Search: “research”

Same Target, Different Origins: Aligning Latent Rollout for Consistent PDE Emulation

arXiv:2610.21370v1 Announce Type: new Abstract: Neural PDE solvers commonly generate multi-step predictions through autoregressive rollout, recursively applying a learned evolution operator and feedin

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SATOR: Backpropagation-Free 3D Test-Time Adaptation via Sample-Adaptive Token Routing

DGX agent

arXiv:2610.21367v1 Announce Type: new Abstract: Test-time adaptation (TTA) mitigates the performance degradation of source-pretrained models on unlabeled test data under distribution shifts. Recent ba

researcharxiv-cs-cv

16 Oct 2026

SCCL-DT: Semantic-Conflict-Aware Contrastive Learning with Dual-Rule Teacher Distillation for Zero-Shot Chest X-Ray Diagnosis

arXiv:2610.22094v1 Announce Type: new Abstract: Contrastive vision-language pre-training supports zero-shot chest X-ray diagnosis, but standard objectives treat every off-diagonal image-report pair in

Scene Reconstruction Using Ubiquitous Wireless Signals via Diffusion Posterior Inference Over physics-Grounded Representations

arXiv:2610.22120v1 Announce Type: new Abstract: Knowing the layout of the physical world matters for many applications, yet the sensors we rely on for it (e.g., cameras and LiDAR) can fail in darkness

SceneWeave: Audience-Guided Scene Exploration for Dramatic Adaptation

arXiv:2610.21737v1 Announce Type: new Abstract: Adapting prose into a script requires transforming narration and inner experience into scenes that convey relationships and psychological contradictions

Second Maximum of a Gaussian Random Field and Exact (t-)Spacing test

arXiv:2406.18397v4 Announce Type: replace-cross Abstract: In this article, we introduce the novel concept of the second maximum of a Gaussian random field defined on a Riemannian manifold: Once the fi

Seeing Both the Forest and the Trees: Reusing Holistic 3D Priors for Part-Decomposed Generation

arXiv:2610.21850v1 Announce Type: new Abstract: Generating 3D assets with explicit parts requires two properties at once: geometric detail inside each part and structural coherence across the whole ob

SelectPFN: Amortized Bayesian Variable Selection for Sparse Nonlinear Regression with Prior-Data Fitted Networks

arXiv:2610.21950v1 Announce Type: cross Abstract: Tabular foundation models based on prior-data fitted networks (PFNs) predict on a new data set in a single forward pass, but they cannot report which

Set Membership Estimation for Stochastic Linear Contextual Bandits

arXiv:2606.20022v2 Announce Type: replace-cross Abstract: Contextual bandit learning has attracted increasing attention in engineering applications due to its ability to incorporate side/context infor

Sharp and Stable Label-Free Calibration Certificates from Logical Coherence

arXiv:2610.21567v1 Announce Type: new Abstract: Query-conditioned probabilistic predictors answer logically related questions one at a time, and labels to check their calibration are often missing. Fo

SHIP: Structured and Hierarchical Insight with Privacy

arXiv:2610.21048v1 Announce Type: new Abstract: Extracting insights from textual data while protecting user privacy is increasingly important. Existing methods produce private insights in diverse form

SIEVE: Overcoming Topological Obstruction in Equivariant Self-Supervised Learning

arXiv:2610.21087v1 Announce Type: cross Abstract: We identify a theoretical incompatibility between 3D point cloud masked autoencoding and SE(3)-equivariant networks. We prove that standard masking tr

Single-stage joint diffusion of sky images and clear-sky index for ultra-short-term irradiance forecasting: what the frame-to-irradiance stage discards

arXiv:2610.21795v1 Announce Type: new Abstract: Sky-image irradiance forecasting is dominated by a two-stage design: a video-prediction model forecasts future sky frames, and a separately trained regr

Sliced Wasserstein Sketches with Error Certificates

arXiv:2610.20788v1 Announce Type: new Abstract: Compact representations that support geometrically meaningful comparisons are valuable for learning from probability distributions. We study optimal, fr

