Search: “agents”

The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence

arXiv:2408.12622v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is reshaping society, from video generation to medical diagnosis, coding agents to autonomous vehicles. Yet resea

safetyarxiv-cs-lg

6 May 2026

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Model Releases

Towards Understanding Specification Gaming in Reasoning Models

DGX agent

arXiv:2605.02269v1 Announce Type: new Abstract: Specification gaming is a critical failure mode of LLM agents. Despite this, there has been little systematic research into when it arises and what driv

model-releasesarxiv-cs-ai

6 May 2026

Tutorials

ValueBlindBench: Agreement-Gated Stress Testing of LLM-Judged Investment Rationales Before Returns Are Observable

arXiv:2604.25224v2 Announce Type: replace Abstract: LLM-based financial agents increasingly produce investment rationales before the outcomes needed to evaluate them are observable. This creates a del

tutorialsarxiv-cs-ai

6 May 2026

AFFormer: Adaptive Feature Fusion Transformer for V2X Cooperative Perception under Channel Impairments

arXiv:2605.01888v1 Announce Type: new Abstract: Accurate 3D object detection is essential for ensuring the safety of autonomous vehicles. Cooperative perception, which leverages vehicle-to-everything

safetyarxiv-cs-cv

Assistance Without Interruption: A Benchmark and LLM-based Framework for Non-Intrusive Human-Robot Assistance

model-releasesarxiv-cs-ro

arXiv:2605.01368v1 Announce Type: new Abstract: Human-robot interaction (HRI) has long studied how agents and people coordinate to achieve shared goals. In this work, we formalize and benchmark the no

Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs

arXiv:2605.01242v1 Announce Type: new Abstract: Reinforcement learning (RL) is a fundamental framework for sequential decision-making, in which an agent learns an optimal policy through interactions w

safetyarxiv-cs-lg

GRAVITY: Architecture-Agnostic Structured Anchoring for Long-Horizon Conversational Memory

arXiv:2605.01688v1 Announce Type: new Abstract: Long-horizon conversational agents rely on memory systems with increasingly sophisticated retrieval mechanisms. However, retrieved fragments are typical

researcharxiv-cs-cl

Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design

arXiv:2605.00931v1 Announce Type: new Abstract: Federated learning (FL) is fundamentally a distributed optimization problem executed by communicating agents with local data, local computation, and par

local-aiarxiv-cs-lg

HyMem: Hybrid Memory Architecture with Dynamic Retrieval Scheduling

arXiv:2602.13933v2 Announce Type: replace Abstract: Large language model (LLM) agents demonstrate strong performance in short-text contexts but often underperform in extended dialogues due to ineffici

researcharxiv-cs-ai

Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare

arXiv:2605.01961v1 Announce Type: new Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents. However

safetyarxiv-cs-lg

Robust volatility updates for Hierarchical Gaussian Filtering

arXiv:2605.00966v1 Announce Type: new Abstract: Hierarchical Gaussian Filtering (HGF) networks allow for efficient updating of posterior distributions (beliefs) about hidden states of an agent's envir

The Compliance Gap: Why AI Systems Promise to Follow Process Instructions but Don't

arXiv:2605.01771v1 Announce Type: new Abstract: An auditor instructs an AI assistant: 'open each file individually using the Read tool -- no scripts, no agents.' The AI replies 'Yes' -- then issues a

When Correct Isn't Usable: Improving Structured Output Reliability in Small Language Models

arXiv:2605.02363v1 Announce Type: new Abstract: Deployed language models must produce outputs that are both correct and format-compliant. We study this structured-output reliability gap using two math

LLM-Oriented Information Retrieval: A Denoising-First Perspective

arXiv:2605.00505v1 Announce Type: cross Abstract: Modern information retrieval (IR) is no longer consumed primarily by humans but increasingly by large language models (LLMs) via retrieval-augmented g

Applications

Scaling Federated Linear Contextual Bandits via Sketching

arXiv:2605.00500v1 Announce Type: new Abstract: In federated contextual linear bandits, high data dimensionality incurs prohibitive computation and communication costs: local agents perform O(d^3)-tim

applicationsarxiv-cs-lg

ScreenParse: Moving Beyond Sparse Grounding with Complete Screen Parsing Supervision

model-releasesarxiv-cs-cv

arXiv:2602.14276v2 Announce Type: replace Abstract: Modern computer-use agents (CUA) must perceive a screen as a structured state, what elements are visible, where they are, and what text they contain

