Search: “applications”

Steering the Fidelity in State Preparation on Noisy Bosonic Systems with Risk-Seeking Optimization

arXiv:2610.18495v1 Announce Type: cross Abstract: In order to execute quantum algorithms on bosonic quantum computers, it is necessary to encode their inputs into a desired target quantum state, with

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Safety

Strong Gaussian Approximation of Constant-Learning-Rate SGD: Bias and Trajectory Inference

DGX agent

arXiv:2610.14567v1 Announce Type: cross Abstract: This paper develops strong Gaussian approximations (KMT type) for constant learning rate stochastic gradient descent (SGD). We work with heavy tailed

safetyarxiv-cs-lg

14 Oct 2026

Tensor-reduced atomic density representations

arXiv:2210.01705v3 Announce Type: replace-cross Abstract: Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machi

TETRIS-4DGS: Efficient Post-Training Compression of 4D Gaussian Splatting

arXiv:2610.14663v1 Announce Type: new Abstract: 4D Gaussian Splatting (4DGS) has emerged as a powerful dynamic scene representation, supporting real-time high quality rendering but suffers from prohib

The Midas Touch for Metric Depth: Interpretable Metricization for Generalizable Depth Perception

arXiv:2610.16749v1 Announce Type: new Abstract: Recent foundation models have drastically improved the generalization of monocular relative depth estimation, yet recovering accurate metric depth from

Totem: Object-Centric 3D Memory for World-Consistent Video Generation

arXiv:2610.15807v1 Announce Type: new Abstract: Generating coherent videos grounded in a unified underlying world remains a major challenge, particularly under large viewpoint changes, where existing

Toward Zero-Shot Point Cloud Registration Across Diverse Scales, Scenes, and Sensor Setups

model-releasesarxiv-cs-cv

arXiv:2601.02759v2 Announce Type: replace Abstract: Some deep learning-based point cloud registration methods struggle with zero-shot generalization, often requiring target-domain retraining, fine-tun

Tutorials

Towards Modality-Agnostic Seizure Classification: A Unified Deep Learning Framework for Heterogeneous EEG and Intracranial EEG Signals

arXiv:2610.16596v1 Announce Type: new Abstract: Seizure prediction and detection, essential for clinical treatment of epilepsy, remain the crucial task of this neurological disease. EEG and iEEG are w

tutorialsarxiv-cs-lg

Towards Safe Autonomy for Computer Use

arXiv:2610.18508v1 Announce Type: cross Abstract: The safety of LLM-based agents currently rests on alignment training and safety classifiers, neither of which can rule out that a sophisticated indire

safetyarxiv-cs-lg

TrajCFG: Token-Wise Classifier-Free Guidance via Denoising Trajectory Feedback

arXiv:2610.14153v1 Announce Type: new Abstract: Classifier-free guidance (CFG) typically applies a shared scale to all latent tokens, although their velocity predictions evolve differently during samp

TropNNC: Structured Neural Network Compression Using Tropical Geometry

arXiv:2409.03945v4 Announce Type: replace Abstract: We present TropNNC, a framework for compressing neural networks with linear and convolutional layers and ReLU-type activations using tropical geomet

Uncertainty Evaluation: Bridging Machine Learning and Measurement Science

arXiv:2610.18066v1 Announce Type: new Abstract: Reliable uncertainty evaluation is essential for assessing the trustworthiness of machine learning (ML) predictions, particularly in safety-critical app

safetyarxiv-cs-lg

UniDeblur: Unified Motion Deblurring via Fourier Convolution Learning

model-releasesarxiv-cs-cv

arXiv:2610.12941v2 Announce Type: replace Abstract: Image deblurring is a fundamental task in computer vision, yet existing methods mainly specialize in isolated scenarios. Consequently, the deblurrin

Tutorials

Universal Incremental Learning: Mitigating Confusion from Inter- and Intra-task Distribution Randomness

arXiv:2503.07035v3 Announce Type: replace Abstract: Existing incremental learning (IL) methods rely on strong assumptions: incoming tasks either introduce exclusively new classes or new domains (i.e.,

tutorialsarxiv-cs-cv

VisualCoding: Learning Executable Skills for Reliable Visual Reasoning

model-releasesarxiv-cs-cv

arXiv:2610.14403v1 Announce Type: new Abstract: Learning reusable skills can improve visual reasoning agents, yet applying a skill still requires the agent to implement or invoke a procedure and produ

zPocket: Better Pocket Finding with Frozen Co-Folding Features

arXiv:2610.17696v1 Announce Type: new Abstract: Identifying protein-ligand binding sites (pockets) is important for understanding protein function and developing drugs. However, no public method has l

