Search: “inference”

Controllable Diverse Sampling through Pullback Directions

arXiv:2610.14294v1 Announce Type: cross Abstract: Conditional diffusion models can generate high-quality images that align closely with a given prompt. However, samples generated from different random

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Correlation-Aware Reconstruction for Multivariate Time Series Anomaly Detection

DGX agent

arXiv:2610.15250v1 Announce Type: new Abstract: Reconstruction-based detectors for multivariate time series anomaly detection typically learn a single entangled representation of normal behavior and f

model-releasesarxiv-cs-lg

14 Oct 2026

DeepFlow: Two Convergent Differentiable Approaches for Pairwise Conditional Random Fields

arXiv:2610.16546v1 Announce Type: new Abstract: Basic probabilistic models form the basis of modern deep learning largely by defining the final activation layer of a network and allowing the networks

Hardware

Denoise X: Self-Supervised U-Net for Quantum Mottle Suppression in Chest Radiography

arXiv:2610.18556v1 Announce Type: new Abstract: Deep learning for medical image restoration is often constrained by the need for paired training data. Acquiring perfectly registered clean-noisy radiog

hardwarearxiv-cs-cv

Estimating the Causal Impact of Social Navigation Signals in Corridor Scenarios

arXiv:2610.16411v1 Announce Type: new Abstract: Deploying robots in human-shared environments requires deciding not only how to navigate, but also when dedicated social signals are worth emitting. Cla

safetyarxiv-cs-ro

FEM-Validated Real-Time Modelling of Soft Tactile Deformation

arXiv:2610.16667v1 Announce Type: new Abstract: Accurate modelling of soft tactile sensors remains difficult because finite element methods (FEM) are computa- tionally expensive, while real-time simul

local-aiarxiv-cs-ro

FrameLite: Decision-Oriented Aesthetic Cropping with a Compact Vision-Language Model

arXiv:2610.17639v1 Announce Type: new Abstract: Aesthetic image cropping improves composition by balancing subjects, context, and image boundaries. Large vision-language models bring semantic knowledg

Tutorials

From Pixels to Executable Physics: Behavior-Grounded World Models for Embodied Action Consequence Prediction

arXiv:2610.17630v1 Announce Type: cross Abstract: In outdoor environments, varying terrain and contact conditions can cause identical action commands to produce different motion outcomes for quadruped

tutorialsarxiv-cs-cv

Game-Theoretic Reinforcement Learning based Topology-Agnostic Defense for Virtual Power Plants

arXiv:2610.15183v1 Announce Type: new Abstract: The rapid proliferation of distributed energy resources (DERs) aggregated by virtual power plants (VPPs) elevates cybersecurity risk by expanding the at

Graph3R: Unifying View Graph Initialization with Feed-Forward Reconstruction

arXiv:2610.16272v1 Announce Type: new Abstract: Feed-forward reconstruction recovers camera poses and dense geometry from image sets whose views are assumed to come from a single scene. In practice, u

GuidanceNFT: NFT-Based Reward Fine-Tuning of Guided Diffusion Models

arXiv:2610.15856v1 Announce Type: new Abstract: DiffusionNFT (NFT) fine-tunes diffusion models toward task-specific rewards, but optimizing these rewards does not guarantee fidelity to the full prompt

HairFlow: Modeling 3D Hair Strands with Multi-Modal Conditioned Diffusion

arXiv:2610.17348v1 Announce Type: new Abstract: Modeling 3D hair strands is challenging due to complex hairstyle geometry, scarce high-quality training data, and incomplete or noisy input observations

Heuristics are Not Enough: Calibrated and Corrected Caching for Video Diffusion Acceleration

arXiv:2610.16545v1 Announce Type: new Abstract: Feature caching has become a mainstream training-free strategy for accelerating video diffusion models by reusing intermediate outputs across denoising

Industry

How FactSet cut model training and deployment from days to hours with Databricks AI Runtime

FactSet leveraged the Databricks AI Runtime to cut end‑to‑end model training and deployment times from days to hours. By adopting high‑performance GPU clusters, managed data pipelines, and automated n

industrydatabricks

InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection

arXiv:2610.15032v1 Announce Type: new Abstract: Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in which

