Search: “governance”

Preconditioned Physics-Informed Neural Operator Training

arXiv:2609.36216v1 Announce Type: new Abstract: Neural operators are typically trained in a supervised fashion, which requires a dataset to be generated with a classical solver. Training them physics-

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Predictive Geometry of Hidden Trajectories in Transformers

DGX agent

arXiv:2609.37717v1 Announce Type: cross Abstract: Decoder-only transformers are trained only through a terminal next-token prediction loss, yet this loss constrains every intermediate hidden state thr

researcharxiv-cs-cl

30 Sept 2026

Pure and physics-guided deep learning approaches for spatio-temporal groundwater level prediction

arXiv:2603.25779v2 Announce Type: replace-cross Abstract: Groundwater represents a key element of the water cycle, yet it exhibits complex and context-dependent relationships that make its modeling ch

tutorialsarxiv-cs-ai

SMat-Attention: Structured Long-Context Sequence Modeling

arXiv:2609.36062v1 Announce Type: new Abstract: Long-context sequence models face a fundamental tradeoff: softmax attention uses flexible token-level interactions at quadratic cost, whereas linear att

researcharxiv-cs-ai

Stochastic Optimization Under Power-Law Spectra: Tight Bounds and Shuffling Analysis

arXiv:2609.36271v1 Announce Type: new Abstract: Recent work has established that power-law spectral conditions on data enable tight convergence bounds for deterministic gradient descent, resolving the

ThinkingGuard: Decoding Implicit Hazards via Step-by-Step Risk Attribution in Multimodal Large Language Models

arXiv:2609.36562v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) are increasingly deployed in safety-critical domains, their reliability is threatened by multimodal imp

safetyarxiv-cs-ai

What if automating AI R&D triggers an intelligence explosion?

arXiv:2609.36054v1 Announce Type: cross Abstract: In contrast to even a year ago, AI systems now write most of the code inside the companies that build them. As more of the AI research and development

safetyarxiv-cs-ai

3D Point Tracking with State Space Models

model-releasesarxiv-cs-cv

arXiv:2609.34035v1 Announce Type: new Abstract: Tracking any point of a dynamic scene in metric 3D - in absolute meters, not up to an unknown scale - underpins 3D and 4D reconstruction, robot navigati

A Journey to the Edge of Stability

arXiv:2609.32290v1 Announce Type: new Abstract: It has recently been found that deep learning often occurs at the 'edge of stability (EoS),' where the maximum Hessian eigenvalue of the model is stabil

A Unified Optimism-Agnostic Framework for Linear Bandits over Spherical Action Sets

arXiv:2609.32149v1 Announce Type: cross Abstract: Linear bandits model sequential decision-making problems with noisy rewards that are linear in the decision variable, where an agent must simultaneous

ActionGround: Training-Free Runtime Refinement of Frozen VLA Policies

arXiv:2609.33256v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models map visual observations and language instructions directly to robot actions, but they do not explicitly represent th

safetyarxiv-cs-ro

Amnesia by Design, Memory By Necessity: Persistent State for Document Intelligence

model-releasesarxiv-cs-cv

arXiv:2609.32041v1 Announce Type: new Abstract: Modern Document AI reads contracts, extracts fields, reasons over tables, and grounds answers to page regions, then forgets everything. Processing an am

An Active-Bottleneck Mechanism for Weak-to-Strong Generalization

arXiv:2609.33835v1 Announce Type: cross Abstract: Weak-to-strong generalization (W2SG) occurs when a student trained on a teacher's predictions outperforms that teacher. We study when this happens und

Automated Lesion Segmentation of Stroke MRI Using nnU-Net: A Comprehensive External Validation Across Acute and Chronic Lesions

arXiv:2601.08701v2 Announce Type: replace-cross Abstract: Accurate and generalisable segmentation of stroke lesions from magnetic resonance imaging (MRI) is essential for advancing clinical research,

Autonomous Research Project Management as an Agent Skill: A Case Study in Exact Spectral Spatial Regression

arXiv:2609.31683v1 Announce Type: new Abstract: This work presents an end-to-end demonstration of autonomous machine learning research conducted by an agent skill on consumer hardware. The demonstrati

Applications

Continuous-Time Trajectory Generation from Discrete Observations with Stochasticity

arXiv:2609.32026v1 Announce Type: cross Abstract: Physical systems evolve continuously in time, yet their states are typically observed only at discrete times. Generating trajectories consistent with

applicationsarxiv-cs-lg

Correct then Forecast: Observer State-Space Models for Time Series Forecasting

arXiv:2609.33566v1 Announce Type: new Abstract: Time series forecasting requires extrapolating the dynamics of an observed process beyond the last available measurement. Yet recurrent forecasting mode

