Search: “persistence”

A Deterministic and Auditable AI Security Risk Assessment Framework with ATLAS Aligned Executable Rules and Formal Verification

arXiv:2610.01436v1 Announce Type: new Abstract: Artificial intelligence systems are increasingly deployed in high impact and safety critical settings, yet security assessment remains difficult to repr

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Research

Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization

DGX agent

arXiv:2610.01375v1 Announce Type: new Abstract: A user's movie, news, and dialogue histories differ in their native actions and outputs, yet each interaction supplies evidence that can update user mem

researcharxiv-cs-lg

2 Oct 2026

Actions with Receipts: Jointly Binding Claims, Evidence, and Execution for Replayable Tool-Agent Auditing

arXiv:2610.00327v1 Announce Type: cross Abstract: Tool-using agents can expose citations and execution logs while leaving a critical association unaudited: whether the claim shown to a user is the cla

AI Emulation of Stochastic Sudden Stratospheric Warming with Interpretable Latent Structure

arXiv:2610.02069v1 Announce Type: cross Abstract: Rare weather regime transitions pose a challenge for data-driven modeling due to class imbalance. In this study, we develop a probabilistic deep learn

An ontology for cross-sectoral crisis management: core and public health modules

model-releasesarxiv-cs-ai

arXiv:2610.01326v1 Announce Type: new Abstract: This paper presents the European Crisis Management Ontology (ECMO), a modular OWL-based ontology intended as a cross-sectoral reference for disaster ris

Architectural Sampling: Test-Time Scaling via Computational Diversity in Frozen Vision-Language Models

model-releasesarxiv-cs-ai

arXiv:2610.01687v1 Announce Type: cross Abstract: Test-time scaling often seeks better answers by sampling multiple responses from a frozen model, yet conventional temperature sampling generates every

Are We Recovering Mechanisms? Objective-Level Recovery Gaps in Mechanistic Interpretability

arXiv:2610.02098v1 Announce Type: new Abstract: Mechanistic interpretability aims to recover the internal computations responsible for model behavior. Progress in automated circuit discovery is often

Asynchronous LLM Post-Training: Group-Mass Capping and Convergence Analysis

arXiv:2610.01896v1 Announce Type: cross Abstract: Asynchronous reinforcement learning (RL) improves the efficiency of large language model post-training but introduces stale rollouts generated by earl

Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering

arXiv:2610.02014v1 Announce Type: new Abstract: The rapid maturation of artificial intelligence (AI) and machine learning (ML) has catalyzed a profound shift in how chemical engineering problems are f

Before It Fades: Reinforcing Temporal Representations at Inference Time in VideoLLMs

arXiv:2610.01595v1 Announce Type: cross Abstract: Video Large Language Models (VideoLLMs) receive frames in sequential order and interpret how visual content evolves along the temporal axis, yet tempo

BG4Sea: Biogeochemical Seasonal Forecastability via Progressive Information Scaling

arXiv:2607.16731v2 Announce Type: replace Abstract: Marine biogeochemical forecasting is increasingly important for managing marine ecosystems and the carbon cycle, yet global, seasonal forecast produ

Causal Memory Policy: Making Memory Utility Identifiable by Intervening on Retrieval

arXiv:2610.02070v1 Announce Type: new Abstract: Memory-augmented large language models must decide which memories to retain, and recent systems do so by estimating each memory's effect on task perform

CoEvolve: Construct-to-Edit Visual Grounding with Bidirectional State Refinement

arXiv:2610.01710v1 Announce Type: new Abstract: Visual grounding localizes an object described by language with a bounding box. Most multimodal grounding models compress target identification, spatial

Contrastive Attention Mitigates Spectral Bias in Spiking Transformers

arXiv:2610.01403v1 Announce Type: new Abstract: Spiking Transformers merge the energy-efficiency of spiking neural networks (SNNs) with the representational power of self-attention, creating a promisi

Designing for Interpretation Uncertainty: Architecture and Principles for Topological Learning Analytics Dashboards

arXiv:2610.01749v1 Announce Type: cross Abstract: Topological Data Analysis (TDA) offers novel methods for understanding temporal dynamics in complex systems, yet its application in information system

DiVid: Diagnosing Dimension-Specific Diversity Collapse in Video Generation Models

arXiv:2610.01661v1 Announce Type: new Abstract: Despite remarkable progress, video generation models often produce highly similar outputs when repeatedly sampled from the same prompt, limiting their u

