Search: “agents”

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

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Position: AI Security Policy Should Target Systems, Not Models

DGX agent

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

model-releasesarxiv-cs-ai

12 May 2026

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

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

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

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

Towards Understanding Specification Gaming in Reasoning Models

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

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

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

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

Local Ai

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

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

4 May 2026

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

4 May 2026

ScreenParse: Moving Beyond Sparse Grounding with Complete Screen Parsing Supervision