Search: “persistence”

Bias Amplification in Multi-Agent Network: How Biased Agents Shape Opinions and Rhetoric

model-releasesarxiv-cs-lg

arXiv:2609.18306v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in applications involving interaction between agents, where their output plays a role in collecti

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Causal-History Test-Time Scaling for Failure Recovery in Autoregressive World-Action Models

DGX agent

arXiv:2609.18016v1 Announce Type: new Abstract: World-action models (WAMs) have emerged as a promising paradigm for robot manipulation by jointly modeling future visual dynamics and robot actions. How

applicationsarxiv-cs-ro

17 Sept 2026

Compiled Agency: Frontier General-Purpose Coding Agents Build Winning Game Players from Bare Interaction - from Flappy Bird to StarCraft II and Civilization

arXiv:2609.18996v1 Announce Type: new Abstract: LLM agents have repeatedly struggled to convert knowledge of a game into competent play, even when researchers build the agent around the model - supply

Disentangling Long-Term Memory via Latent Neuro-Symbolic Reasoning

arXiv:2609.18461v1 Announce Type: new Abstract: Personalized agents are required to reason over long-term history interactions to infer both explicit preferences and implicit behavioral evidence. Whil

Local Ai

Entropy in Conversational AI: Structured Unpredictability as Inferrable Interiority

arXiv:2609.19044v1 Announce Type: new Abstract: Sampling can increase response diversity without producing history-dependent behavior. We formalize a different design target, structured unpredictabili

local-aiarxiv-cs-cl

ERPBench: A State-Grounded Evaluation Paradigm for Computer-Use Agents in Enterprise Software

arXiv:2609.17885v1 Announce Type: new Abstract: Computer-use agents that operate through screenshots and simulated actions are advancing rapidly, yet their evaluation remains anchored to general deskt

EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses

arXiv:2608.28363v2 Announce Type: replace Abstract: LLM agents increasingly modify their own prompts, tools, middleware, resources, and execution harnesses at runtime. Such self-evolution can improve

agentsarxiv-cs-ai

In-Context Robot Learning with VLM Agents

arXiv:2609.19138v1 Announce Type: cross Abstract: Enabling robots to adapt to unfamiliar environments as readily as humans remains a moonshot goal of embodied AI. No finite collection of demonstration

safetyarxiv-cs-ro

Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data

arXiv:2609.18842v1 Announce Type: new Abstract: The scaling laws hold that a language model grows more capable with more parameters and more training data, and Mixture-of-Experts (MoE) architectures h

Multi-Hop Knowledge Composition is Bound by Pretraining Exposure

arXiv:2606.09338v2 Announce Type: replace Abstract: Large Language Models fail at implicit multi-hop reasoning: a model answers 'When was X born?' and 'Who is Y's closest friend?' correctly but fails

researcharxiv-cs-cl

PACT: Can Enterprise AI Assistants Be Trusted Under Pressure?

arXiv:2609.18605v1 Announce Type: cross Abstract: As corporate AI adoption continues to grow, enterprise-grade LLM agents are being deployed into sensitive contexts such as hiring, healthcare, and fin

Applications

Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for Clinical Adoption of Artificial Intelligence in Nigeria

arXiv:2609.19096v1 Announce Type: cross Abstract: Artificial intelligence (AI) is increasingly integrated into healthcare systems worldwide, yet its successful clinical adoption depends critically on

applicationsarxiv-cs-ai

Reflections on Trusting Trust, Revisited: Contaminating Self-Modifying AI Coding Agents with Poisoned Benchmarks

arXiv:2609.17817v1 Announce Type: cross Abstract: Thompson's 'Reflections on Trusting Trust' showed that a compiler can be poisoned to reinsert its own backdoor, so that even recompiling clean source

REVERSAL-BENCH: A Reversibility Axis and Reset Oracle for Measuring the Reset-Free RL Cliff

arXiv:2609.17745v1 Announce Type: cross Abstract: A central goal of autonomous reinforcement learning is continuous policy training without external resets. However, existing paradigms largely depend

Hardware

Risk-Aware World Modeling with Flow-Guided Occupancy Evolution for Selective Trajectory Planning in Automated Driving

arXiv:2609.18442v1 Announce Type: new Abstract: Safe motion planning in automated driving requires anticipating evolving traffic risks and deciding when to revise the current planned trajectory. We in

hardwarearxiv-cs-ai

Switchable-Polarity Electropermanent Magnet: Reconfigurable Magnetic Fields for Scalable Fluidic Control

arXiv:2603.24811v2 Announce Type: replace Abstract: Scalable control of pneumatic and fluidic networks remains fundamentally constrained by architectures that require continuous power input, dense ext

researcharxiv-cs-ro

The Free Inference Dimension: Complexity Measure for Zero-Collision Navigation under Hypothesis Mixtures

arXiv:2609.17816v1 Announce Type: cross Abstract: Solomonoff induction frames prediction as a mixture over computable hypotheses, typically leading to identification of the true environment. In a fini

agentsarxiv-cs-ai

Trajectory Learnability for Offline On-Policy Distillation with Imperfect Teachers

arXiv:2609.18321v1 Announce Type: cross Abstract: Offline on-policy distillation gains efficiency by collecting student trajectories and teacher supervision once and reusing them throughout optimizati

