Search: “research”

A Unifying Perspective on Descent Directions for Constrained Minimization and Switching Criteria

arXiv:2006.08426v5 Announce Type: replace-cross Abstract: Descent directions are fundamental to first-order methods for constrained convex optimization. In this work, we provide a unified perspective

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AdaEva: Accelerating LLM-Driven Algorithm Design with Adaptive Partial Evaluation

DGX agent

arXiv:2610.03896v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used for automated algorithm design. However the computational cost of evaluating the generated algorithms

researcharxiv-cs-lg

6 Oct 2026

AdaptInfer: Adaptive Token Pruning for Vision-Language Model Inference via Dynamical Text Guidance

arXiv:2508.06084v3 Announce Type: replace Abstract: Vision-language models (VLMs) have achieved impressive performance on multimodal inference tasks, but the cost remains a significant challenge due t

AgenticTactileVLA: Contact-Guided Execution-Time Supervision for Generalizable Dexterous Manipulation without VLA Retraining

arXiv:2610.04391v1 Announce Type: new Abstract: Vision-language-action policies may predict a transferable manipulation strategy yet fail to realize it reliably on the encountered object: objects comp

researcharxiv-cs-ro

Aligning the Query Space: Greedy Information Projection for Language Model Data Selection

arXiv:2603.13790v2 Announce Type: replace Abstract: Data selection for language models is often framed as balancing example quality and diversity. We argue that both are consequences of a more fundame

An equality condition for the Dobrushin bound on attention rollout and how often it holds in trained transformers

arXiv:2610.05558v1 Announce Type: new Abstract: The Dobrushin coefficient of each attention-rollout factor satisfies kappa(frac12(I+A))lefrac12(1+kappa(A)), and multiplying these inequalities over lay

Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusion

arXiv:2610.06094v1 Announce Type: new Abstract: Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy, but scatter, beam hardening, noise, truncation, and other artifacts li

AnchorGen: Anchored Optimization for Customizable Generative 3D Design

arXiv:2610.06135v1 Announce Type: new Abstract: Engineering design often starts from a 2D sketch that fixes style and proportions, yet the subsequent 3D shape optimization relies on learned generative

Anlu: Enabling In-Context Time Series Anomaly Detection in Foundation Models via Counterfactual Supervision

arXiv:2610.06180v1 Announce Type: new Abstract: Whether a time-series pattern is anomalous often depends on the operating regime of the monitored process. A missing event can signal a fault in one reg

Any-scale Object Detection using Arbitrary-scaled Images

arXiv:2610.04346v1 Announce Type: new Abstract: This paper proposes any-scale object detection using arbitrary-scale super-resolution for continuously rescaling object images, while general multi-scal

Application of sequence learning for predicting radiation damage of the CMS electromagnetic calorimeter

arXiv:2610.04058v1 Announce Type: cross Abstract: In this paper we use machine learning methods to predict radiation damage in the lead tungstate crystals of the CMS electromagnetic calorimeter at the

Arithmetic Actor Heads and Training Stabilization for Out-of-Distribution Reinforcement Learning

arXiv:2610.05143v1 Announce Type: new Abstract: Reinforcement learning (RL) policies can deteriorate under out-of-distribution (OOD) magnitude shifts. Starting from soft actor-critic (SAC) and its Bay

ARO: Aligned Representation learning for multi-Omics data

arXiv:2610.06443v1 Announce Type: new Abstract: The high cost of functional molecular assays, and prevalence of missing modalities and unmatched samples in computational biology, create significant ba

ASD-FEAT: A Multi-Modal Infant Video-Derived Dataset for Early ASD Risk Prediction

arXiv:2610.04051v1 Announce Type: new Abstract: Accurate early screening for Autism Spectrum Disorder (ASD) is a precursor to timely intervention, which is critical for improving cognitive and behavio

Asynchronous Tracking, Optical Communication and 3D Motion Capture using Event-based Sensors

arXiv:2610.04342v1 Announce Type: cross Abstract: Inter-robot communication is a key enabling technology in cooperative robotics applications. While wireless communication is ubiquitous, simple, and e

Attention-Based Surface Representation Learning for Robot State Prediction and Open-Ended Surface Classification

arXiv:2610.04240v1 Announce Type: cross Abstract: For ground robots operating in outdoor environments, understanding the properties of the underlying terrain is essential for ensuring reliable operati

