Search: “governance”

Beyond Memory: A Transactional Continuity Kernel for Long-Lived AI Agents

arXiv:2608.11632v1 Announce Type: cross Abstract: Persistent AI agents accumulate versioned state across long horizons, but storage retention alone does not identify authoritative state. Without an ex

agentsarxiv-cs-ai

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Causal Structure is Inducible but Functionally Decoupled: The Routing/Readout Boundary of a Typed Mechanism Library

DGX agent

arXiv:2608.11767v1 Announce Type: new Abstract: When a language model answers an interventional question, the computation it must perform depends on the type of evidence the query requires. We report

model-releasesarxiv-cs-cl

13 Aug 2026

Conflict and Congruency Effects in Large Language Models: In-Weight and In-Context Competition in a Verbal Conflict Task

arXiv:2608.11510v1 Announce Type: cross Abstract: Congruency effects, observed in conflict tasks such as Stroop and flanker tasks, have been investigated for nearly a century in psychology and neurosc

Dual-Domain Cross-Modal Decoding for Clinical Text-Guided Medical Image Segmentation

arXiv:2608.11335v1 Announce Type: cross Abstract: Clinical text can narrow down what to segment, but recent text-guided designs emphasize spatial alignment while overlooking frequency content that gov

Ethics Practices in AI Development: An Empirical Study Across Roles and Regions

arXiv:2508.09219v3 Announce Type: replace-cross Abstract: Recent advances in AI applications have raised growing concerns about the need for ethical guidelines and regulations to mitigate the risks po

Kernel Methods for Learning Operators with Multiple Inputs and Outputs

arXiv:2608.11831v1 Announce Type: new Abstract: Learning mappings between infinite-dimensional objects is a central challenge in scientific machine learning. We introduce a general kernel-based encode

Large Language Models Reproduce Racial Stereotypes When Used for Text Annotation

arXiv:2603.13891v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation

Learning with Bilevel-Minimax Optimization for Efficient and Reliable Transfer Attacks

arXiv:2608.11815v1 Announce Type: cross Abstract: Transfer-based adversarial attacks craft adversarial examples using surrogate models to mislead black-box victim models. Beyond perturbation generatio

researcharxiv-cs-cv

Tutorials

Low-Interaction-Rank Learning: Unifying Multiplicative Dual-Encoder Heads

arXiv:2608.11661v1 Announce Type: cross Abstract: A multiplicative dual-encoder network computes a real-valued output for a pair of inputs as the inner product of their separate encodings. This archit

tutorialsarxiv-cs-ai

Massive Activations in Hybrid Linear Attention Large Language Models: Pre-Attention Spikes and Inter-Spike Plateaus

arXiv:2608.12149v1 Announce Type: new Abstract: We present the first systematic study of Massive activations (MAs) in layer-interleaved HLA LLMs and uncover two architecture-aligned morphologies: MAs

researcharxiv-cs-cl

Post-Training with Policy Gradients: Optimality and the Base Model Barrier

arXiv:2603.06957v2 Announce Type: replace-cross Abstract: We study post-training linear autoregressive models with outcome and process rewards. Given a context oldsymbol{x}, the model must predict the

A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction

arXiv:2606.14498v2 Announce Type: replace-cross Abstract: Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access to molecular orbita

CodeRabbit bags $143M to help companies get a grip on the explosion of AI-generated code

CodeRabbit Inc., the creator of a popular tool that automatically reviews artificial intelligence-generated code, is becoming more ambitious after closing on its latest 143 million Series C round of f

agentssiliconangle

ComBodied Agents: a New Paradigm of Human-Centric Agentic AI

arXiv:2608.10915v1 Announce Type: new Abstract: After an older adult misses a medication dose, a software agent can send another reminder and an embodied agent can bring the medication. Yet neither ex

Compositional Benchmark Synthesis for Hierarchical Human Action Recognition

arXiv:2608.10765v1 Announce Type: new Abstract: Recognizing human behavior across levels of abstraction, from atomic actions to long-horizon intentions, requires data annotated along a semantic hierar

Detecting an Effect Is Not Learning to Act on It: A Reward-SNR Floor for LLM Acquisition Agents

arXiv:2608.10441v1 Announce Type: cross Abstract: Many pipelines can pay a per-example cost to acquire an auxiliary, model-derived observation -- an LLM's structured reasoning, a slow oracle, an expen

safetyarxiv-cs-cl

Efficient Weak-Entropy PINN for Solving Hyperbolic Conservation Laws

arXiv:2608.10389v1 Announce Type: cross Abstract: In recent years, neural networks have significantly advanced numerical solutions of partial differential equations (PDEs). However, solving PDEs with