Slip-Induced Fall Suppression During Cornering for a Remotely Operated Motorcycle with a Rear-Wheel-Swing Mechanism

arXiv:2610.21089v1 Announce Type: new Abstract: Slip-induced falls when riding motorcycles are difficult to avoid even for expert riders and represent a major cause of single-vehicle accidents. Despit

researcharxiv-cs-ro

SNOAR: Self-training with Noise filtering, Observation adding, and Remixing for Speech Denoising Without Clean References

arXiv:2610.20863v1 Announce Type: cross Abstract: Unsupervised speech denoising methods such as MixIT and noisy-target training can be trained directly on noisy recordings, avoiding the sim-to-real ga

SoftCache: Adaptive Step Size Caching for Diffusion Transformers

arXiv:2610.22369v1 Announce Type: new Abstract: Diffusion and flow models generate a sample over iterative steps, each a full pass through a large network, and that pass dominates generation cost. Tra

Sparse Attention for Video Generation Acceleration via Growing Sparsity and Reduced Search

arXiv:2610.21129v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) dominate video generation but suffer from high inference cost due to the quadratic complexity of self-attention. Sparse at

Sparse Federated Echo State Networks for Time-Series Classification via Smooth-L1 Regularization

arXiv:2610.21742v1 Announce Type: new Abstract: Echo State Networks (ESNs), as lightweight implementations of reservoir computing, provide efficient solutions for temporal data processing in resource-

Spectral Indistinguishability and Learning-Curve Identifiability in Quadratic Networks

arXiv:2610.20678v1 Announce Type: new Abstract: We study what spectral summaries and scalar learning curves reveal about centered quadratic networks. We construct analytic positive input densities wit

Speculative Forcing for Efficient Autoregressive Video Generation

arXiv:2610.22347v1 Announce Type: new Abstract: Few-step autoregressive video generators enable streaming generation, yet they run every token through the final denoising step, regardless of how much

Stateful Testing for Deep Learning Compilers

arXiv:2610.22003v1 Announce Type: cross Abstract: Modern deep learning compilers compile models lazily and cache artifacts across invocations. Retained compiler state can make a later invocation incor

StochLOB: Heavy-Tailed Score Matching for Robust Limit Order Book Trend Prediction

arXiv:2610.21081v1 Announce Type: new Abstract: Score-based diffusion methods usually perturb data with Brownian Gaussian noise, corresponding to a light-tailed forward stochastic differential equatio

SurgStreamer: proactive, request-driven monitoring of surgical video streams with structured state and a reusable reasoning layer

arXiv:2610.21895v1 Announce Type: new Abstract: Surgical teams could benefit from assistants that watch the video for requested events, such as 'tell me when the clip applier appears during Calot tria

T-GADE: Thermodynamical Generative-AI-Driven Evolution of LLM Artifacts

arXiv:2609.12286v2 Announce Type: replace Abstract: Introducing large language models (LLMs) into evolutionary computation enables semantic generation of candidates, but the classical problem of maint

TASTE: Task-Aware Out-of-Distribution Detection via Stein Operators

arXiv:2602.07640v2 Announce Type: replace Abstract: Out-of-distribution detection methods are often either data-centric, detecting deviations from the training input distribution irrespective of their

Temporal Periodic Image Registration with Implicit Neural Representations

arXiv:2610.21845v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) have recently gained popularity for their ability to model functions using simple and lightweight networks. These

The Blume-Capel Model as a phi^6 Field Theory: A Monte Carlo and Flow Model Study

arXiv:2610.20982v1 Announce Type: cross Abstract: We report Monte Carlo simulations of a continuous spin variant of the Blume-Capel model. We compute the phase diagrams in the crystal-field-temperatur

The Right Byte First: How UTF-16 Helps Multilingual Boundary Prediction

arXiv:2610.22071v1 Announce Type: new Abstract: Many hierarchical byte-level language models group bytes into patches using a learned boundary predictor whose view of the next codepoint depends on enc

The Tilted Sampling Problem: Optimism, Posterior Sampling, and Logarithmic Regret

arXiv:2610.21908v1 Announce Type: new Abstract: Many modern learning systems are used not to select a single best action, but to sample diverse high-quality outputs. Examples include entropy-regulariz

The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock

arXiv:2610.20665v1 Announce Type: new Abstract: Large language model (LLM) agents can solve complex interactive tasks, yet reasoning and tool calls take time. In continuously changing environments, an

