Search: “agents”

Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling

arXiv:2605.12312v1 Announce Type: new Abstract: Random delays weaken the temporal correspondence between actions and subsequent state feedback, making it difficult for agents to identify the true prop

13 May 2026

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Hardware

TriBand-BEV: Real-Time LiDAR-Only 3D Pedestrian Detection via Height-Aware BEV and High-Resolution Feature Fusion

DGX agent

arXiv:2605.12220v1 Announce Type: new Abstract: Safe autonomous agents and mobile robots need fast real time 3D perception, especially for vulnerable road users (VRUs) such as pedestrians. We introduc

hardwarearxiv-cs-cv

13 May 2026

UHR-Micro: Diagnosing and Mitigating the Resolution Illusion in Earth Observation VLMs

model-releasesarxiv-cs-cv

arXiv:2605.12237v1 Announce Type: new Abstract: Vision-Language Models (VLMs) increasingly operate on ultra-high-resolution (UHR) Earth observation imagery, yet they remain vulnerable to a severe scal

13 May 2026

Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2605.10034v1 Announce Type: new Abstract: Recent Autonomous Driving (AD) works such as GigaFlow and PufferDrive have unlocked Reinforcement Learning (RL) at scale as a training strategy for driv

Beyond Thinking: Imagining in 360^irc for Humanoid Visual Search

arXiv:2605.09146v1 Announce Type: new Abstract: Humanoid Visual Search (HVS) requires agents to actively explore immersive 360^irc environments. While prior methods treat this as a monolithic task rel

researcharxiv-cs-cv

Do Benchmarks Underestimate LLM Performance? Evaluating Hallucination Detection With LLM-First Human-Adjudicated Assessment

arXiv:2605.08462v1 Announce Type: cross Abstract: Hallucination remains a persistent challenge in Large Language Models (LLMs), particularly in context-grounded settings such as RAG and agentic AI sys

Tutorials

Efficient Estimation of Kernel Surrogate Models for Task Attribution

arXiv:2602.03783v2 Announce Type: replace-cross Abstract: Modern AI agents such as large language models are trained on diverse tasks -- translation, code generation, mathematical reasoning, and text

tutorialsarxiv-cs-ai

Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthesis

arXiv:2605.10910v1 Announce Type: cross Abstract: We consider the problem of synthesizing Clifford quantum circuits for devices with all-to-all qubit connectivity. We approach this task as a reinforce

Execution Envelopes: A Shared Admission Contract for Backend AI Execution Requests

arXiv:2605.08267v1 Announce Type: cross Abstract: Enterprise AI backends increasingly admit heterogeneous execution requests across model deployment, inference, evaluation, data movement, and agentic

Governed Metaprogramming for Intelligent Systems: Reclassifying Eval as a Governed Effect

arXiv:2605.05248v2 Announce Type: replace-cross Abstract: AI systems increasingly synthesize executable structure at runtime: LLMs generate programs, agents construct workflows,self-improving systems

Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning

arXiv:2605.10546v1 Announce Type: new Abstract: Pixel-based deep reinforcement learning agents are typically trained on heavily downsampled visual observations, a convention inherited from early bench

Learning Strategic Value and Cooperation in Multi-Player Stochastic Games through Side Payments

arXiv:2303.05307v2 Announce Type: replace-cross Abstract: We study general-sum, multi-player stochastic games with transferable utility, motivated by settings where agents can use side payments to mak

researcharxiv-cs-ai

LLM Advertisement based on Neuron Auctions

arXiv:2605.08326v1 Announce Type: cross Abstract: As Large Language Models (LLMs) transition into conversational agents, generative advertising emerges as a crucial monetization strategy. However, emb

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI

arXiv:2605.08678v1 Announce Type: new Abstract: Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes. As large language models demonst

Neural Co-state Policies: Structuring Hidden States in Recurrent Reinforcement Learning

arXiv:2605.05373v2 Announce Type: replace Abstract: A key capability of intelligent agents is operating under partial observability: reasoning and acting effectively despite missing or incomplete stat

PDEAgent-Bench: A Multi-Metric, Multi-Library Benchmark for PDE Solver Generation

arXiv:2605.09636v1 Announce Type: new Abstract: PDE-to-solver code generation aims to automatically synthesize executable numerical solvers from partial differential equation (PDE) specifications. Thi