ELVAE: Evidential Learning-Based Variational Autoencoder for Uncertainty-Aware Generation

arXiv:2608.10398v1 Announce Type: cross Abstract: Variational autoencoders generate samples from probabilistic latent representations but do not distinguish uncertainty about the latent location from

Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery

arXiv:2608.10801v1 Announce Type: new Abstract: Spatio-temporal PV data are essential for understanding adoption processes in off-grid regions, yet such data remain largely unavailable. Automated segm

researcharxiv-cs-cv

Generalized Linear Markov Decision Process

arXiv:2506.00818v2 Announce Type: replace-cross Abstract: Offline reinforcement learning for longitudinal studies often faces two linked challenges: rewards may be binary or bounded, and reward observ

Generator-Guided Inverse Sampling for Levy-Driven Generative Models

arXiv:2608.10384v1 Announce Type: new Abstract: This paper studies inverse sampling for Levy-driven generative models from the perspective of Markov generators. Unlike conventional diffusion models, L

Hierarchical Compositionality for An Assistive AI Agent

arXiv:2608.10330v1 Announce Type: new Abstract: AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are

agentsarxiv-cs-ai

Position Encoding in Transformers: From Absolute and Relative Methods to Rotary Position Embeddings and Long-Context Scaling

arXiv:2608.10021v1 Announce Type: new Abstract: Self-attention models content-dependent interactions between tokens but does not by itself encode token order. Position encoding addresses this limitati

safetyarxiv-cs-cl

The Epistemic Politics of AI Anthropomorphism

arXiv:2608.00961v2 Announce Type: replace-cross Abstract: AI anthropomorphism is typically treated as a problem of user misperception requiring institutional correction. Users who engage in sustained

Local Ai

The Kuramoto Neural Operator: Learning to Solve PDEs via Coupled Oscillator Dynamics

arXiv:2608.10234v1 Announce Type: cross Abstract: Operator learning is a rapidly advancing area of computational science. It is particularly well suited to problems where a partial differential equati

local-aiarxiv-cs-lg

The Signal Rail: A Deterministic Motion Grammar for Communicating Conversational Agent State in Terminal Interfaces

arXiv:2608.10689v1 Announce Type: cross Abstract: Terminal interfaces to conversational agents report rich internal state (listening, thinking, executing tools, awaiting input, failing) almost entirel

agentsarxiv-cs-cl

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration

arXiv:2608.08689v1 Announce Type: new Abstract: The state evolution of a complex system arises jointly from object laws, relational propagation, domain conservation, and unmodeled error. Forcing all s

Accelerate PostgreSQL migrations using Gemini in Database Migration Service

model-releasesgoogle-cloud-ai

Imagine this scenario: Your team decides to migrate a core application from an existing commercial database like Oracle or SQL Server to open source PostgreSQL or a fully managed service such as Alloy

ADEx-FNO: A Unified Ambient-Domain Framework for Fourier Neural Operators on Varying Geometries

arXiv:2608.08608v1 Announce Type: cross Abstract: Fourier neural operators (FNOs) provide efficient nonlocal spectral learning, but varying geometries and independently chosen discretizations remain d

An Explainable GNN Framework for Component-Level Anomaly Diagnosis

arXiv:2608.09246v1 Announce Type: new Abstract: Industrial processes are complex systems composed of multiple interacting sensors that generate multivariate time series (MTS). Detecting anomalies in s

Claude's watermark probably doesn't work how you think. As the CTO of GPTZero, I'll explain how Anthropic, Google and OpenAI are building te…

model-releasesemad-mostaque--x

Claude's watermark probably doesn't work how you think. As the CTO of GPTZero, I'll explain how Anthropic, Google and OpenAI are building text watermarking in this brief explainer and whether it can b

Clustered Attractor Manifolds and Dynamical Condensation in Self-Attention

arXiv:2608.08922v1 Announce Type: cross Abstract: Transformer layers generate state-dependent interaction networks: token representations determine the attention matrix, which in turn updates the repr