Hardware

From Earth to Orbit, France’s Ecosystem Delivers Agentic Computing at Scale With NVIDIA

Companies in France are deploying agentic computing across industries spanning space, financial services, the public sector and scientific research, demonstrating how the country’s ecosystem has moved

hardwarenvidia-blog

13 Oct 2026

Gemini 4 Argon available on Google Cloud, built to help solve your business’ hardest problems

model-releasesgoogle-cloud-ai

Last week we announced that Gemini Enterprise has evolved to make it even simpler to get work done, and introduced the Gemini agent, your new universal agent for work. It's always on and ready to answ

13 Oct 2026

Gemini 4 Argon is available: — For developers: Build with the Gemini API paid tier and if you’re a Google AI Ultra subscriber, you can try i…

model-releasesgoogle-ai--x

Gemini 4 Argon is available: — For developers: Build with the Gemini API paid tier and if you’re a Google AI Ultra subscriber, you can try it in @antigravity, @GoogleAIStudio, and @Android Studio. — F

13 Oct 2026

10 Years of the SRE book: Announcing the second edition of 'Site Reliability Engineering”

model-releasesgoogle-cloud-ai

A decade ago, Google released Site Reliability Engineering: How Google Runs Production Systems with O'Reilly Media. What began as a collection of our practices for managing large-scale infrastructure

A Local Agentic AI Framework to Generate Edge Case Scenarios for Safety Assurance

arXiv:2610.13479v1 Announce Type: new Abstract: Generating edge case scenarios is crucial for training, testing and safety assuring autonomous systems. This identifies hazardous failures to increase s

local-aiarxiv-cs-ai

A Scalable Learned Iterative Solver for Nonlinear MPC via Temporal Decomposition

arXiv:2610.13480v1 Announce Type: cross Abstract: Nonlinear model predictive control (NMPC) can be applied to large nonlinear systems with complex objectives while enforcing constraints, but the cost

AgentTune: A Unified Training Lifecycle for Tool-Using LLM Agents

arXiv:2610.13335v1 Announce Type: new Abstract: Tool-using agents increasingly require a common substrate for building, training, evaluating, distilling, and maintaining agentic systems, yet existing

Amortised Inference through One-Step Implicit Sampling

arXiv:2610.12679v1 Announce Type: new Abstract: Efficient sampling from Bayesian posterior distributions in high dimensions remains a hard problem, despite advances in theory and algorithms. Multiple

APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces

model-releasesarxiv-cs-cl

arXiv:2610.13623v1 Announce Type: cross Abstract: Professional agents increasingly work through command-line interfaces and multiple tools over shared workspaces containing files, messages, and busine

Beyond Second-Order Information: Paid Logistic Regression with Noisy Features

arXiv:2610.12919v1 Announce Type: cross Abstract: We consider online binary prediction with paid stochastic features, where the learner can spend resources to obtain better observations of the underly

BLOKS: Scaling Kernel Solvers with Sketched Block Preconditioning

arXiv:2610.13382v1 Announce Type: new Abstract: Kernel Ridge Regression (KRR) is a theoretically sound algorithm for nonparametric learning, which is also often used to investigate properties of deep

Coordinate-Free Automatic Differentiation

arXiv:2610.12776v1 Announce Type: new Abstract: Automatic differentiation (AD) is a cornerstone of modern machine learning. Since all AD frameworks implicitly fix a basis of Euclidean space to define

Credit Attribution in Timestamped Soccer Networks Using Multivariate Hawkes Processes

arXiv:2610.12775v1 Announce Type: cross Abstract: The two main traditional player statistics used in soccer are goals scored and assists, with an assist credited when a player's pass immediately leads

Critical Mass: Structure-Disjoint Splits Drift With the Libraries They Are Built On

arXiv:2610.12694v1 Announce Type: new Abstract: Molecular machine learning evaluates out-of-distribution performance with structure disjoint splits, a construction rule fixed once at the scale its lib