Value Explicit Pretraining for Learning Transferable Representations

arXiv:2312.12339v3 Announce Type: replace Abstract: Understanding visual inputs for a given task amidst varied changes is a key challenge posed by visual reinforcement learning agents. We propose exti

Attractor FCM

arXiv:2604.27947v1 Announce Type: cross Abstract: In this paper an attractor FCM is created, tested, and analyzed. This FCM is neither a hebbian based nor agentic, nor a hybrid; it rather is a gradien

D3-Gym: Constructing Real-World Verifiable Environments for Data-Driven Discovery

arXiv:2604.27977v1 Announce Type: new Abstract: Despite recent progress in language models and agents for scientific data-driven discovery, further advancing their capabilities is held back by the abs

Detecting is Easy, Adapting is Hard: Local Expert Growth for Visual Model-Based Reinforcement Learning under Distribution Shift

arXiv:2604.27411v1 Announce Type: new Abstract: Visual model-based reinforcement learning (MBRL) agents can perform well on the training distribution, but often break down once the test environment sh

local-aiarxiv-cs-lg

Hardware

EdgeFM: Efficient Edge Inference for Vision-Language Models

arXiv:2604.27476v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated strong applicability in edge industrial applications, yet their deployment remains severely constrained

hardwarearxiv-cs-cv

Tutorials

Graph World Models: Concepts, Taxonomy, and Future Directions

arXiv:2604.27895v1 Announce Type: new Abstract: As one of the mainstream models of artificial intelligence, world models allow agents to learn the representation of the environment for efficient predi

tutorialsarxiv-cs-ai

Learning to Forget: Continual Learning with Adaptive Weight Decay

arXiv:2604.27063v1 Announce Type: new Abstract: Continual learning agents with finite capacity must balance acquiring new knowledge with retaining the old. This requires controlled forgetting of knowl

NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria

arXiv:2510.18183v2 Announce Type: replace Abstract: Finding Nash equilibria in two-player zero-sum imperfect-information games remains a central challenge in multi-agent reinforcement learning. Recent

Simulating Infant First-Person Sensorimotor Experience via Motion Retargeting from Babies to Humanoids

arXiv:2604.27583v1 Announce Type: cross Abstract: Motion retargeting from humans to human-like artificial agents is becoming increasingly important as humanoid robots grow more capable. However, most

researcharxiv-cs-ro

What Suppresses Nash Equilibrium Play in Large Language Models? Mechanistic Evidence and Causal Control

arXiv:2604.27167v1 Announce Type: cross Abstract: LLM agents are known to deviate from Nash equilibria in strategic interactions, but nobody has looked inside the model to understand why, or asked whe

A Decision-Theoretic Formalisation of Steganography With Applications to LLM Monitoring

arXiv:2602.23163v3 Announce Type: replace Abstract: Large language models are beginning to show steganographic capabilities. Such capabilities could allow misaligned models to evade oversight mechanis

researcharxiv-cs-ai

Beyond Screenshots: Evaluating VLMs' Understanding of UI Animations

arXiv:2604.26148v1 Announce Type: cross Abstract: AI agents operating on user interfaces must understand how interfaces communicate state and feedback to act reliably. As a core communicative modality

researcharxiv-cs-cl

Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields

arXiv:2604.26095v1 Announce Type: new Abstract: {Closed-loop inverse source localization and characterization (ISLC) requires a mobile agent to select measurements that localize sources and infer late

Inferix: A Block-Diffusion based Next-Generation Inference Engine for World Simulation

arXiv:2511.20714v2 Announce Type: replace-cross Abstract: World models serve as core simulators for fields such as agentic AI, embodied AI, and gaming, capable of generating long, physically realistic

LLM Psychosis: A Theoretical and Diagnostic Framework for Reality-Boundary Failures in Large Language Models

arXiv:2604.25934v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) as interactive agents has exposed a category of behavioral failure that prevailing terminology, princip

Thinking About Thinking: Evaluating Reasoning in Post-Trained Language Models

arXiv:2510.16340v2 Announce Type: replace Abstract: Recent advances in post-training techniques have endowed Large Language Models (LLMs) with enhanced capabilities for tackling complex, logic-intensi

safetyarxiv-cs-cl

29 Apr 2026

Benchmarking Source-Sensitive Reasoning in Turkish: Humans and LLMs under Evidential Trust Manipulation

arXiv:2604.24665v1 Announce Type: cross Abstract: This paper investigates whether source trustworthiness shapes Turkish evidential morphology and whether large language models (LLMs) track this sensit