BeliefGraph-JEPA: Structured Latent World Models for Action-Conditioned Time Series

arXiv:2610.05409v1 Announce Type: new Abstract: Action-conditioned time-series forecasting requires accounting for how future actions and exogenous forcings influence multiple targets through partiall

Ben Affleck reveals he writes Python, understands convolutional neural networks, worked extensively with GPUs, and used his celebrity status…

Ben Affleck reveals he writes Python, understands convolutional neural networks, worked extensively with GPUs, and used his celebrity status to get private looks at Google and OpenAI’s video models “I

researchyann-lecun--x

Beyond the Good, the Bad, and the Ugly: Colormap Assessment through Data-Aware Perceptual Metric

arXiv:2610.03720v1 Announce Type: cross Abstract: Continuous colormaps are widely used to visualize scalar fields, and their quality is typically evaluated using measures such as discriminative power

Billion-Scale Thumbnail Optimization for Uncurated Short-Form Videos via Multi-Armed Bandits

arXiv:2610.04931v1 Announce Type: new Abstract: This paper introduces a real-time thumbnail optimization system deployed at a global O(B) scale on a major short-form video platform. Unlike traditional

Calibrated Weak Supervision for Post-Harvest Burned-Cropland Mapping Under Label Scarcity

arXiv:2610.05040v1 Announce Type: new Abstract: Mapping post-harvest burned cropland is difficult when fires are small and fragmented and reliable labels are scarce. We developed a calibrated weak-sup

Casual Flash Lighting for Gaussian Splat Inverse Rendering

arXiv:2610.06035v1 Announce Type: new Abstract: Recovering geometry, materials, and lighting from photographs is highly ambiguous when only static illumination is available. Active-lighting setups red

CellSplat4D: PSF-Aware 4D Gaussian Splatting for Sparse Robotic Live-Cell Imaging

arXiv:2610.04199v1 Announce Type: new Abstract: A robotic microscope watching living cells cannot afford to look as often as it would like. Every volume it acquires costs photons the specimen does not

CentriQ: Calibration-Free Quantization of Diffusion Transformers via Exact Mean Centering

arXiv:2610.06260v1 Announce Type: new Abstract: Diffusion transformers (DiTs) achieve state-of-the-art image generation, but their sampling cost limits deployment. Quantizing both weights and activati

Checkable NTK Positivity and Finite-Width Gradient Descent for Scalar- and Vector-Valued PINNs with Strong-Form, Weak-Form, and Nonlocal Linear Constraints

arXiv:2610.04357v1 Announce Type: new Abstract: We give checkable positive-definiteness criteria for the limiting neural tangent kernel (NTK) and high-probability finite-width gradient-descent guarant

ChronoWorld: Camera-Controlled Consistent 4D World Generation via Spatiotemporal Cues and Geometric Reflections

arXiv:2610.06687v1 Announce Type: new Abstract: While existing camera-controllable video generation models can produce visually compelling sequences, preserving intrinsic 4D spatiotemporal coherence r

CleanMDM: Clean Motion Diffusion Model for Multimodal Motion Cleanup

arXiv:2610.05411v1 Announce Type: new Abstract: Motion capture data is rarely directly usable, as they typically exhibit missing segments, jitter, drift and contact artifacts. Traditionally, corrupted

Co-Optimizing Graph Sparsification and Approximate Computing for Energy-Efficient FPGA-Based GCN Inference

arXiv:2610.06138v1 Announce Type: new Abstract: Graph Convolutional Networks (GCNs) have emerged as a powerful framework for learning from graph-structured data, yet their deployment on resource-const

CoDance: Learning Reactive and Compliant Human-Humanoid Interaction from Video

arXiv:2610.05324v1 Announce Type: cross Abstract: Partnered human-humanoid interaction couples locomotion with continuous physical contact. A humanoid needs to coordinate with a person's motion while

Combining Improvements in Uplink AI-RAN

arXiv:2610.05936v1 Announce Type: cross Abstract: One of the major transformative factors in 6G will be the integration of Artificial Intelligence (AI) to become a native part of Radio Access Network

Compact set-valued deep ensembling in multi-class classification

arXiv:2610.05332v1 Announce Type: new Abstract: This paper tackles visible challenges in deep ensemble learning, where deep neural networks serve as ensemble members: training and storage burdens, and

COMPASS: Comet Object Measurement Pipeline with Automated Selection and Scoring

arXiv:2610.04680v1 Announce Type: new Abstract: Summary: The single-cell gel electrophoresis ('comet') assay is a widely used technique for quantifying DNA damage at the individual cell level. However