Domain generalization and synthetic data in object detection: the enabler, the probe, and the gap

arXiv:2610.00030v1 Announce Type: new Abstract: Object detection models often experience performance degradation when deployed under distribution shifts, caused by for example changes in weather type,

safetyarxiv-cs-cv

DramaAgent: Agentic Storytelling Video Generation

arXiv:2610.00097v1 Announce Type: new Abstract: Recent diffusion and autoregressive models have substantially improved text-to-video generation, yet producing coherent long-form story videos with cons

agentsarxiv-cs-cv

ECoMEM: Explicit Concept Memory for Memory-Dependent Robot Control

arXiv:2610.00801v1 Announce Type: new Abstract: A robot may lose sight of an object it must later retrieve, need to recall what a person demonstrated earlier, or track which steps of a task it has alr

safetyarxiv-cs-ro

Ego2Act: Evaluating Goal-Directed Manipulation in Egocentric Video Generation

arXiv:2610.01092v1 Announce Type: new Abstract: Video generation models are increasingly being explored as world simulators for embodied planning and learning. To do so effectively, these models must

Empty Commitments: When Agents Promise What Their Runtime Cannot Deliver

arXiv:2610.01045v1 Announce Type: new Abstract: A chatbot that says 'I will remind you tomorrow' will not run again until the user writes. We call such a promise an empty commitment: a promise of an a

Applications

Energy Time-Series Imputation with Differentially Private Diffusion Models via Clipping-Aware Objective Conditioning

arXiv:2610.00209v1 Announce Type: new Abstract: Reliable recovery of missing measurements is important for monitoring and analysis in energy time-series systems, where fine-grained measurements may co

applicationsarxiv-cs-lg

Tutorials

EvoGen-Harness: Learning Where and How to Evolve Image-Generation Harnesses

arXiv:2610.00383v1 Announce Type: new Abstract: Modern text-to-image (T2I) systems can be improved without modifying generator parameters by adapting the external system around frozen generators. Howe

tutorialsarxiv-cs-lg

Tutorials

Exploring More, Reasoning Better: Stepwise Risk-Sensitive GRPO for Diffusion Language Models

arXiv:2610.00661v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) generate text by denoising a sequence or successive blocks, allowing several tokens to be revealed in parallel

tutorialsarxiv-cs-ai

Applications

Exponential quantum advantage in processing massive classical data

arXiv:2604.07639v2 Announce Type: replace-cross Abstract: Broadly applicable quantum advantage, particularly in classical data processing and machine learning, has been a fundamental open problem. In

applicationsarxiv-cs-ai

EyeTAG: Eye Trajectory-Aware Gaze Estimation

arXiv:2610.00922v1 Announce Type: new Abstract: Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction. Single-frame methods predict

safetyarxiv-cs-cv

Fault-Tolerant Budget Conservation in Distributed Multi-Agent Delegation

arXiv:2610.00349v1 Announce Type: new Abstract: Resource limits are becoming an authorization boundary for AI agents that delegate work across concurrent and failure-prone workers. Parent-child alloca

FFBL-Coop: Association-Decoupled Cooperative 3D Multi-Object Tracking

arXiv:2610.01750v1 Announce Type: new Abstract: Cooperative 3D tracking must integrate complementary observations across agents and time while maintaining consistent identities. When evidence integrat

Applications

Foundations without Fundamentals: Zero-Shot Blind Spots in Time Series FMs

arXiv:2610.02058v1 Announce Type: new Abstract: Despite the success of Time Series Foundation Models (TSFMs) on broad benchmarks, their ability to internalize basic temporal logic, especially in setti

applicationsarxiv-cs-lg

From Network Intrusion Detection to Blockchain-Backed Endpoint Detection and Response: Mapping the Landscape of Decentralized Detection-and-Response Architectures

arXiv:2610.01872v1 Announce Type: cross Abstract: While the literature on blockchain-assisted intrusion detection and prevention systems (IDS/IPS) for Internet of Things (IoT) and Industrial Internet

FutureWorlds: Learning Robotic World Models from Alternative Futures

arXiv:2610.01019v1 Announce Type: new Abstract: Robotic world models predict action-conditioned future scenes, providing a foundation for understanding action outcomes. However, turning alternative pr

safetyarxiv-cs-cv

Generative Modeling of Stochastic Dynamics for Long-Time Evolution

model-releasesarxiv-cs-lg

arXiv:2610.00546v1 Announce Type: cross Abstract: Exact stochastic equations for non-equilibrium dynamics are rarely accessible. We show that the long-time evolution of stochastic dynamics can be pred

