Search: “agents”

Safe In-Context Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2509.25582v3 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks w

28 May 2026

Self-Improving Language Models with Bidirectional Evolutionary Search

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DGX agent

arXiv:2605.28814v1 Announce Type: new Abstract: Search has been proposed as an effective method for self-improving language models and agentic systems, both for post-training sample generation and for

agentsarxiv-cs-cl

28 May 2026

The Optimal Sample Complexity of Linear Contracts

arXiv:2601.01496v2 Announce Type: replace-cross Abstract: In this paper, we settle the problem of learning optimal linear contracts from data in the offline setting, where agent types are drawn from a

28 May 2026

Can Retrieval Heads See Images? Multimodal Retrieval Heads in Long-Context Vision-Language Models

arXiv:2605.27243v1 Announce Type: new Abstract: Large vision-language models increasingly rely on long-context modeling to reason over documents, hour-level videos, and long-horizon agent trajectories

Real-Time Progress Prediction in Reasoning Language Models

arXiv:2506.23274v4 Announce Type: replace-cross Abstract: Recent reasoning language models, particularly those that employ long latent chains of thought, achieve strong performance on complex agentic

Self-signals Driven Multi-LLM Debate for Efficient and Accurate Reasoning

arXiv:2510.06843v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have exhibited impressive capabilities across diverse application domains. Recent work has explored Multi-LLM Age

The Sensation Modulating Network:Haltability as the architectural ground for object-directed phenomenology

arXiv:2605.26856v1 Announce Type: cross Abstract: Cognitive science remains split between cognitivism - which accounts for recursion and language but cannot ground formal symbols in meaning - and 4E a

AION: Next-Generation Tasks and Practical Harness for Time Series

arXiv:2605.25045v1 Announce Type: new Abstract: Time series research is moving beyond fixed forecasting benchmarks toward realistic tasks that combine prediction, contextual reasoning, tool use, and s

Claw-Anything: Benchmarking Always-On Personal Assistants with Broader Access to User's Digital World

arXiv:2605.26086v1 Announce Type: new Abstract: Large language model agents are increasingly envisioned as always-on personal assistants with access to anything relevant in the user's digital world. Y

Local Ai

CoRe-Code: Collaborative Reinforcement Learning for Code Generation

arXiv:2605.24812v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance in code generation, but most methods rely on autoregressive decoding without global planni

local-aiarxiv-cs-ai

Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification

arXiv:2605.25592v1 Announce Type: cross Abstract: We study optimal experimental design for multinomial logit (MNL) bandits, where an agent repeatedly selects a subset of K items from a ground set of s

Safety

PID-Guided Partial Alignment for Multimodal Decentralized Federated Learning

arXiv:2601.10012v2 Announce Type: replace Abstract: Multimodal decentralized federated learning (DFL) must support collaboration among agents that hold different modality subsets and often different m

safetyarxiv-cs-lg

SkillEvolBench: Benchmarking the Evolution from Episodic Experience to Procedural Skills

arXiv:2605.24117v1 Announce Type: new Abstract: Large language model (LLM) agents accumulate rich episodic trajectories while solving real-world tasks, but it remains unclear whether such experience c

WorldGUI: An Interactive Benchmark for Desktop GUI Automation from Any Starting Point

arXiv:2502.08047v5 Announce Type: replace Abstract: Recent progress in GUI agents has substantially improved visual grounding, yet robust planning remains challenging, particularly when the environmen

Agentivism: a learning theory for the age of artificial intelligence

arXiv:2604.07813v2 Announce Type: replace Abstract: Learning theories have historically changed when the conditions of learning evolved. Generative and agentic AI create a new condition by allowing le

AI Assurance: A Comprehensive Testing Strategy for Enterprise AI Systems

arXiv:2605.23459v1 Announce Type: cross Abstract: Enterprise AI systems, built on large language models, retrieval pipelines and autonomous agents, introduce a class of risks that traditional software

Curriculum reinforcement learning with measurable task representation learning

arXiv:2605.23372v1 Announce Type: cross Abstract: In curriculum reinforcement learning (CRL), an agent incrementally accumulates knowledge over a sequence of tasks (i.e., a curriculum), and the learni