Interaction-as-a-Judge

arXiv:2610.17895v1 Announce Type: new Abstract: Coding agents are tasked with producing sophisticated interactive applications such as websites, desktop applications, and games. While humans assess th

Jag: Direct Box Prediction for Efficient Visual Grounding

arXiv:2610.13239v2 Announce Type: replace Abstract: Visual grounding combines image--language understanding with geometric prediction, yet autoregressive multimodal models often express a box as a coo

Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models

arXiv:2610.18001v1 Announce Type: new Abstract: Diffusion language models (DLMs) enable flexible parallel generation by resolving tokens according to the model's evolving predictability rather than a

Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies

arXiv:2610.14406v1 Announce Type: cross Abstract: It is widely appreciated that robotic policies require long-term memory to perform complex tasks. However, directly conditioning policies on all past

Optimal Transport PAC-Bayes Bounds

arXiv:2610.16560v1 Announce Type: new Abstract: PAC-Bayesian generalization bounds are among the strongest distribution-dependent guarantees in statistical learning and are typically expressed in term

PATTERN Can Be Solved from Memory: Generator-Aware Baselines for Synthetic Node Classification

arXiv:2610.15020v1 Announce Type: new Abstract: PATTERN, a synthetic node-classification benchmark still used to rank graph networks, plants a random subgraph in a random host graph and asks which nod

Perception Beyond the Visible: Incompleteness-Aware Scene Understanding for Robot Autonomy

arXiv:2610.18670v1 Announce Type: cross Abstract: Reliable robot autonomy requires reasoning about the world from partial and noisy observations. However, many perception systems model only visible sc

Perfect Reconstruction Cannot Show Whether Two Codebooks on a VAE's Latent Space Agree

arXiv:2610.14012v1 Announce Type: new Abstract: Many systems read the latent space of a variational autoencoder (VAE) as discrete codes, and rely on each latent being read back as the code it was writ

Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction

arXiv:2610.15864v1 Announce Type: cross Abstract: Large-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to re

Pristine Models

arXiv:2610.14091v1 Announce Type: new Abstract: Training on broad, unfiltered data enables high-quality image generation, but can also teach models concepts that should not be generated; restricting t

Applications

Proactive Physical AI through Continuous Observation and Timely Intervention

arXiv:2610.16967v1 Announce Type: new Abstract: Physical AI is commonly formulated around an explicitly specified task: given a goal or instruction, a system perceives, reasons, and acts. In many real

applicationsarxiv-cs-cv

Prompt Interference in LLM Post-Training: Why Pass@k Optimization Can Degrade Pass@1

arXiv:2602.21189v3 Announce Type: replace Abstract: Pass@k is a widely used performance metric for verifiable large language model tasks, including mathematical reasoning, code generation, and short-a

RCStat: A Statistical Framework for using Relative Contextualization in Transformers

arXiv:2506.19549v2 Announce Type: replace-cross Abstract: Identifying which tokens and activations truly influence model predictions is critical for both the efficiency and interpretability of large a

RDM: Recurrent Diffusion Model for Human Motion Generation

arXiv:2406.07169v3 Announce Type: replace Abstract: Human motion generation is a challenging task due to its high dimensionality and the difficulty of generating fine-grained motions. Diffusion method

Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networks

arXiv:2610.16022v1 Announce Type: new Abstract: Neuromorphic sensing and computing promise low latency and high energy efficiency for robotics, yet most event-based visual place recognition (VPR) pipe

Tutorials

SchemaPose: RGB-Based Category-Level Pose Estimation with Parametric Category Schema

arXiv:2610.18374v1 Announce Type: new Abstract: RGB-based category-level 9D pose estimation remains highly challenging because a monocular system must generalize across unseen object instances while j

tutorialsarxiv-cs-cv

SERQ: Saliency-Aware Low-Rank Error Reconstruction for LLM Quantization

arXiv:2603.08185v2 Announce Type: replace Abstract: Post-training quantization (PTQ) has emerged as a prevailing technique for deploying large language models (LLMs) efficiently in terms of both memor

