Search: “governance”

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

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

DGX agent

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

safetyarxiv-cs-lg

29 Sept 2026

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

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

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

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

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,

What Should a Streaming Video Model Remember?

arXiv:2606.16353v2 Announce Type: replace Abstract: Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under

tutorialsarxiv-cs-cv

When Does Domain Adaptation Help on Physical Vibration Sensors? A Held-Out-Bearing Study of Neural-Operator and Convolutional Models

arXiv:2609.31639v1 Announce Type: new Abstract: Diagnosing rolling-element bearing faults from vibration is a canonical physical-sensing task and a widely used benchmark for domain adaptation under op

When Known Physics Helps Neural PDE Models: Residual Constraints Out-Regularize Generic Priors for Nonlinear Dynamics

arXiv:2609.34012v1 Announce Type: new Abstract: Neural PDE surrogates increasingly incorporate structural priors, yet it is often unclear whether their gains arise from physics-specific information or

1Password ties AI agent access to individual tasks

AI agent access is complicating identity controls as agents log in, carry credentials and act on someone’s behalf. They have characteristics of both human and machine users. That overlap can make acti

agentssiliconangle

Adaptive Random Matrices in Gaussian Bandits: Spectral Universality and Selection-Induced Outliers

arXiv:2609.30321v1 Announce Type: cross Abstract: Adaptive arm selection changes the distribution of the observations collected by a bandit algorithm, but it need not change their limiting empirical s

Agentic AI security pushes vendors toward shared safeguards

Agentic AI security is becoming a cross-industry challenge as autonomous systems move among products and platforms. Okta Inc. argues that building trust will require coordinated safeguards rather than

agentssiliconangle

Agentic Limit Order Books: Phase Transitions and Market Impact

arXiv:2609.31260v1 Announce Type: cross Abstract: We investigate the systemic macroscopic dynamics emerging from Limit Order Books (LOBs) populated exclusively by autonomous reinforcement-learning age

agentsarxiv-cs-ai

Local Ai

AtomWorld-Mem: Memory-Restored World States for Long-Horizon Atomistic Evolution

arXiv:2609.31133v1 Announce Type: new Abstract: High-fidelity atomistic evolution over long timescales requires more than observing the current crystal configuration. Instantaneous atomistic snapshots

local-aiarxiv-cs-ai

Cosine Similarity Is Not Evidence: Measuring the Noise Floor of Interpretability Transfer Under Quantization

arXiv:2609.30275v1 Announce Type: new Abstract: A statistic reported without the quantity needed to interpret it is not evidence. We develop that thesis for a concrete practice in AI safety. Interpret

Diagnosing the Sources of Compositional Failure in Vision-Language Models: A Controlled Analysis

arXiv:2609.31456v1 Announce Type: new Abstract: Vision-language models (VLMs) often struggle with compositional reasoning tasks, but the reasons for this underperformance remain unclear. A common hypo

Energy-efficient operation of neural operators for virtual sensing

arXiv:2609.30580v1 Announce Type: new Abstract: Virtual sensing repeatedly reconstructs physical fields from changing observations, often on a fixed geometry. We investigate how shared spatial computa

Evolutionary Safety of Recursive Self-Improving AI: Taxonomy, Risk Discovery, and Evaluation

arXiv:2609.31186v1 Announce Type: new Abstract: Artificial intelligence is advancing rapidly, with increasingly capable systems taking larger roles in reasoning, decision-making, scientific discovery,

safetyarxiv-cs-ai

FARE: Forensic Acceptance Region Estimation for Catching Bait-and-Switch Image Generators

arXiv:2609.30982v1 Announce Type: cross Abstract: Modern AI image generators are increasingly deployed as opaque APIs, where customers can query the deployed service, but cannot inspect model weights

safetyarxiv-cs-ai

I Act Therefore I Am: When Is JEPA's Action-Conditioning Enough to Learn Causal Mechanisms?

arXiv:2609.31161v1 Announce Type: new Abstract: Recent empirical and theoretical advances suggest that joint-embedding predictive architectures (JEPAs) may learn meaningful representations for action-

Light Field Primitive for Novel View Synthesis

arXiv:2609.31198v1 Announce Type: new Abstract: We present Light Field Primitives (LFP), a formulation for novel view synthesis that replaces the dense ray database with a compact set of differentiabl

Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events

arXiv:2609.30746v1 Announce Type: new Abstract: Extreme events in chaotic systems are difficult to learn from short trajectories because they are controlled by transient finite-time instability rather

Online Learning via Learned Latent Bayesian Tracking

arXiv:2609.31559v1 Announce Type: new Abstract: Online learning in non-stationary environments requires models to adapt rapidly from streaming data under strict computational constraints. A principled

PIA: A Personal Intelligence Agent Turning Health Conversations into Records and Records into Understanding

arXiv:2609.31255v1 Announce Type: new Abstract: General-purpose agent memory summarizes conversations: it extracts salient snippets, embeds them, and retrieves the top-k into the prompt. A health agen

agentsarxiv-cs-cl

Reliability-Regulated Trajectory Optimization for Progressive COLMAP-Free 3D Gaussian Splatting

arXiv:2609.30865v1 Announce Type: new Abstract: COLMAP-free 3D Gaussian Splatting (3DGS) bypasses computationally expensive structure-from-motion (SfM) pipelines, yet progressive camera pose tracking

Retrainable physics-integrated neural differentiable modeling of sintering across material systems

arXiv:2609.31518v1 Announce Type: cross Abstract: Sintering is widely used to manufacture ceramics, but coupled densification and grain growth, material-dependent kinetics, and sparse measurements com

Symbiotic Architecture for Post-Hoc Audio Extension of Frozen Language Models

arXiv:2609.30784v1 Announce Type: cross Abstract: This paper proposes an architecture for equipping large language models (LLMs) with audio-understanding capabilities without fine-tuning their weights

researcharxiv-cs-cl