From Residuals to Reasons: LLM-Guided Mechanism Inference from Tabular Data

arXiv:2605.22897v1 Announce Type: new Abstract: A persistent challenge in machine learning for scientific applications is jointly achieving prediction and understanding. Statistical models excel on st

GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory

arXiv:2602.12316v2 Announce Type: replace Abstract: Frontier AI systems are increasingly capable and deployed in high-stakes multi-agent environments. However, existing AI safety benchmarks largely ev

Query-Adaptive Semantic Chunking for Retrieval-Augmented Generation: A Dynamic Strategy with Contextual Window Expansion

arXiv:2605.22834v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems depend critically on document chunking quality for retrieving relevant context. Fixed chunking segments doc

agentsarxiv-cs-cl

Turning Adaptation into Assets: Cross-Domain Bridging for Online Vision-Language Navigation

arXiv:2605.23257v1 Announce Type: cross Abstract: Navigating under non-stationary environment shifts poses a critical challenge for a Vision-and-Language Navigation (VLN) agent deployed in the wild. Y

Demo-JEPA: Joint-Embedding Predictive Architecture for One-shot Cross-Embodiment Imitation

arXiv:2605.20811v1 Announce Type: new Abstract: Robotic imitation learning is often treated as reproducing demonstrated actions, but actions are inherently embodiment-specific. When demonstrations com

agentsarxiv-cs-ro

21 May 2026

Emergence of a Flow-Assisted Casting Strategy for Olfactory Navigation via Memory-Augmented Reinforcement Learning

arXiv:2605.18881v1 Announce Type: new Abstract: In dynamic flow fields, various animals exhibit remarkable odor search capabilities despite relying on stochastic detections. Interestingly, there exist

Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization

arXiv:2605.18772v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) improves the factual accuracy of large language model (LLM) outputs by grounding generation in external knowledge

Probing Embodied LLMs: When Higher Observation Fidelity Hurts Problem Solving

arXiv:2605.20072v1 Announce Type: new Abstract: Large Language Models are increasingly proposed as cognitive components for robotic systems, yet their opaque decision processes make it difficult to ex

The Insurability Frontier of AI Risk: Mapping Threats to Affirmative Coverage, Silent Exposures, and Exclusions

arXiv:2605.18784v1 Announce Type: cross Abstract: The rapid diffusion of agentic AI has created a new coverage problem for commercial insurance: some AI-mediated losses are now affirmatively insured,

Safety

An Empirical Study of Privacy Leakage Chains via Prompt Injection in Black-Box Chatbot Environments

arXiv:2605.18133v1 Announce Type: cross Abstract: LLM-based chatbot agents increasingly process user requests by combining natural-language reasoning with external tools such as web browsing. These ca

safetyarxiv-cs-ai

Embodied Task Planning via Graph-Informed Action Generation with Large Language Models

model-releasesarxiv-cs-cl

arXiv:2601.21841v3 Announce Type: replace Abstract: While Large Language Models (LLMs) have demonstrated strong zero-shot reasoning capabilities, their deployment as embodied agents still faces fundam

Going Headless? On the Boundaries of Vertical AI Firms

arXiv:2605.17812v1 Announce Type: new Abstract: Vertical AI firms in accounting, law, healthcare, procurement, and similar domains historically bundled workflow, domain logic, and accountability into

Human-Flow Digital Twin for Predicting the Effects of Mobility Introduction on Visitor Circulation

arXiv:2605.17426v1 Announce Type: cross Abstract: We propose a framework for predicting the effects of mobility introduction measures using a human-flow digital twin. This digital twin incorporates a

JSON-Bag: A generic game trajectory representation

arXiv:2508.00712v2 Announce Type: replace-cross Abstract: We introduce JSON Bag-of-Tokens model (JSON-Bag) as a method to generically represent game trajectories by tokenizing their JSON descriptions

One Model to Translate Them All: Universal Any-to-Any Translation for Heterogeneous Collaborative Perception

arXiv:2605.17907v1 Announce Type: cross Abstract: By sharing intermediate features, collaborative perception extends each agent's sensing beyond standalone limits, but real-world feature modality hete

