Search: “governance”

inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs

arXiv:2610.18051v1 Announce Type: new Abstract: This work investigates the application of the Membership Inference Test (MINT) to large language models (LLMs) fine-tuned with LoRA, aiming to determine

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

Joint Contrastive-Adversarial Representation Learning for Unsupervised Domain Adaptation

DGX agent

arXiv:2610.15613v1 Announce Type: cross Abstract: Effective unsupervised domain adaptation (UDA) requires a representation that captures transferable structure from both the labeled source domain and

safetyarxiv-cs-lg

14 Oct 2026

KedgeFlow: A Programmable Control Plane for Enterprise AI Agents

arXiv:2610.14810v1 Announce Type: cross Abstract: Enterprise AI agents can execute altered versions of approved actions, concurrently reuse the same permission, or retry operations whose responses wer

Learning to Extrapolate in Depth with Looped Transformers

arXiv:2610.14456v1 Announce Type: new Abstract: Looped transformers reuse a single block across iterations, raising a natural question: how far can a block trained at one depth be safely extrapolated

Limit Theory for Online Expectile Estimation under Short and Long Range Dependence

arXiv:2610.16030v1 Announce Type: cross Abstract: Expectiles are tail-sensitive distributional summaries with important applications in financial risk measurement and forecasting, yet statistical theo

Tutorials

Manifold Navigation Learning Using Recurrent Networks: Efficient Training Techniques

arXiv:2610.14325v1 Announce Type: new Abstract: Position tracking is a well-studied problem in Euclidean navigation, yet comparatively little attention has been given to navigation on general manifold

tutorialsarxiv-cs-cv

Nearest Neighbor Functional Curve Completion for Intraday Probabilistic Revisions of Renewable Generation Forecasts

arXiv:2610.17075v1 Announce Type: new Abstract: We propose a nonparametric approach that fuses deterministic initial forecasts and a partial realization into a probabilistic forecast using the framewo

Neuro-Symbolic Graph Transformers for Hierarchical Intention Recognition from Multimodal Human Activity Sequences

arXiv:2610.18176v1 Announce Type: cross Abstract: Proactive human-robot collaboration requires autonomous systems to infer not only immediate actions but also the underlying hierarchical intentions go

agentsarxiv-cs-lg

Applications

Nonparametric Kernel Clustering with Bandit Feedback

arXiv:2601.07535v2 Announce Type: replace-cross Abstract: Clustering with bandit feedback refers to the problem of partitioning the arms of an N-armed stochastic bandit according to their underlying d

applicationsarxiv-cs-lg

Tutorials

OPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities

arXiv:2508.12943v3 Announce Type: replace-cross Abstract: Public service systems in many African regions suffer from delayed emergency response and spatial inequity, causing avoidable suffering. This

tutorialsarxiv-cs-lg

Phase-Conditioned Affordance Fields for SE(3)-Equivariant Visuomotor Policies

arXiv:2610.17281v1 Announce Type: new Abstract: SE(3)-equivariance provides a useful geometric inductive bias for visuomotor policies, encoding that actions covary with rigid transformations of the ob

safetyarxiv-cs-ro

Physics-informed random neural architectures on compact boundaryless multiply connected manifolds for random-operator models in machine learning and uncertainty quantification

arXiv:2610.14844v1 Announce Type: cross Abstract: We introduce stochastic physics-informed neural networks (SPINNs) for stochastic boundary-value problems (SBVPs) with random operators. The method enf

PhySimCode: A Benchmark and Evaluation Method for Physics Video to Code Generation

model-releasesarxiv-cs-cv

arXiv:2610.15722v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are good at video understanding and code generation, but it remains unclear whether they can recover the physic

PVD-ONet: A Multi-scale Neural Operator Method for Singularly Perturbed Boundary Layer Problems

arXiv:2507.21437v3 Announce Type: replace Abstract: Physics-informed neural networks and physics-informed DeepONet excel at solving partial differential equations but often struggle with singularly pe

Representation Drift in Continual Learning: LoRA versus N-gram Memory

arXiv:2610.14921v1 Announce Type: new Abstract: A language model that keeps learning online must not slowly erode its own base capabilities. We ask which part of a modern LLM should absorb continual u

SemTree: Judge-Aware Semantic Decision Trees for Inspectable and Repairable Rule Decisions

arXiv:2610.13999v1 Announce Type: new Abstract: Rule decisions such as content moderation, advertising compliance, and eligibility checks combine several conditions, exemptions, and missing-informatio