UAVs Meet Embodied Intelligence: Bridging Human Intents and Flying Dynamics Via Harnessing Physical-Digital AI Agents

arXiv:2609.18326v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) extend embodied intelligence into continuous three-dimensional space, where perception, reasoning, physical embodiment,

agentsarxiv-cs-ro

Visual Compliance via Executable Safety Rule Entailment

arXiv:2609.18328v1 Announce Type: new Abstract: Recent advances in LLMs and VLMs have enabled safety systems to reason beyond simple risk patterns toward more contextual and semantic safety concerns.

When Audit Quality Fails to Predict Downstream Utility: A Counterfactual Study of Synthetic-Data Selectors for Low-Resource African NLP

arXiv:2609.18960v1 Announce Type: new Abstract: Quality-aware synthetic-data selection rests on a proxy: examples that an LLM judge rates as good should also help a downstream model learn. In a contro

tutorialsarxiv-cs-cl

Applications

Adaptive Bayesian Partner Selection for Federated Clinical Centers

arXiv:2609.16446v1 Announce Type: new Abstract: Federated learning (FL) in healthcare faces pronounced heterogeneity and temporal concept drift across clinical centers, where evolving patient populati

applicationsarxiv-cs-lg

AI for Games in the Foundation Model Era

arXiv:2609.16679v1 Announce Type: new Abstract: Foundation models, alongside advances in learned game-world models, are reshaping AI across the game lifecycle. Beyond playing games, recent systems mod

AsyncCouple-Flow: Asynchronous Cross-Modal Coupling and Flow Matching for Spatio-Temporal Forecasting

model-releasesarxiv-cs-lg

arXiv:2609.16573v1 Announce Type: new Abstract: Multi-modal spatio-temporal forecasting (MM-STF) supports weather nowcasting, traffic prediction, and earth-system modeling by combining heterogeneous s

Attention is All You Need Until You Need Retention

arXiv:2501.09166v2 Announce Type: replace-cross Abstract: Pretrained Transformers keep what they learned in their weights and lose what they observe once a session ends. The first version of this pape

tutorialsarxiv-cs-ai

Local Ai

Cognitive Admission Control: Risk-Conditioned Assurance for Consequential Actions in Agentic Distributed Systems

arXiv:2609.16313v1 Announce Type: cross Abstract: In agentic distributed systems, an agent may be authorized to mutate external infrastructure while lacking evidence that the mutation is ready to exec

local-aiarxiv-cs-ai

Coupled Calibration and Learning: Mitigating Teacher Bias in LLM Distillation without Target-Domain Reward Feedback

arXiv:2609.17474v1 Announce Type: cross Abstract: Large language model (LLM) distillation aims to transfer the capabilities of a powerful teacher to a smaller student. Direct imitation, however, can a

Decomposition Buys Integrity, Not Yield

arXiv:2609.17464v1 Announce Type: cross Abstract: Multi-agent systems split a task across a tree of agents and justify the split with folklore: smaller contexts, cleaner separation, parallelism. We as

LCAP: Population-Informed Latent Chip Adaptation from Few Output Probes for Photonic Neural Networks

arXiv:2609.16823v1 Announce Type: new Abstract: Photonic neural networks (PNNs) offer efficient analog inference, but parameters optimized under ideal device models can degrade after fabrication, crea

researcharxiv-cs-lg

Lesion-centered 3D mapping of colonoscopy procedures: validation of a hierarchical ensemble pipeline on public benchmark videos

model-releasesarxiv-cs-cv

arXiv:2609.16672v1 Announce Type: new Abstract: Background and Objective: Colonoscopy recording practice preserves text reports and still photographs, while the spatial information already present in