Derivative-Informed Training of Neural Operators On-the-Fly via Sketched Tangent Consistency

arXiv:2609.32797v1 Announce Type: new Abstract: Derivative-informed training improves neural operators by directly supervising their input-output sensitivities, which is crucial when neural operators

DevelopmentODE: Structured Neural ODEs for Early Brain Development Dynamics Across a Decade

arXiv:2609.33048v1 Announce Type: new Abstract: Understanding how individual brain development unfolds over childhood requires modeling developmental trajectories from sparse longitudinal observations

tutorialsarxiv-cs-lg

Does Vision-Language Pretraining Granularity Matter? A Controlled Evaluation of Vision-Language Objectives Across Chest X-Ray Interpretation Tasks

arXiv:2609.31985v1 Announce Type: new Abstract: Vision-language pretraining objectives differ in the spatial granularity of their supervision, yet the implications of this distribution for chest X-ray

Efficient Message Passing for Partial Differential Equation Priors

arXiv:2609.32956v1 Announce Type: new Abstract: Prior information for real-world physical quantities is most elegantly expressed via partial differential equations (PDEs). In this paper, we propose a

Emergence, Not Bandwidth: Physical Coupling and the Limits of Learned Multi-Agent Communication

arXiv:2609.34373v1 Announce Type: new Abstract: Rate-limited multi-agent teams raise three questions the emergent-communication literature has answered only empirically: what an optimal message should

First Learn, Then Memorize: The Spectral Bias of Diffusion Models

arXiv:2609.35377v1 Announce Type: new Abstract: Diffusion models trained on a finite dataset first learn to generate novel, high-quality samples and only much later collapse onto their training set. W

Forecasting Bacterial Antimicrobial Resistance Trends Using Machine Learning on WHO GLASS Surveillance Data: A Retrieval-Augmented Generation Approach for Policy Decision Support

arXiv:2602.22673v3 Announce Type: replace Abstract: Background: Antimicrobial resistance (AMR) is a global health threat. While the WHO Global Antimicrobial Resistance and Use Surveillance System (GLA

From Feed-Forward to Flow: Unifying Reconstruction and Generation Is Easier Than You Think

arXiv:2609.32761v1 Announce Type: new Abstract: Reconstruct where the images provide evidence, and generate where they do not: recent success of spatial world models such as Atlas (World Labs Team, 20

Local Ai

HoTS: Homophily-Aware Temperature Scaling for Graph Neural Network Calibration

arXiv:2609.32426v1 Announce Type: new Abstract: For graph node classification, calibrated class probabilities are needed when confidence scores, usually the maximum predicted class probability, are us

local-aiarxiv-cs-lg

Identifying Temporal Features within Transcoders for Time Sensitive Factual Recall

arXiv:2609.33183v1 Announce Type: cross Abstract: Large Language Models (LLMs) suffer from temporal misalignment, often due to the contradictory nature of their training corpora. While current mitigat

Agents

Improving Causal Effect Estimation of Weighted RegressionBased Estimator using Neural Networks

arXiv:2110.15075v2 Announce Type: replace Abstract: Estimating causal effects from observational data informs us about which factors are important in an autonomous system, and enables us to take bette

agentsarxiv-cs-lg

Agents

Interactive Distributionally Robust Multi-Agent Learning with General Function Approximation

arXiv:2609.32048v1 Announce Type: new Abstract: Model misspecification poses a fundamental challenge in multi-agent reinforcement learning, where transition uncertainty can be amplified by strategic i

agentsarxiv-cs-lg

Learning Dynamics of Continual Learning: A Unified View of Data Attribution, Forgetting, and Plasticity Loss

arXiv:2609.33620v1 Announce Type: new Abstract: Modern language models are likely to be updated throughout their lifetime rather than trained once and frozen. Each update therefore participates in a r

tutorialsarxiv-cs-lg

Learning Propagation Geometry from Message-Passing Feedback

arXiv:2609.34711v1 Announce Type: new Abstract: Learning local geometry enables graph neural networks (GNNs) to adapt how they compare and integrate neighborhood information. However, estimating geome