Softmax-Free Attention in Spiking Transformers as the Zero-Temperature Limit of Two-Level Systems

arXiv:2610.15968v1 Announce Type: new Abstract: Spiking Transformers achieve energy efficiency by discarding softmax normalization in favor of thresholded, binary attention. However, this removal brea

Sparsity Saves Compute, Not Samples: Oracle Inequalities for Dense and Sparse Mixture of Experts

arXiv:2610.15039v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models combine specialized experts through an input-dependent routing mechanism, and their sparse variants underpin many modern

Structure-preserving fidelity diagnostics for partial differential equations

arXiv:2610.17483v1 Announce Type: new Abstract: Machine learning surrogates of partial differential equations (PDEs) are commonly trained on data generated by numerical solvers. Therefore, the trainin

StructZO: Structured Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning

arXiv:2610.16451v1 Announce Type: new Abstract: Fine-tuning large language models at scale is increasingly limited by the memory cost of backpropagation. Zeroth-Order (ZO) optimizers reduce this burde

Applications

The EU is investigating OpenAI and Anthropic after their AI agents hacked into other organisations. These early enforcement steps might lead…

applicationsgary-marcus--x

The EU is investigating OpenAI and Anthropic after their AI agents hacked into other organisations. These early enforcement steps might lead to the first legal battle between Brussels and the AI labs

VideoSlate: Stateful Commit Control for Temporally Grounded Agentic Video Reasoning

arXiv:2610.16357v1 Announce Type: new Abstract: Agentic video reasoning systems gather and assess video evidence across stages, selecting relevant spans as visual evidence for answering questions. New

agentsarxiv-cs-cv

Visualizing the World Model in an Agent's Value Function

arXiv:2610.14186v1 Announce Type: cross Abstract: Could model-free reinforcement learning algorithms actually be learning a model of the environment? In this work, we apply counterfactual intervention

When Does Opponent Modeling Pay Off? Spatial Inference Stabilises Reinforcement Learning under Partial Observability

arXiv:2610.18229v1 Announce Type: cross Abstract: Deep reinforcement learning agents in partially observable competitive games must act on beliefs about an unseen opponent's intent. We study a spatial

agentsarxiv-cs-lg

Who Improves the Robot? Toward Physical Recursive Self-Improvement

arXiv:2610.18842v1 Announce Type: cross Abstract: Robots are increasingly autonomous in task execution, yet their improvement remains largely human-driven. This study investigates Physical Recursive S

You can't solve real-world physical tasks through autoregressive sampling alone. The physical world is entirely unforgiving to hallucination…

agentsfrancois-chollet--x

You can't solve real-world physical tasks through autoregressive sampling alone. The physical world is entirely unforgiving to hallucination or approximate pattern matching. If we want actual autonomo

Industry

Sources: Anthropic and OpenAI have pushed for substantial control over what external AI evaluators can publish, out of concern that trade secrets could leak (Aditya Soni/Reuters)

Aditya Soni / Reuters: Sources: Anthropic and OpenAI have pushed for substantial control over what external AI evaluators can publish, out of concern that trade secrets could leak — Anthropic and Open

industrytechmeme

13 Oct 2026

A Baseline Assessment for the Agentic Web Technology Stack

model-releasesarxiv-cs-ai

arXiv:2610.13703v1 Announce Type: new Abstract: The fast pace in which protocols, frameworks, and technologies are being proposed for LLM-based agents results in a complex scenario for anyone aiming t

A Distributional Framework for Behavioral Characterization and Risk Management of Large Language Models

arXiv:2610.12718v1 Announce Type: cross Abstract: Large language models are stochastic by nature. Repeated executions under the same experimental conditions may produce different responses. We propose

Algebraic Integer Quantization for Machine Learning Applications

arXiv:2610.12494v1 Announce Type: new Abstract: In this article, we propose algebraic integer number formats (AINF) as a framework for highly configurable and accurate compressed computation. A key pr

