale vector and Euclidean optimization due to their coordinate-wise a

Deep Learning Strain Estimation: Is Physics-Based Simulation the Solution?

arXiv:2605.28697v1 Announce Type: cross Abstract: Speckle tracking echocardiography (STE) is the clinical standard for myocardial strain estimation. Despite good performance on global strain (GLS), it

Deep Neural Network Training as Random Effects: An Optimization-Inference Duality

arXiv:2605.27991v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have achieved remarkable empirical success, yet their training dynamics remain understood mainly from optimization rather

Density-aware Sample-specific Attack

arXiv:2605.27809v1 Announce Type: new Abstract: Despite recent progress in backdoor attacks, existing methods remain susceptible to post-training defenses that erase the backdoor through fine-tuning o

Design of a Real-time Asynchronous Monocular Odometry for Planetary Exploration

arXiv:2605.27661v1 Announce Type: new Abstract: We describe our preliminary design of a real-time asynchronous event-based monocular odometry for planetary exploration. Operating under strict computat

researcharxiv-cs-ro

Detecting Diffusion-Generated Time Series Under Generator Shift

arXiv:2605.28355v1 Announce Type: new Abstract: The boundary between real and diffusion-generated time series is becoming increasingly difficult to draw, yet detection in this domain remains underexpl

Developing an Intelligent Job Recommendation System Using Semantic Retrieval and Explainable AI Techniques

arXiv:2605.27656v1 Announce Type: cross Abstract: Online recruitment platforms require recommendation methods capable of retrieving relevant job opportunities from large and heterogeneous collections

🧱 DiffusionBlocks: Training Neural Networks One Block at a Time https://pub.sakana.ai/diffusionblocks

DiffusionBlocks is a novel training method that enables neural networks to be trained sequentially, one architectural block at a time, rather than end-to-end. This approach, developed by Sakana AI, le

researchdavid-ha--x

DODO: Discrete OCR Diffusion Models

arXiv:2602.16872v2 Announce Type: replace Abstract: Optical Character Recognition (OCR) is a fundamental task for digitizing information, serving as a critical bridge between visual data and textual u

Domain size asymptotics for Markov logic networks

arXiv:2509.04192v2 Announce Type: replace Abstract: A Markov logic network (MLN) M determines a probability distribution P_n^M on the set mathbf{W}_n of structures, or ``possible worlds'', with domain

Dynamic Topic Modeling with a Higher-Order Hypergraphical Representation

arXiv:2605.28269v1 Announce Type: new Abstract: Dynamic topic modeling is widely used to analyze evolving trends in scientific literature, medical records, and social media. Traditional topic models r

EAGer: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling

arXiv:2510.11170v2 Announce Type: replace-cross Abstract: With the rise of reasoning language models and test-time scaling methods as a paradigm for improving model performance, substantial computatio

EchoAvatar: Real-time Generative Avatar Animation from Audio Streams

arXiv:2605.28272v1 Announce Type: new Abstract: Real-time synthesis of high-fidelity 3D character motion from audio is a pivotal component for next-generation interactive avatars and virtual assistant

Economy then vs now: GDP growth: 2024 2.8% 2026 1.6% Inflation: 2024 2.9% 2026 3.8% Wages: 2024 rising faster than inflation 2026 rising slo…

Economy then vs now: GDP growth: 2024 2.8% 2026 1.6% Inflation: 2024 2.9% 2026 3.8% Wages: 2024 rising faster than inflation 2026 rising slower than inflation Unemployment rate: 2024 4% 2026 4.3% Jobs

researchyann-lecun--x

Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning

arXiv:2605.28409v1 Announce Type: new Abstract: Post-training using online reinforcement learning (RL) is an important training step for LLMs, including code-generating models. However, online RL for

EgoRelight: Egocentric Human Capture and Illumination Recovery for Relightable and Photoreal Avatar Rendering

arXiv:2605.28401v1 Announce Type: new Abstract: Mixed Reality (MR) headsets promise a future of immersive telepresence where virtual humans blend indistinguishably into real or virtual surroundings. A

EIT-Pneumatic Hybrid Robotic Skin for Practical and Accurate Force Map Reconstruction

arXiv:2605.28468v1 Announce Type: new Abstract: We present a hybrid robotic skin that combines electrical impedance tomography (EIT) with pneumatic tactile sensing to improve force reconstruction capa

researcharxiv-cs-ro

Elon Musk: If Kamala wins she'll hide the Epstein files, & be a threat to democracy. If Trump wins he'll release the Epstein files, & focus …

Elon Musk: If Kamala wins she'll hide the Epstein files, & be a threat to democracy. If Trump wins he'll release the Epstein files, & focus on the people. This idiot has NEVER been right about anythin

researchyann-lecun--x

Enhancing Ultra-low-field MRI with Segmentation-guided Adversarial Learning

arXiv:2605.28016v1 Announce Type: new Abstract: Ultra-low-field (ULF) MRI offers portable and low-cost imaging but suffers from poor image quality. To address this, we present our submission to the 20

Entropy-aware Masking for Masked Language Modeling

arXiv:2605.28526v1 Announce Type: new Abstract: Masked language modeling has become a standard pretraining objective for training encoder-based language models. In this approach, certain tokens in the

Entropy Distribution as a Fingerprint for Hallucinations in Generative Models

arXiv:2605.28264v1 Announce Type: new Abstract: Large Language Models (LLMs) often generate factually incorrect outputs, commonly termed hallucinations, that undermine trust and limit deployment in hi