Domain-Filtered Knowledge Graphs from Sparse Autoencoder Features

arXiv:2604.23829v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) extract millions of interpretable features from a language model, but flat feature inventories aren't very useful on their ow

Evaluating whether AI models would sabotage AI safety research

arXiv:2604.24618v1 Announce Type: new Abstract: We evaluate the propensity of frontier models to sabotage or refuse to assist with safety research when deployed as AI research agents within a frontier

Grammar-Constrained Refinement of Safety Operational Rules Using Language in the Loop: What Could Go Wrong

arXiv:2604.23523v1 Announce Type: cross Abstract: Safety specifications in cyber-physical systems (CPS) capture the operational conditions the system must satisfy to operate safely within its intended

InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation

arXiv:2602.23024v2 Announce Type: replace Abstract: Mobile manipulation is a fundamental capability for general-purpose robotic agents, requiring both coordinated control of the mobile base and manipu

safetyarxiv-cs-ro

IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance

arXiv:2604.23446v1 Announce Type: new Abstract: Industrial maintenance environments increasingly rely on AI systems to assist operators in understanding asset behavior, diagnosing failures, and evalua

Intervention-Aware Multiscale Representation Learning from Imaging Phenomics and Perturbation Transcriptomics

arXiv:2604.22832v1 Announce Type: cross Abstract: Microscopy-based phenotypic profiling is scalable for drug discovery but lacks the mechanistic depth of transcriptomics, which remains costly and scar

Learning Selective LLM Autonomy from Copilot Feedback in Enterprise Customer Support Workflows

arXiv:2604.23855v1 Announce Type: new Abstract: We present a deployed system that automates end-to-end customer support workflows inside an enterprise Business Process Management (BPM) platform. The a

safetyarxiv-cs-cl

RAT: RunAnyThing via Fully Automated Environment Configuration

arXiv:2604.23190v1 Announce Type: cross Abstract: Automating repository-level software engineering tasks is a foundational challenge for autonomous code agents, largely due to the difficulty of config

SMP: Reusable Score-Matching Motion Priors for Physics-Based Character Control

arXiv:2512.03028v3 Announce Type: replace-cross Abstract: Data-driven motion priors that can guide agents toward producing naturalistic behaviors play a pivotal role in creating life-like virtual char

Tutorials

Context-Sensitive Abstractions for Reinforcement Learning with Parameterized Actions

arXiv:2512.20831v2 Announce Type: replace Abstract: Real-world sequential decision-making often involves parameterized action spaces that require both, decisions regarding discrete actions and decisio

tutorialsarxiv-cs-ai

27 Apr 2026

When Does LLM Self-Correction Help? A Control-Theoretic Markov Diagnostic and Verify-First Intervention

arXiv:2604.22273v1 Announce Type: new Abstract: Iterative self-correction is widely used in agentic LLM systems, but when repeated refinement helps versus hurts remains unclear. We frame self-correcti

27 Apr 2026

Do MLLMs Understand Pointing? Benchmarking and Enhancing Referential Reasoning in Egocentric Vision

model-releasesarxiv-cs-cv

arXiv:2604.21461v1 Announce Type: new Abstract: Egocentric AI agents, such as smart glasses, rely on pointing gestures to resolve referential ambiguities in natural language commands. However, despite

Frozen LLMs as Map-Aware Spatio-Temporal Reasoners for Vehicle Trajectory Prediction

arXiv:2604.21479v1 Announce Type: new Abstract: Large language models (LLMs) have recently demonstrated strong reasoning capabilities and attracted increasing research attention in the field of autono

local-aiarxiv-cs-cv

Hyperloop Transformers

arXiv:2604.21254v1 Announce Type: cross Abstract: LLM architecture research generally aims to maximize model quality subject to fixed compute/latency budgets. However, many applications of interest su

Inferring High-Level Events from Timestamped Data: Complexity and Medical Applications

arXiv:2604.21793v1 Announce Type: new Abstract: In this paper, we develop a novel logic-based approach to detecting high-level temporally extended events from timestamped data and background knowledge