Thermodynamically Consistent Machine Learning for Fracture Prediction in Heterogeneous Materials

arXiv:2610.20614v1 Announce Type: cross Abstract: Understanding fracture behavior is critical for assessing the structural integrity and longevity. Highly time-consuming physics-based fracture simulat

Time-Optimal Ergodic Retrieval

arXiv:2610.21823v1 Announce Type: new Abstract: Retrieval tasks are a form of search task where a robot must explore, find an object, and return with it as quickly as possible. Recent work in ergodic

researcharxiv-cs-ro

Towards automatic digital garment assembly

arXiv:2610.20651v1 Announce Type: new Abstract: Creating digital garments from existing two-dimensional patterns remains a time-consuming process. Although patterns are widely available in digital for

Towards Better Aligned LLM Judges for Radiology Report Evaluation across Modalities and Anatomies

arXiv:2604.03376v2 Announce Type: replace Abstract: Current work on radiology report evaluation has focused primarily on LLM-based metrics for chest X-rays (CXR), leaving open whether such approaches

Two Sample Sizes in Test-Time LLM Ensembling: Buffered ERM and Minimax Theory

arXiv:2610.21363v1 Announce Type: cross Abstract: Test-time scaling can improve the performance of large language models by generating multiple answers to a question and aggregating them. Weighted ens

Two-Stage Score Estimation for Group-Specific Diffusion Models

arXiv:2610.20979v1 Announce Type: cross Abstract: Generative modeling across related distributions raises a question: how can shared structure support accurate generation for a target distribution wit

Uncertainty-Aware Federated Learning for Joint Communication and Computation in XR Systems

arXiv:2610.21969v1 Announce Type: cross Abstract: The convergence of communication and computation, empowered by artificial intelligence (AI) enhanced capabilities, emerges as a key enabler for realiz

Unfragmenting Nawatl NMT: Single-Variant Specialization for Masewalkopa

arXiv:2610.22110v1 Announce Type: new Abstract: In minoritized (low-resource) language technologies, a common response to data sparsity is to aggregate text across multiple varieties and treat the com

Unsupervised Clustering of Public Transport Vehicles Using Deep Morphological Features and Color Histograms

arXiv:2610.22270v1 Announce Type: new Abstract: Surveillance cameras along urban roads produce large volumes of unlabeled video that could support the monitoring of public transport, but grouping the

V-SHARE: Provable Convergence Guarantees for Advantage-based Algorithms

arXiv:2610.21448v1 Announce Type: cross Abstract: In reinforcement learning, advantage-based value-learning methods, unlike standard Q-learning, share information across actions by decomposing the act

Vertical Sliced Wasserstein Distances for Meta-Measures

arXiv:2610.20743v1 Announce Type: new Abstract: Many modern learning problems require comparing probability distributions whose individual observations are themselves probability measures, naturally g

When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus

arXiv:2610.20676v1 Announce Type: new Abstract: Correctness feedback suppresses the very evidence that specification-completeness auditing is designed to uncover: programs that satisfy a written contr

When Judges Agree and Are Wrong: Consistency-Accuracy Dissociation as a Blind Spot for Label-Free Evaluation

arXiv:2610.21128v1 Announce Type: cross Abstract: Label-free evaluation methods, such as self-consistency filtering, majority-vote aggregation, and agreement-based reliability scoring, rely on an impl

When Reasoning Traces Conflict with Evidence: How Answer Configuration Shapes Evidence Following

arXiv:2610.20805v1 Announce Type: new Abstract: A reasoning trace retained at answer time plays two roles: it can raise accuracy, and it can compete with the current evidence when the two disagree. We

When Should a Low-Light Pipeline Generate? Auditable Risk Control for Selective Generative Enhancement

arXiv:2610.21335v1 Announce Type: new Abstract: Generative low-light enhancers recover plausible texture but may hallucinate structure; restoration networks preserve structure but can leave regions un

Where Is the 'Self' of a Large Language Model? -- From Self-Index to Self-Reference

arXiv:2610.16717v2 Announce Type: replace Abstract: Does a large language model have a self, and if so, of what kind? We separate two functional levels familiar from biology: a self-index, which disti

Which Tokens to Merge? Diffusion Dynamics for Efficient Image Generation

arXiv:2610.22370v1 Announce Type: new Abstract: Token merging has emerged as an effective training-free strategy for accelerating diffusion models by reducing redundant token computation while minimiz