Evaluating the Feasibility of Inferring Dietary Behavior Change Receptivity from Egocentric Images of Eating Environment

arXiv:2605.27950v1 Announce Type: new Abstract: Accurately assessing dietary behavior change receptivity is essential for designing effective just-in-time adaptive interventions (JITAIs) that promote

Evaluating the Generation Capabilities of Large Chinese Language Models

arXiv:2308.04823v5 Announce Type: replace Abstract: This paper unveils CG-Eval, the first-ever comprehensive and automated evaluation framework designed for assessing the generative capabilities of la

Event-based Motion & Appearance Fusion for 6D Object Pose Tracking

arXiv:2603.08264v2 Announce Type: replace Abstract: Object pose tracking is a fundamental and essential task for robotics to perform tasks in the home and industrial settings. The most commonly used s

Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State

arXiv:2603.14515v2 Announce Type: replace Abstract: Neural-network wave functions in Variational Monte Carlo (VMC) have achieved great success in accurately representing both ground and excited states

Explaining Digital Pathology Models via Clustering Activations

arXiv:2511.14558v2 Announce Type: replace Abstract: We present a clustering-based explainability technique for digital pathology models based on convolutional neural networks. Unlike commonly used met

Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers

arXiv:2605.28215v1 Announce Type: new Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples. Yet, how these models use th

Explanation Generation for Contradiction Reconciliation with LLMs

arXiv:2603.22735v2 Announce Type: replace Abstract: Existing NLP work commonly treats contradictions as errors to be resolved by choosing which statements to accept or discard. Yet a key aspect of hum

Explicit Critic Guidance for Aligning Diffusion Models

arXiv:2605.27736v1 Announce Type: cross Abstract: Online reinforcement learning is becoming increasingly important for aligning diffusion models with non-differentiable objectives. However, existing m

Expressive Power of Floating-Point Neural Networks with Arbitrary Reduction Orders and Inexact Activation Implementations

arXiv:2605.28704v1 Announce Type: new Abstract: Most existing expressivity theories for neural networks assume exact real arithmetic, whereas practical neural networks are executed under finite-precis

Extensions of Robbins-Siegmund Theorem with Applications in Reinforcement Learning

arXiv:2509.26442v2 Announce Type: replace Abstract: The Robbins-Siegmund theorem establishes the convergence of stochastic processes that are almost supermartingales and is one of the most commonly us

Extracting Small Translation Specialists from LLMs by Aggressively Pruning Experts

arXiv:2605.28042v1 Announce Type: cross Abstract: Modern large language models (LLMs) achieve state-of-the-art machine translation performance, but they do so as broad generalists largely trained for

Fast KV Compaction via Attention Matching

arXiv:2602.16284v2 Announce Type: replace Abstract: Scaling language models to long contexts is often bottlenecked by the size of the key-value (KV) cache. In deployed settings, long contexts are typi

FasterVAR: Plug-and-Play Acceleration for Visual Autoregressive Models

arXiv:2512.16483v2 Announce Type: replace Abstract: Visual Autoregressive (VAR) modeling departs from the next-token prediction paradigm of traditional Autoregressive (AR) models through next-scale pr

FEA-SLT: A Gloss-Free End-to-End Framework for Facial-Expression-Aware Sign Language Translation

arXiv:2601.03549v2 Announce Type: replace-cross Abstract: Sign Language Translation (SLT) is a challenging cross-modal task requiring joint modeling of manual articulations and non-manual signals. Exi

Fine-Tuning Dynamics of In-Context Factual Recall in Transformers

arXiv:2605.27774v1 Announce Type: new Abstract: In-context learning -- performing tasks based on examples given in the prompt -- is an important capability that has emerged in large language models an

FinTexTS: Financial Text-Paired Time-Series Dataset via Semantic-Based and Multi-Level Pairing

arXiv:2603.02702v3 Announce Type: replace Abstract: The financial domain involves a variety of important time-series problems. Recently, time-series analysis methods that jointly leverage textual and

Fitting Unknown Number of Hyperplanes with Manifold Optimization

arXiv:2605.28501v1 Announce Type: new Abstract: Fitting an unknown number of hyperplanes to data is a fundamental yet challenging problem in machine learning, characterized by its non-convexity, non-d

Flatness-Aware Stochastic Gradient Langevin Dynamics

arXiv:2510.02174v3 Announce Type: replace Abstract: Flatness of the loss landscape has been widely studied as an important perspective for understanding the behavior and generalization of deep learnin

Forget to Know, Remember to Use: Context-Aware Unlearning for Large Language Models

arXiv:2510.17620v2 Announce Type: replace Abstract: Large language models may encode sensitive information or outdated knowledge that needs to be removed, to ensure responsible and compliant model res

Frequency-Guided Action Diffusion via Sub-Frequency Manifold Traversal

arXiv:2605.27919v1 Announce Type: cross Abstract: Learning visuomotor policies via behavior cloning typically involves mimicking expert demonstrations collected by human operators. However, natural hu

From Kellgren-Lawrence to Calcium Pyrophosphate Crystal Deposition: A Soft-Labelling Framework for Knee Osteoarthritis Assessmen

arXiv:2605.28176v1 Announce Type: new Abstract: Background and objective. Conventional Deep Learning (DL) approaches for Knee Osteoarthritis (KOA) grading rely on one-hot labels, which fail to capture

From Passive Monitoring to Active Defence: Resilient Control of Manipulators Under Cyberattacks

arXiv:2603.13003v2 Announce Type: replace Abstract: Cyber-physical robotic systems are vulnerable to false data injection attacks (FDIAs), in which an adversary corrupts sensor signals while evading r

researcharxiv-cs-ro