Measurement-Error-Aware Causal Distributed-Lag Quantile Modeling of Indoor Air Pollution and Short-Term Lung-Function Deterioration

arXiv:2609.31646v1 Announce Type: cross Abstract: Low-cost indoor air-quality sensors could support personalized asthma prevention, but their nonlinear measurement error, delayed exposure effects, tim

MotionSpaceFlow: Representation-Aware Flow Matching in Direct Motion Space

arXiv:2609.34190v1 Announce Type: new Abstract: Recent advances in diffusion and flow models have substantially improved text-driven human motion generation. Yet most methods generate in low-dimension

Neural Dynamics as the Composition of Quantized Units

arXiv:2609.32487v1 Announce Type: new Abstract: Deep learning is commonly interpreted at two levels: the macroscopic, through aggregate trends in loss summarized by scaling laws, and the microscopic,

On-Policy or Off-Policy Learning? A Systematic Study of Distillation Dynamics

arXiv:2609.35259v1 Announce Type: new Abstract: On-policy learning has been argued to reduce catastrophic forgetting, produce sparser parameter updates, and improve generalisation. However, existing c

Physics-Guided Conditional Diffusion Model for Rare Event Synthesis and Diagnosis for the Water-Gas Shift Reaction

arXiv:2609.35499v1 Announce Type: new Abstract: As the world moves towards sustainable energy sources, hydrogen (H2) can be treated as an eco-friendly alternative to fossil fuels due to its high energ

PINNMorph: Evolving Online Adaptation Policies for Physics-Informed Neural Networks

arXiv:2609.32685v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) provide a learning-based framework for solving partial differential equations (PDEs), yet their training beha

Probabilistic Geodesic Flow Matching on Location-Scale Families

arXiv:2609.34613v1 Announce Type: cross Abstract: Flow matching (FM) has recently emerged as a promising framework for generative modeling due to its conceptual simplicity and strong empirical perform

Protected Cores Are Not Enough: Certifying AI-Proposed Revisions of Temporal Specifications

arXiv:2609.33461v1 Announce Type: cross Abstract: Runtime monitoring traditionally evaluates a specification that is fixed before execution or externally modified when requirements change. In learning

ReSS: Residual-Restoring Sparse Attention for 3D Vision Transformers

arXiv:2609.35593v1 Announce Type: new Abstract: 3D vision transformers such as VGGT predict camera poses and scene geometry from multi-view images in a single forward pass, but their global attention

Rethinking Training-Inference Mismatch in LLM Reinforcement Learning: Where It Arises and How to Correct It

arXiv:2609.32444v1 Announce Type: new Abstract: We study training-inference mismatch in reinforcement learning with verifiable rewards (RLVR) for large language models, where rollouts are sampled by a

Symmetric Composition of Anisotropic Operators for Global Subseasonal-to-Seasonal Climate Forecasting

arXiv:2602.15040v2 Announce Type: replace-cross Abstract: Accurate global Subseasonal-to-Seasonal (S2S) climate forecasting is critical for disaster preparedness and resource management, yet it remain

SynDORBench: Evaluating LVLM Perceptual Robustness Under Physically Constrained Visibility Conditions

model-releasesarxiv-cs-cv

arXiv:2609.31823v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have demonstrated remarkable performance on multimodal reasoning benchmarks, yet their perceptual reliability under

The Geometry of Logic: Stratification Induces Semantic Structure and Robust Reasoning

arXiv:2609.32927v1 Announce Type: new Abstract: Transformer-based language models perform well on symbolic tasks, yet it remains unclear whether they learn generalizable rules or rely on statistical s

tutorialsarxiv-cs-lg

Type-Balanced Federated Learning for Visual Analog Meter Reading

arXiv:2609.31998v1 Announce Type: new Abstract: Analog dial meters are widely deployed in industrial applications and utility sites, where environments and meter types vary and inspection data may be

Uncertainty Quantification of Next Generation Reservoir Computing with Applications to Memory-Driven Dynamical Systems

arXiv:2609.32169v1 Announce Type: cross Abstract: Nonlinear dynamical systems with memory arise across science and engineering, yet uncertainty quantification for efficient forecasting methods such as

Understanding and Mitigating Under-Confidence in GNNs from the Final Layer

arXiv:2505.11335v4 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness on graph-based tasks. However, their predictive confidence is often miscalib

What Does the Rank Buy? A Spectral and Distributional Analysis of Low-Rank Adaptation

arXiv:2609.32002v1 Announce Type: cross Abstract: The rank r in LoRA is widely treated as a capacity control: a smaller rank is assumed to yield a simpler model that generalizes better. We show that,