Local Ai

Playing games with knowledge: AI-Induced delusions need game theoretic interventions

arXiv:2605.08409v1 Announce Type: new Abstract: Conversational AI has a fundamental flaw as a knowledge interface: sycophantic chatbots induce epistemic entrenchment and delusional belief spirals even

local-aiarxiv-cs-ai

Position: AI Security Policy Should Target Systems, Not Models

arXiv:2605.09504v1 Announce Type: cross Abstract: We present swarm-attack, an open-source adversarial testing framework in which multiple lightweight LLM agents coordinate through shared memory, paral

Reflective Prompted Policy Optimization: Trajectory-Grounded Revision and Salience Bias

arXiv:2605.08315v1 Announce Type: new Abstract: Existing LLM-based policy optimizers see only scalar rewards: that a policy scored 0.45, but not whether the agent got stuck in a loop, fell into a hole

SalesSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators

arXiv:2605.08334v1 Announce Type: new Abstract: We present SalesSim, a framework and testbed for evaluating the ability of Multimodal Large Language Models (MLLMs) to simulate realistic, persona-drive

safetyarxiv-cs-cl

Selective Deficits in LLM Mental Self-Modeling in a Behavior-Based Test of Theory of Mind

arXiv:2603.26089v2 Announce Type: replace-cross Abstract: The ability to represent oneself and others as agents with knowledge, intentions, and belief states that guide their behavior - Theory of Mind

Shields to Guarantee Probabilistic Safety in MDPs

arXiv:2605.10888v1 Announce Type: cross Abstract: Shielding is a prominent model-based technique to ensure safety of autonomous agents. Classical shielding aims to ensure that nothing bad ever happens

Statistical Model Checking of the Keynes+Schumpeter Model: A Transient Sensitivity Analysis of a Macroeconomic ABM

arXiv:2605.10447v1 Announce Type: cross Abstract: Agent-based models (ABMs) are increasingly used in macroeconomics, but their analysis still often relies on ad hoc Monte Carlo campaigns with heteroge

Step Rejection Fine-Tuning: A Practical Distillation Recipe

arXiv:2605.10674v1 Announce Type: cross Abstract: Rejection Fine-Tuning (RFT) is a standard method for training LLM agents, where unsuccessful trajectories are discarded from the training set. In the

Talk to Your Slides: High-Efficiency Slide Editing via Language-Driven Structured Data Manipulation

model-releasesarxiv-cs-cl

arXiv:2505.11604v5 Announce Type: replace Abstract: Editing presentation slides is a frequent yet tedious task, ranging from creative layout design to repetitive text maintenance. While recent GUI-bas

TIE: Time Interval Encoding for Video Generation over Events

arXiv:2605.10543v1 Announce Type: new Abstract: Director-style prompting, robotic action prediction, and interactive video agents demand temporal grounding over concurrent events -- a regime in which

safetyarxiv-cs-cv

Towards Conversational Medical AI with Eyes, Ears and a Voice

arXiv:2605.09272v1 Announce Type: new Abstract: The practice of medicine relies not only upon skillful dialogue but also on the nuanced exchange and interpretation of rich auditory and visual cues bet

When Reviews Disagree: Fine-Grained Contradiction Analysis in Scientific Peer Reviews

arXiv:2605.10171v1 Announce Type: cross Abstract: Scientific peer reviews frequently contain conflicting expert judgments, and the increasing scale of conference submissions makes it challenging for A

Applications

AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models

arXiv:2605.07308v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have significantly advanced the capabilities of robotic agents in executing diverse tasks; however, they still face

applicationsarxiv-cs-ro

Beyond Linear Attention: Softmax Transformers Implement In-Context Reinforcement Learning

arXiv:2605.07333v1 Announce Type: new Abstract: In-context reinforcement learning (ICRL) studies agents that, after pretraining, adapt to new tasks by conditioning on additional context without parame

Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matching

model-releasesarxiv-cs-cv

arXiv:2601.15884v2 Announce Type: replace Abstract: Contrast-enhanced imaging is central to oncologic diagnosis, but contrast agents can be contraindicated for many of the patients who need them most.