Conditional Flow Matching for Single-Neuron Electrophysiology: Capturing Multimodal Responses Across Stimuli

arXiv:2610.06520v1 Announce Type: new Abstract: Neurons of the brain exhibit a rich repertoire of electrophysiology dynamics with the same repeated stimulus eliciting very different voltage responses

Consideration Circuits: Depth Separation and Universality Beyond a Single Softmax

arXiv:2610.04143v1 Announce Type: new Abstract: Most feature-based choice models, classical and deep, score items and apply a single softmax. We introduce consideration circuits (CC), feature-based mo

Construction and Evaluation of Machine Learning Models for Near-Real-Time Fire Detection from MTG FCI Imagery

arXiv:2610.05154v1 Announce Type: new Abstract: Geostationary satellite observations are important for wildfire detection and monitoring. The current study evaluates machine learning models for MTG FC

Convergence of Statistical Estimators via Mutual Information Bounds

arXiv:2412.18539v3 Announce Type: replace-cross Abstract: Recent advances in statistical learning theory have revealed profound connections between mutual information (MI) bounds, PAC-Bayesian theory,

Cooperative Sheaf Neural Networks

arXiv:2507.00647v4 Announce Type: replace Abstract: Sheaf diffusion has recently emerged as a promising design pattern for graph representation learning due to its inherent ability to handle heterophi

Coupling Noisy Pairwise Knowledge to the DAG Posterior for Causal Discovery

arXiv:2610.04559v1 Announce Type: new Abstract: External causal reports can improve structure learning from limited observations, but their reliability varies across sources and variable pairs. We int

COVER: Learning to Accept More in Selective Sleep Staging

arXiv:2610.03911v1 Announce Type: new Abstract: Traditional sleep-staging methods apply the same model to every EEG epoch. Such uniform deployment expends computation on epochs that a smaller model co

Critical Points of Degenerate Metrics on Algebraic Varieties: A Tale of Overparametrization

arXiv:2512.21029v2 Announce Type: replace-cross Abstract: We study the critical points over an algebraic variety of an optimization problem defined by a quadratic objective that is degenerate. This sc

Cross-dataset harmonization for robust endoscopic image analysis

arXiv:2610.06663v1 Announce Type: new Abstract: A significant problem in endoscopic image analysis is that the machine learning (ML) models used for this purpose usually underperform when applied on i

Cross-Modal Contrastive Learning for the Retrieval of Immunotherapy-Associated Molecular Signatures from Histopathology

arXiv:2610.05157v1 Announce Type: new Abstract: Gastric Adenocarcinoma is a leading cause of cancer mortality. Although 'Inflamed/Non-Inflamed' subtypes have been proposed to predict immunotherapy res

Cross-Trait Transfer in Subliminal Learning

arXiv:2610.04260v1 Announce Type: new Abstract: Subliminal learning is a phenomenon where a student language model acquires a teacher model's behavioral traits by training on semantically unrelated ou

CT-Miner: Fast and Coarse-Grained Time-Series Pattern Mining via Cartesian Trees

arXiv:2610.05330v1 Announce Type: cross Abstract: Time series often contain recurring structural patterns, and efficiently mining such patterns into compact representations is essential for scalable a

D-DOIT: Training-free Adaptation of Discrete Diffusion via Doob's h-Transform

arXiv:2610.04938v1 Announce Type: new Abstract: We propose D-DOIT (Discrete Doob-Oriented Inference-time Transformation), a training-free and efficient adaptation method for discrete diffusion models

DASH: Fast, Valid Counterfactuals for Deep Networks via Batched Directional Search

arXiv:2610.04783v1 Announce Type: new Abstract: Counterfactual explanations are most useful when they can be generated with low latency, remain close to the factual input, and satisfy input-domain, ca

De-occluding broadband metalens

arXiv:2601.19403v2 Announce Type: replace-cross Abstract: Obstructions such as raindrops, fences, or dust degrade captured images, especially when mechanical cleaning is infeasible. Conventional solut

Deep Learning for Sleep Heart Rate Estimation from Accelerometers: Toward Population-Scale Cardiac Insight Without Optical Sensors

arXiv:2610.06823v1 Announce Type: new Abstract: Large longitudinal cohorts often contain wrist accelerometry without optical heart-rate sensing, motivating recovery of cardiac information from motion