Commitment Before Realization: When Classifier-Free Guidance Becomes Unnecessary in Masked Diffusion Language Models

arXiv:2608.08082v1 Announce Type: new Abstract: Classifier-free guidance (CFG) is usually kept on throughout masked diffusion language model decoding, although its benefit varies across prompts and ov

researcharxiv-cs-cl

CONFER: Conflict-Aware Evidence Negotiation for Regime-Calibrated Weak Supervision in Multimodal Emotion Recognition

arXiv:2608.07867v1 Announce Type: new Abstract: Multimodal emotion recognition often treats self-reported labels as reliable supervision while overlooking self-report unreliability and cross-modal con

Constrained Learning with Universally Learnable Concept Classes

arXiv:2608.08414v1 Announce Type: new Abstract: We study constrained statistical learning over infinite-dimensional hypothesis classes in the fully nonconvex setting, and establish universal PACC lear

Detection of Self-Introductions in Legislative Testimony

arXiv:2608.07891v1 Announce Type: new Abstract: Self-introductions are common in legislative committee testimonies. Successfully detecting them and extracting the speaker's name is enormously helpful

researcharxiv-cs-cl

Tutorials

Did the Grid Erase the Event? EndoClock for Auditing Medical World-Model Pipelines

arXiv:2608.09266v1 Announce Type: new Abstract: Medical world models commonly learn from multimodal recordings synchronized onto a fixed-rate grid. This preprocessing resamples each native stream onto

tutorialsarxiv-cs-cv

Distributed Optimization with Streaming Data: A Temporal Weighting Perspective

arXiv:2608.09565v1 Announce Type: cross Abstract: Optimization theory is a widely used tool for intelligent decision-making. While classical optimization deals with fixed, time-invariant objective fun

DualCert: A Solver for the Traveling Salesman Problem with Constraint-Coupled Learning

arXiv:2608.09042v1 Announce Type: new Abstract: Large traveling salesman problem (TSP) instances require a solver to allocate limited computation while preserving the validity of its outputs. Existing

Dynamic gain neuromodulation attenuates the stability gap under joint training

arXiv:2507.14056v3 Announce Type: replace-cross Abstract: Recent work in continual learning has highlighted the stability gap -- a temporary performance drop on previously learned tasks when new ones

Estimation of Spacecraft Inertia Tensor Using Attitude-Only Data from Torque-Free Motion

arXiv:2608.08886v1 Announce Type: new Abstract: We present an attitude-only framework for estimating a spacecraft's normalized inertia tensor from torque-free rotational motion. Our method supports bo

researcharxiv-cs-ro

Tutorials

From Objectives to What Models Learn: A Landau Theory of Invariant Learning

arXiv:2608.09396v1 Announce Type: new Abstract: Invariant learning seeks representations that remain predictive across environments, yet the behavior of its objectives along the regularization path is

tutorialsarxiv-cs-lg

From Values to Benchmarks: Evaluating Large Language Models for Governmental Use in Dutch

arXiv:2608.09925v1 Announce Type: cross Abstract: Large language models are increasingly being deployed in governmental settings, yet few existing evaluation frameworks jointly reflect the values of p

Geometry Beats Estimated Depth: RGB-Only Multi-Camera 3D Tracking under Sim2Real

arXiv:2608.07579v1 Announce Type: cross Abstract: The AI City Challenge 2026 Track 1 evaluates multi-camera 3D perception in large indoor warehouses under a synthetic-to-real (Sim2Real) setting; depth

Gradient Under Microscope: Benchmarking Resource Utilization of Memory-Efficient Gradient Computation Methods

model-releasesarxiv-cs-lg

arXiv:2608.08961v1 Announce Type: new Abstract: AI training's rising resource intensity is straining electricity supplies and carbon budgets, motivating systematic study of memory-efficient training o

LexKairos: Benchmarking Legal Temporal Capabilities in LLMs

model-releasesarxiv-cs-cl

arXiv:2608.09106v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong performance across a wide range of legal tasks. In legal practice, time is a critical concept that

Matching Supervision to the Student's Learning Capacity: A Unified Framework for On-Policy Self-Distillation

arXiv:2608.08176v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) improves the reasoning abilities of LLMs by internalizing privileged context into model parameters through self-disti

Applications

Metadata Reconstruction from Values Alone: Recovering Column Semantics in Undocumented Warehouses

arXiv:2608.07946v1 Announce Type: cross Abstract: Text-to-SQL benchmarks ship schemas whose column names already say what the columns mean. Production warehouses are the inverse: cryptic identifiers,

applicationsarxiv-cs-ai