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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

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

DGX agent

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

researcharxiv-cs-cv

6 Oct 2026

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: A da Vinci Adapter for Serial-link and Humanoid Robots as an Accessible Platform for Surgical Robotics Research

arXiv:2610.05792v1 Announce Type: new Abstract: Robotic minimally invasive surgery offers well-documented clinical benefits, but the cost and infrastructure requirements of purpose-built platforms lim

researcharxiv-cs-ro

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

Delay-coordinate reconstruction and conditional-moment causal diagnostics in stochastic systems

arXiv:2610.05632v1 Announce Type: new Abstract: Partial observation and delay-coordinate reconstruction are rooted in deterministic dynamical-systems theory, whereas there are many systems with intrin

Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?

arXiv:2510.12680v2 Announce Type: replace Abstract: Hybrid thinking enables LLMs to switch between reasoning and direct answering, offering a balance between efficiency and reasoning capability. Yet o

Detecting Nighttime Anomalies from NASA Black Marble Using a Generalized Spatio-Temporally Robust Framework of Machine Leaning Ensembles

arXiv:2610.06674v1 Announce Type: cross Abstract: Nighttime lights from NASA's Black Marble product suite capture thermal and light emission signals from anomalous events including fires, volcanic eru

Diagnosis-Conditioned Spatial Gating and Decoder-Level Supervised Contrastive Learning for Radiology Report Generation

arXiv:2610.04159v1 Announce Type: new Abstract: Radiology report generation models can produce fluent text while still containing finding-level inaccuracies. Diagnosis-driven methods improve generatio

Differentially Private Mixing of Public Datasets Improves Private Learning

arXiv:2610.06636v1 Announce Type: new Abstract: Many machine learning applications involve sensitive data and therefore require training under differential privacy (DP). However, DP training often deg

Diffusion-Based Stress Testing of Overload Monitoring for Resilient Emergency Cellular Networks Using Internet CDR Proxies

arXiv:2610.04526v1 Announce Type: cross Abstract: Disasters can overload cellular control-plane signaling within minutes, yet fine-grained Radio Resource Control (RRC) or Next Generation (NG) Applicat

Dimension-Free Decentralized Nonsmooth Nonconvex Stochastic Optimization

arXiv:2610.05789v1 Announce Type: cross Abstract: We investigate decentralized nonsmooth nonconvex stochastic optimization over a network of n nodes, with the goal of finding an (elta,epsilon)-Goldste

dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra

arXiv:2610.06282v1 Announce Type: new Abstract: We introduce a novel invariance for peptide tandem mass spectrometry data, unlocking self-supervised representation learning that improves de novo seque

Discrete Action Matching: Learning Stochastic Dynamics from Samples via State Graphs

arXiv:2610.05071v1 Announce Type: new Abstract: Learning population dynamics from unpaired temporal marginals is an ill-posed inverse problem that requires structural assumptions on the underlying dyn

Distributed Cascade Force Control of Soft-Tactile-Based Multi-robot System for Object Transportation

arXiv:2610.04775v1 Announce Type: new Abstract: In this paper, we present a distributed cascade force control system (DCFC) for multiple robots with the aim of pushing a rigid object towards a desired

researcharxiv-cs-ro

Distribution Matching Evolutionary Algorithms for Rare Event Sampling

arXiv:2610.03833v1 Announce Type: cross Abstract: A novel discovery is one which is both useful and surprising: a generative model's output is a useful discovery if it has a low probability of being g

Divide-and-Conquer CoT: RL for Reducing Latency via Parallel Reasoning

arXiv:2601.23027v2 Announce Type: replace Abstract: Long chain-of-thought reasoning (Long CoT) is now fundamental to state-of-the-art LLMs, especially in mathematical reasoning. However, LLM generatio

Do RUL explanations hold up? Faithfulness and stability of attributions on C-MAPSS

arXiv:2610.04278v1 Announce Type: new Abstract: Deep remaining-useful-life (RUL) models on NASA C-MAPSS are now routine, and so are heatmaps that colour sensors and timesteps. A heatmap that looks mec

DynaMesh: Dynamic 3D Texture Generation

arXiv:2610.05529v1 Announce Type: new Abstract: We present DynaMesh, a dynamic texture generation method for 3D meshes. Given a textureless shape and a text prompt describing an effect, our method pro

Dynamic Minimax Regret Optimization for Robust LLM Post-Training

arXiv:2610.06329v1 Announce Type: new Abstract: Modern LLM training increasingly relies on heterogeneous data sources spanning different domains, tasks, preference distributions, and difficulty levels

Dynamic Time Step Prediction in Inverse Heat Dissipation for Blur-Like Image Restoration Tasks

arXiv:2610.03942v1 Announce Type: new Abstract: When using diffusion models to target image restoration problems, diffusion inversion is typically employed to retain relevant image information from th

DyQ-VLA: Temporal-Dynamic-Aware Quantization for Embodied Vision-Language-Action Models

arXiv:2603.07904v3 Announce Type: replace Abstract: Vision-language-action (VLA) models achieve favorable task performance, yet runtime errors in closed-loop execution evolve with alternating updates

EagleDepth: Efficient Fine-Grained Depth Estimation via Pixel Diffusion Decoder

arXiv:2610.04554v1 Announce Type: new Abstract: Recovering detailed geometry from high-resolution images is critical for precise perception of the surroundings and objects. However, existing methods w

EEGDM: Label-Efficient EEG Representation Learning with Generative Diffusion Model

arXiv:2508.14086v4 Announce Type: replace Abstract: Electroencephalography (EEG) is a critical tool for monitoring brain activity and diagnosing neurological disorders such as epilepsy. However, learn

Efficient Hierarchical Transformers for Representing Log Data

arXiv:2408.16803v2 Announce Type: replace Abstract: Transformers have gained widespread acclaim for their versatility in handling diverse data structures, yet their application to log data remains und