Grounding Large Language Models in DSGE Simulators for Policy Generation and Forecasting

arXiv:2610.01128v1 Announce Type: new Abstract: Large language models can produce economic policy responses that sound reasonable, but this does not show that their actions are consistent with economi

Hardening Soft Information: Evidence on Analyst Integration Costs

arXiv:2505.12269v4 Announce Type: replace-cross Abstract: We examine how the cost of transforming qualitative information into precise numerical estimates--a form of integration cost--creates a struct

Hierarchical Continuous Diffusion Language Models

arXiv:2610.02193v1 Announce Type: cross Abstract: Discrete diffusion language models offer a compelling alternative to autoregressive generation for tasks demanding bidirectional reasoning and global

HumanoidTTT: Test-Time Capability Reuse for Efficient Humanoid Control

arXiv:2610.00198v1 Announce Type: new Abstract: Recent advances in motion generation and whole-body tracking have enabled humanoid robots to execute increasingly diverse motions, yet the same motion c

researcharxiv-cs-ro

Learning Linear Systems under Heavy-Tailed Noise: A Non-Asymptotic Analysis from A Single Trajectory

arXiv:2610.00637v1 Announce Type: new Abstract: We establish non-asymptotic sample complexity bounds for the least-squares estimation of vector autoregressive models for exponentially stable systems w

M^2Weather: A Benchmark for Joint Multi-Station and Multi-Variable Weather Forecasting

model-releasesarxiv-cs-lg

arXiv:2610.00370v1 Announce Type: new Abstract: Station weather forecasting is fundamentally shaped by both complex spatial dependencies across stations and strong physical coupling among weather vari

Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning

model-releasesarxiv-cs-ai

arXiv:2610.01936v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable fluency across many tasks but remain limited by their static, parameter bound knowledge and th

MemFit: Efficient Long-Term Agentic Memory

arXiv:2610.00872v1 Announce Type: new Abstract: Long-term memory systems for large language models (LLMs) have gained popularity for extending reasoning capabilities across applications. Current memor

Multi-Behavioral Evolved Substrates Through Neuromodulation and Activation Selection

arXiv:2610.00148v1 Announce Type: cross Abstract: Open-ended artificial life systems must acquire diverse competencies from a single evolving genotype. Biological brains combine neuromodulation, which

Local Ai

OneStreamer: Unifying Perception, Memory, and Proactive Response in Streaming Video Interaction

arXiv:2610.01762v1 Announce Type: new Abstract: Streaming video LLMs must retain evidence before its relevance to future tasks is known and respond when sufficient evidence becomes available. The chal

local-aiarxiv-cs-cv

Participation-Sensitive Convergence and the Fragment First, Converge Later Pattern in Asynchronous Online Learning: A Topological Analysis Across 22 OULAD Courses

arXiv:2610.01738v1 Announce Type: cross Abstract: Asynchronous online learning offers temporal flexibility at a structural cost: learning communities tend to fragment rather than cohere. eta_0, the nu

safetyarxiv-cs-lg

PhysVista: Benchmarking Physical Intelligence in VLMs via a Perception-Reasoning-Assessment Loop

arXiv:2610.00559v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown strong multimodal reasoning capabilities, yet whether they truly capture the physical consistency underlying re

Quantifying the Impact of Ambulance Ramping: A Multi-Year Analysis of Victorian Emergency Medical Services Cases

arXiv:2610.00818v1 Announce Type: new Abstract: Ambulance ramping, the delay between hospital arrival and patient handover, is a critical operational bottleneck in Emergency Medical Services (EMS), ye

Rethinking the Information Bottleneck: Structured Decomposition under Label-Induced Partitions

arXiv:2610.01175v1 Announce Type: new Abstract: Standard information bottleneck (IB) regularization constrains representations via a single scalar I(Z;X), implicitlytreating all information as homogen

safetyarxiv-cs-lg

Same Reward, Different Skills: When Multimodal RL Learns to Look

model-releasesarxiv-cs-lg

arXiv:2610.01908v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves vision-language benchmark scores even without visual information during training. With im

Scalable, Transferable Meta-network for Data Selection Requires a Different Loss (and Why the Obvious Choice is Problematic)

arXiv:2610.02092v1 Announce Type: cross Abstract: Data selection is critical for training large language models on massive and heterogeneous corpora. Meta-learning for Training-data Selection offers a