Convergence and Emergence of In-Context Reinforcement Learning with Chain of Thought

arXiv:2605.07123v1 Announce Type: new Abstract: In-context reinforcement learning (ICRL) refers to the ability of RL agents to adapt to new tasks at inference time without parameter updates by conditi

Decentralized Time-Varying Optimization for Streaming Data via Temporal Weighting

arXiv:2605.06971v1 Announce Type: cross Abstract: Classical optimization theory largely focuses on fixed objective functions, whereas many modern learning systems operate in dynamic environments where

Discovering Ordinary Differential Equations with LLM-Based Qualitative and Quantitative Evaluation

arXiv:2605.07323v1 Announce Type: new Abstract: Discovering governing differential equations from observational data is a fundamental challenge in scientific machine learning. Existing symbolic regres

Echo: KV-Cache-Free Associative Recall with Spectral Koopman Operators

arXiv:2605.06997v1 Announce Type: new Abstract: Long chain-of-thought reasoning and agentic tool-calling produce traces spanning tens of thousands of tokens, yet Transformer KV caches grow linearly wi

Entropy-Regularized Adjoint Matching for Offline Reinforcement Learning

arXiv:2605.06156v2 Announce Type: replace-cross Abstract: Integrating expressive generative policies, such as flow-matching models, into offline reinforcement learning (RL) allows agents to capture co

Multi-Environment POMDPs with Finite-Horizon Objectives

arXiv:2605.07537v1 Announce Type: new Abstract: Partially Observable Markov Decision Processes (POMDPs) are systems in which one agent interacts with a stochastic environment, and receives only partia

Multi-Objective Constraint Inference using Inverse reinforcement learning

arXiv:2605.06951v1 Announce Type: new Abstract: Constraint inference is widely considered essential to align reinforcement learning agents with safety boundaries and operational guidelines by observin

SB-TRPO: Towards Safe Reinforcement Learning with Hard Constraints

arXiv:2512.23770v3 Announce Type: replace-cross Abstract: In safety-critical domains, reinforcement learning (RL) agents must often satisfy strict, zero-cost safety constraints while accomplishing tas

Sparse Autoencoders as Plug-and-Play Firewalls for Adversarial Attack Detection in VLMs

arXiv:2605.07447v1 Announce Type: cross Abstract: Vision-language models (VLMs) have advanced rapidly and are increasingly deployed in real-world applications, especially with the rise of agent-based

Distilling Bayesian Belief States into Language Models for Auditable Negotiation

arXiv:2605.04507v1 Announce Type: new Abstract: Negotiation agents must infer what their counterpart values, update those beliefs over dialogue turns, and choose actions under uncertainty. End-to-end

safetyarxiv-cs-cl

7 May 2026

A Benchmark for Interactive World Models with a Unified Action Generation Framework

model-releasesarxiv-cs-cv

arXiv:2605.03941v1 Announce Type: new Abstract: Achieving Artificial General Intelligence (AGI) requires agents that learn and interact adaptively, with interactive world models providing scalable env

A Sentence Relation-Based Approach to Sanitizing Malicious Instructions

arXiv:2605.01078v1 Announce Type: cross Abstract: Retrieval-augmented generation and tool-integrated LLM agents increasingly depend on external textual sources. This reliance broadens the available at

researcharxiv-cs-ai

Intervention Complexity as a Canonical Reward and a Measure of Intelligence

arXiv:2605.02175v1 Announce Type: new Abstract: The Legg--Hutter universal intelligence measure provides a rigorous scalar assessment of general intelligence as expected reward across all computable e

MEMAUDIT: An Exact Package-Oracle Evaluation Protocol for Budgeted Long-Term LLM Memory Writing

arXiv:2605.02199v1 Announce Type: new Abstract: Long-term LLM agents must compress streams of past interactions into persistent memory before future queries are known. Existing evaluations usually mea

researcharxiv-cs-ai

Local Ai

On-Device Fine-Tuning via Backprop-Free Zeroth-Order Optimization

arXiv:2511.11362v2 Announce Type: replace-cross Abstract: On-device fine-tuning is a critical capability for edge AI systems, which must support adaptation to different agentic tasks under stringent m

local-aiarxiv-cs-cl

Poly-EPO: Training Exploratory Reasoning Models

arXiv:2604.17654v3 Announce Type: replace Abstract: Exploration is a cornerstone of learning from experience: it enables agents to find solutions to complex problems, generalize to novel ones, and sca

Safety and accuracy follow different scaling laws in clinical large language models

model-releasesarxiv-cs-cl

arXiv:2605.04039v1 Announce Type: new Abstract: Clinical LLMs are often scaled by increasing model size, context length, retrieval complexity, or inference-time compute, with the implicit expectation