Sharper Frames, Worse Rooms: Viewpoint Coverage Dominates Image Quality in Feed-Forward 3D Reconstruction

arXiv:2610.15854v1 Announce Type: new Abstract: Grounded spatial reasoning about indoor scenes rests on a geometric reconstruction that the multimodal system does not itself produce. Feed-forward mult

Simpler and Stronger Baselines for All-in-One Image Restoration

arXiv:2512.10581v2 Announce Type: replace Abstract: Current methods for all-in-one image restoration increasingly rely on complex mechanisms, such as Mixture-of-Experts or prompt learning, to handle d

Applications

Split Conformal Interval Length under Huber Contamination

arXiv:2610.15458v1 Announce Type: new Abstract: Existing analyses of interval length in split conformal prediction largely focus on clean data. However, how contamination at the training and calibrati

applicationsarxiv-cs-lg

TabGift: A Context-Efficient Foundation Model for Tabular Data Generation

arXiv:2610.16990v1 Announce Type: new Abstract: Tabular data generation aims to synthesize realistic samples from limited observations while faithfully capturing the underlying data distribution. Tabu

Through the PRISM: Preference Representation in Intermediate States of Video Diffusion Models

model-releasesarxiv-cs-cv

arXiv:2606.20310v2 Announce Type: replace Abstract: Video reward models are central to aligning video diffusion models with human preferences, yet existing ones are external vision-language models tha

Training Generalist Navigation Policies via Action Self-Distillation

arXiv:2610.17593v1 Announce Type: cross Abstract: Language provides a natural interface for humans to specify and reason about goals, but training language-conditioned robotic policies and reasoning t

VARI: Robust Text-to-Video Generation via Group-Structured Intent Alignment

arXiv:2610.18189v1 Announce Type: new Abstract: Modern text-to-video (T2V) models generate visually compelling videos, yet meaning-preserving changes in prompt wording can substantially alter semantic

Visual Tokens on Demand: Visual Budgeting for Scalable and Efficient Multi-Image Reasoning in Multimodal LLMs

arXiv:2610.18858v1 Announce Type: new Abstract: Multimodal large language models have advanced rapidly, but most remain optimized for single-image inputs. Multi-image reasoning is costly because each

Web Scale on a Budget: Tail-Aware Retrieval Evaluation

model-releasesarxiv-cs-cv

arXiv:2610.17986v1 Announce Type: new Abstract: A common practice in instance retrieval is to grow the gallery size to improve a benchmark's discrimination capability. One prominent example is ILIAS,

What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study

arXiv:2610.18869v1 Announce Type: cross Abstract: How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning? We comp

YOUDrive: Driving Style as a Composable Direction for End-to-End Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2610.14802v1 Announce Type: new Abstract: End-to-end driving policies can achieve strong task performance while offering limited control over their driving style. Driving style describes how a v

Industry

Manage private pricing offers for Amazon Bedrock with managed entitlements

Managing AI model access across dozens or hundreds of AWS accounts creates friction when each account must accept AWS Marketplace Private Pricing Offers individually. This post shows two ways to activ

industryaws-ml-blog

13 Oct 2026

One Agent Development Kit, five languages

model-releasesgoogle-cloud-ai

When we talk about agents, that doesn't necessarily mean we're talking about Python. In today's modern ecosystem, an agent might run as a high-concurrency Go microservice on Cloud Run, or live inside

13 Oct 2026

Hardware

XMEMS starts mass-producing chips that cool AI glasses, smartphones and data centers

Silicon chip startup xMEMS Labs Inc. today said it has started mass production of its XMC-2400 and XMC-4800 chips for cooling artificial intelligence hardware. No one has commercialized solid-state ac

hardwaresiliconangle

13 Oct 2026

Chronos Policy: Learning Robot Policies on Physical Time

arXiv:2610.13929v1 Announce Type: new Abstract: Robot policies often convert sensing and control into fixed-rate observation-action sequences, leaving physical time implicit in sequence position. This

safetyarxiv-cs-ro

12 Oct 2026

Deploy decision models with llama.cpp on Amazon SageMaker AI