EndoRSI: Physical-Evidence-Driven Recursive Improvement of Endoscopic Robot Policies

arXiv:2610.13376v1 Announce Type: new Abstract: Artificial intelligence is beginning to automate scientific discovery, yet physical robot-policy research still relies on scientists and engineers to in

safetyarxiv-cs-ro

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

KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting

arXiv:2610.12738v1 Announce Type: cross Abstract: Forecasting rare, high-cost events in multivariate time-series systems is hard because their precursors are seldom observed, so a purely data-driven m

safetyarxiv-cs-ai

RSI-Anything: Unlocking Recursive Self-Improvement with World Models

arXiv:2610.13912v1 Announce Type: new Abstract: Recursive self-improvement (RSI) aims to transform interaction experience into persistent improvements, yet external feedback is costly and world-model

safetyarxiv-cs-cl

Towards a Unified Taxonomy of Agricultural Harvesting Technologies: Linking Physical Interaction, Automation, and Selectivity

arXiv:2610.13688v1 Announce Type: new Abstract: Harvesting technologies have evolved from conventional mechanized systems based on bulk mechanical interaction toward increasingly automated and robotic

researcharxiv-cs-ro

Towards Understanding the Training of GFlowNets: A Theoretical Perspective

arXiv:2610.13711v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn policies that sample discrete objects in proportion to a positive reward. They are trained by minimizing bala

Tutorials

Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure

arXiv:2610.12544v1 Announce Type: new Abstract: Masked diffusion models (MDMs) generate discrete data by iteratively unmasking multiple tokens in parallel using a learned mask predictor. Their output

tutorialsarxiv-cs-lg

VQIL: Visible Quantum Interference Learning for Perturbation Response Ranking on Graphs

arXiv:2610.13689v1 Announce Type: new Abstract: Learning how local perturbations affect a network is important for identifying which components drive or absorb a system's response. We formulate this a

When LiDAR Fades, Radar Takes Over: LiDAR Reliability-Aware Adaptive Localization Under Airborne Obscurants

model-releasesarxiv-cs-ro

arXiv:2610.13788v1 Announce Type: new Abstract: LiDAR-inertial odometry (LIO) is widely used for real-time localization and mapping, but its reliability can degrade substantially in subterranean envir

X-AgentRed: Red-Teaming Personal Agents in Evolving Multi-Service Ecosystems

arXiv:2610.13886v1 Announce Type: new Abstract: Personal agents increasingly connect to services across the web and applications by using tools. As the effects of their actions accumulate and interact

agentsarxiv-cs-ai

Industry

PitchBook: robotics and physical AI companies have raised ~$48B YTD, as they gather training data from people completing tasks in factories, offices, and homes (Rafe Rosner-Uddin/Financial Times)

Rafe Rosner-Uddin / Financial Times: PitchBook: robotics and physical AI companies have raised ~$48B YTD, as they gather training data from people completing tasks in factories, offices, and homes — ‘

industrytechmeme

11 Oct 2026

Agentic-TTT: Training test-time policy for test-time training

model-releasesarxiv-cs-ai

arXiv:2610.12002v1 Announce Type: cross Abstract: Test-time training (TTT) adapts an LLM's parameters using signals derived from test inputs, and can make striking improvements in pre-specified settin

C-LoRA: Continual Low-Rank Adaptation for Pre-trained Visual Models

arXiv:2502.17920v2 Announce Type: replace Abstract: Pre-trained visual models have become fundamental in computer vision, but they face challenges in continual learning scenarios where data and tasks

Applications

Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness

arXiv:2610.12361v1 Announce Type: new Abstract: Large language models increasingly justify legal decisions by naming the statute or precedent behind a verdict, treated as evidence that the decision fo

applicationsarxiv-cs-ai

Cova-PINN: Cross-Domain Conservation Physics-Informed Neural Network for Fluid-Solid Conjugate Heat Transfer in Complex Geometries

arXiv:2610.11108v1 Announce Type: new Abstract: Multi-domain physics-informed neural networks (PINNs) flexibly model medium-specific representations to solve fluid--solid conjugate heat transfer (CHT)

DigitalOcean MicroVMs: Fast, isolated compute for your AI agent infrastructure

Coding-agent platforms, sandbox products, and code-execution services all need the same thing: isolated machines that start up fast, retain their state between bursts of work, and don’t consume comput

agentsdigitalocean

EvoSim: Learning to Model, Modeling to Learn

arXiv:2610.11344v1 Announce Type: new Abstract: Physics-based models connect scientific explanation with quantitative prediction. Constructing them requires selecting physical processes, defining stat

agentsarxiv-cs-ai

Intent Graph: Navigating the Analytical Reasoning Space for Exploratory Data Analysis

arXiv:2610.11025v1 Announce Type: cross Abstract: Exploratory data analysis (EDA) is rarely open-ended in practice: analysts work from high-level domain questions toward the concrete analyses that can

researcharxiv-cs-ai