DeFiRank: Federated Pairwise Ranking for Governance in Decentralized Financial Networks

arXiv:2610.12916v1 Announce Type: new Abstract: Existing governance approaches in decentralized financial networks rely on manual review processes conducted by human committees, which introduces subje

safetyarxiv-cs-ai

DeltaLLM: Delta-Key Sparse Attention for Edge LLMs

arXiv:2507.19608v2 Announce Type: replace Abstract: On-device LLM inference is increasingly important for edge platforms but remains challenging due to high computational and memory demands and tight

local-aiarxiv-cs-ai

Deploying RDMA for Large-Scale Inference and Training Workloads

arXiv:2610.12815v1 Announce Type: new Abstract: RDMA can reduce communication overhead in distributed machine learning systems, but realizing these benefits in production requires more than replacing

DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models

arXiv:2610.12572v1 Announce Type: new Abstract: Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in effi

safetyarxiv-cs-lg

Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models

arXiv:2502.08598v3 Announce Type: replace Abstract: Diffusion models have been widely adopted as a powerful generative framework across numerous scientific domains, yet their inherently iterative samp

Distributed Harmonic-Covariance Feedback for Hole Closure in Planar Robot Swarms

arXiv:2610.12637v1 Announce Type: new Abstract: We consider hole closure in a planar multi-agent system whose sensing disks form a time-varying Cech complex. We propose a harmonic-covariance feedback

local-aiarxiv-cs-ro

DRYLAB-Bench: Benchmarking the Biosecurity Risk of Agentic LLM Mutation Design

model-releasesarxiv-cs-ai

arXiv:2610.13116v1 Announce Type: new Abstract: As large language models (LLMs) acquire advanced biological design capabilities, they offer substantial research benefits but simultaneously introduce s

Evo^2: Evolving the Evolver through Weight-Harness Co-Evolution

model-releasesarxiv-cs-cl

arXiv:2610.13025v1 Announce Type: new Abstract: Self-evolving agents are increasingly used to improve language model systems without human intervention through either weight updates or harness improve

Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy

arXiv:2610.12680v1 Announce Type: new Abstract: Background: Discontinuity in neonatal EEG is a critical marker of brain dysfunction but can be difficult to quantify visually. Inter-burst intervals (IB

From Imbalance to Evidence: Deep Nonparametric Testing for Complex Data under Severe Sample Imbalance

arXiv:2610.12946v1 Announce Type: cross Abstract: Comparing distributions under severe sample imbalance requires effectively using abundant majority observations while retaining sensitivity to differe

From Pixels to Privacy-Sensitive Features: An Information-Theoretic Framework for Reconstruction Bounds

arXiv:2610.13768v1 Announce Type: cross Abstract: What can an adversary recover from a model trained with DP-SGD? Its (arepsilon,elta) guarantee does not answer this question directly. We derive a dis

Hide and Seek: HIDE Concepts to SEEK explanations for black box models

arXiv:2610.13935v1 Announce Type: cross Abstract: Interpreting machine learning model predictions is a central challenge, particularly for black-box vision models. Concept-based attribution methods in

local-aiarxiv-cs-ai

Industry

How Flo Health built safer, more useful open models with Databricks AI Runtime

Flo Health leveraged Databricks AI Runtime to create open‑source language models that prioritize both safety and practical utility. By integrating Databricks’ scalable training infrastructure with rob

industrydatabricks

Agents

Humanize MLE Agents: Multi-Agent Session Alternation Beyond Single-Agent Limits

arXiv:2610.13494v1 Announce Type: new Abstract: Existing LLM-based agent approaches for long-horizon machine learning engineering (MLE) often use context engineering, implemented through custom harnes

agentsarxiv-cs-ai

Kernelized Parameter Sensitivity Atlases for Amortized Conditional Interpretability

arXiv:2610.12737v1 Announce Type: new Abstract: Gradient-based attribution provides input-specific information about model parameters, but computing it via backpropagation for many samples is expensiv

Agents

Know What You Don't Know: Agentic Data Synthesis by Evolving Generation Space for Closed-Loop Learning

arXiv:2610.12593v1 Announce Type: new Abstract: The continued improvement of large language models increasingly depends on synthetic experience, yet effective data synthesis for interactive agents req

agentsarxiv-cs-ai

LLC: lightweight Open WebUI alt now with Agents+MCP+RAG+Image Editing

Quick context: LocalLightChat (LLC) is a chat frontend for local LLMs. Download it, run it, that's it. No installation (unless you want to), no dependencies, and it runs on pretty much anything, inclu

local-air-localllama

Model-agnostic Knowledge Augmented Machine Learning for Weakly Annotated Bioimaging Data

arXiv:2610.13637v1 Announce Type: cross Abstract: Machine learning models are increasingly used for automated feature extraction from weakly annotated bioimaging data, such as microscopy and cytomorph

researcharxiv-cs-ai