PopuLoRA: Co-Evolving LLM Populations for Reasoning Self-Play

arXiv:2605.16727v1 Announce Type: new Abstract: We introduce PopuLoRA, a population-based asymmetric self-play framework for reinforcement learning with verifiable rewards (RLVR) post-training of LLMs

Proof-Carrying Certificates for LLM Pipelines: A Trust-Boundary Architecture

arXiv:2605.16407v1 Announce Type: cross Abstract: We present a framework for verifying the deterministic structured computations surrounding a large language model rather than the model itself, extend

agentsarxiv-cs-cl

SWoMo: Neuro-Symbolic World Model for Cataract Surgery Simulation

arXiv:2605.16530v1 Announce Type: new Abstract: Realistic surgical simulation plays a crucial role in training novice surgeons and in the development of autonomous agents. World models can scale such

Towards Human-Level Book-Writing Capability

arXiv:2605.17064v1 Announce Type: new Abstract: Large language models optimized for instruction following and agentic tasks remain poorly aligned with the requirements of high-quality creative writing

Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery

arXiv:2605.15938v1 Announce Type: cross Abstract: Finding an odor source in a turbulent flow requires effectively leveraging the history of olfactory observations into a robust navigation strategy. In

18 May 2026

Do Biological Structural Guarantees Earn Their Complexity?

arXiv:2605.15225v1 Announce Type: cross Abstract: Biologically-inspired AI agent frameworks claim reliability benefits through structural guarantees adapted from gene regulatory networks, immune syste

18 May 2026

NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol

arXiv:2605.15227v1 Announce Type: new Abstract: Self-driving laboratories (SDLs) have attracted increasing attention as a means of accelerating scientific discovery; however, developing SDL software r

18 May 2026

Concurrency without Model Changes: Future-based Asynchronous Function Calling for LLMs

arXiv:2605.15077v1 Announce Type: cross Abstract: Function calling, also known as tool use, is a core capability of modern LLM agents but is typically constrained by synchronous execution semantics. U

Good to Go: The LOOP Skill Engine That Hits 99% Success and Slashes Token Usage by 99% via One-Shot Recording and Deterministic Replay

arXiv:2605.14237v1 Announce Type: new Abstract: Deploying AI agents for repetitive periodic tasks exposes a critical tension: Large Language Models (LLMs) offer unmatched flexibility in tool orchestra

Gradient Iterated Temporal-Difference Learning

arXiv:2603.07833v2 Announce Type: replace-cross Abstract: Temporal-difference (TD) learning is highly effective at controlling and evaluating an agent's long-term outcomes. Most approaches in this par

Model-Adaptive Tool Necessity Reveals the Knowing-Doing Gap in LLM Tool Use

arXiv:2605.14038v1 Announce Type: new Abstract: Large language models (LLMs) increasingly act as autonomous agents that must decide when to answer directly vs. when to invoke external tools. Prior wor

Modeling Bounded Rationality in Drug Shortage Pharmacists Using Attention-Guided Dynamic Decomposition

arXiv:2605.14111v1 Announce Type: new Abstract: Hospital pharmacists make high-stakes decisions to mitigate drug shortages under uncertainty, time pressure, and patient risk. Interviews revealed that

Why Goal-Conditioned Reinforcement Learning Works: Relation to Dual Control

arXiv:2512.06471v2 Announce Type: replace-cross Abstract: Goal-conditioned reinforcement learning (RL) concerns the problem of training an agent to maximize the probability of reaching target goal sta

Safety

Automated alignment is harder than you think

arXiv:2605.06390v2 Announce Type: replace Abstract: A leading proposal for aligning artificial superintelligence (ASI) is to use AI agents to automate an increasing fraction of alignment research as c

safetyarxiv-cs-ai

14 May 2026

EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering

arXiv:2602.05242v1 Announce Type: cross Abstract: Agentic Test-Time Scaling (TTS) has delivered state-of-the-art (SOTA) performance on complex software engineering tasks such as code generation and bu

14 May 2026

FIKA-Bench: From Fine-grained Recognition to Fine-Grained Knowledge Acquisition

arXiv:2605.13193v1 Announce Type: new Abstract: Fine-grained recognition in everyday life is often not a closed-book classification problem: when encountering unfamiliar objects, humans actively searc