Memorisation bias in medical AI

arXiv:2609.17223v1 Announce Type: new Abstract: Medical AI models hold immense potential to improve patient outcomes, but they are also known to unintentionally memorise individual records from their

safetyarxiv-cs-lg

Partial recovery of meter-scale surface weather

arXiv:2602.23146v2 Announce Type: replace-cross Abstract: Near-surface weather varies over tens to hundreds of meters, yet remains unresolved in analyses and forecasts. We test whether this variation

researcharxiv-cs-cv

Repurposing Unified Topological Signatures for Graph Representation Learning

arXiv:2609.17061v1 Announce Type: cross Abstract: Message-passing Graph Neural Networks (GNNs) iteratively propagate and aggregate local neighborhood information followed by global readout to learn gr

tutorialsarxiv-cs-ai

RoleBreak: Benchmarking Long-Horizon Role-Playing Robustness in Spoken Dialogue

arXiv:2609.16614v1 Announce Type: cross Abstract: Speech-to-speech dialogue models increasingly support persona control, yet existing spoken role-playing benchmarks remain largely character-centric an

Same Problem, Different Field: Cross-Domain Solution Import via Domain-Stripped Computational Fingerprints

model-releasesarxiv-cs-cl

arXiv:2609.07595v2 Announce Type: replace-cross Abstract: The same underlying computational problem is solved across unrelated fields under different names: recursive Bayesian state estimation appears

Schema-Adaptive Action-Conditioned JEPA for Cross-Machine CNC Transfer under Partial Sensor Overlap

arXiv:2609.16071v1 Announce Type: cross Abstract: Cross-machine deployment of industrial world models requires transfer across changes in dynamics, sensing interfaces, sampling regimes, and control un

Self-reported archetypes and behavioral failures in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2609.15998v1 Announce Type: new Abstract: Every large language model (LLM) has behavioral traits and moral preferences that comprise its character. Whether by design or as an emergent property o

Semantic-Spatial Agreement Verification for Mitigating Object Hallucination in Multimodal Large Language Models

arXiv:2609.17269v1 Announce Type: new Abstract: Multimodal large language models generate natural-language responses from visual inputs, yet may mention objects absent from an image. In medication ass

safetyarxiv-cs-cv

TEMPO: Learning Temporal Context for Dynamic Robot Manipulation

model-releasesarxiv-cs-cv

arXiv:2609.16864v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have achieved impressive performance in quasi-static manipulation, but struggle in dynamic manipulation tasks beca

Toward Governance-Aware Autonomous GIS: A Narrative Review of Ethical and Privacy Risks in LLM-Enabled GeoAI

arXiv:2609.16232v1 Announce Type: new Abstract: Geospatial artificial intelligence (GeoAI) powered by large language models (LLMs) is expanding the capacity to query, generate, and interpret spatial i

Towards Illusions Awareness in Cyber-Physical System's Design

arXiv:2609.17260v1 Announce Type: new Abstract: Cyber-Physical Systems (CPS) operate through a continuous sense-compute-act loop within an open context environment, making it impossible to anticipate

researcharxiv-cs-cl

Local Ai

When Confidence Signals Disagree: Local and Global Confidence in Autoregressive Language Models

arXiv:2609.16933v1 Announce Type: cross Abstract: Modern predictive systems expose multiple quantities that are commonly interpreted as measures of confidence. However, these quantities can summarize

local-aiarxiv-cs-ai

World Model Science: Self-Organized Criticality, Weak Chaos, and Metastable Belief Dynamics in Long-Horizon LLM Agents

arXiv:2609.17419v1 Announce Type: new Abstract: Long-horizon LLM agents must maintain task state across extended sequences of observations, actions, tool calls, and intermediate beliefs. We study thes

A Chosen Future Can Still Be Rewritten: Causal Writability in Video Models

arXiv:2609.15980v1 Announce Type: cross Abstract: When a video model generates physically incorrect motion, did it fail to learn the correct motion, or did it learn it but fail to use it? We show the

tutorialsarxiv-cs-cv

A Voxel-Spacing-Aware Extension of PyRadiomics for Anisotropic Texture Analysis

arXiv:2609.14103v1 Announce Type: cross Abstract: Radiomic texture features are commonly extracted from anisotropic CT and MRI acquisitions, where identical voxel offsets may represent different physi

Aerial Wildfire Suppression Planning with a Hybrid CNN-Cellular Automata Fire Model

arXiv:2606.13633v2 Announce Type: replace-cross Abstract: Aerial wildfire suppression requires decisions about when, where, and how to deploy limited aircraft. We present an intervention-design framew

tutorialsarxiv-cs-lg

AgentKV: Phase-Aware KV Eviction for Agentic LLMs

arXiv:2609.14872v1 Announce Type: cross Abstract: Agentic serving can consume orders of magnitude more tokens than chatbot workloads, stressing both KV-cache capacity and decode-time bandwidth. Most K

agentsarxiv-cs-cl

Another Blueprint In The Wall: How to Ask Frontier AI Like a Kid?

arXiv:2609.14803v1 Announce Type: new Abstract: This paper reports experiments across six frontier model types from OpenAI, Anthropic, xAI, and Google DeepMind. Ten independent sessions per model type