AI Agents & Automation

arXiv:2607.08894v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like

arXiv:2607.08964v1 Announce Type: new Abstract: AI agents have become capable of autonomously completing short, well-specified tasks. However, existing terminal

arXiv:2607.09059v1 Announce Type: new Abstract: We present ARCANA, a collaborative multi agent framework for solving ARC AGI 2 tasks under strict test time and

arXiv:2607.09099v1 Announce Type: new Abstract: While multi-agent debate (MAD) frameworks have shown significant potential in general reasoning, their effective

arXiv:2607.09175v1 Announce Type: new Abstract: Deployed LLM agents rely on agentic context, the model-external textual control content assembled by an operatio

arXiv:2607.09217v1 Announce Type: new Abstract: In this system paper, we present OpenProver, an open-source system for LLM-driven automated theorem proving (ATP

arXiv:2607.09322v1 Announce Type: new Abstract: In this work, we introduce LongMedBench, a real-world EHR-based benchmark for long-horizon clinical decision-mak

arXiv:2607.09493v1 Announce Type: new Abstract: Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each sess

arXiv:2607.09521v1 Announce Type: new Abstract: Does every cancer patient truly need a complete diagnostic workup for accurate survival prediction? In multimoda

arXiv:2607.09586v1 Announce Type: new Abstract: The proliferation of agentic AI systems across enterprise and public-sector contexts has outpaced the capacity o

arXiv:2607.08782v1 Announce Type: cross Abstract: Expert parallelism has become the prevailing paradigm to serve Mixture-of-Experts (MoE) models. Its efficiency

arXiv:2607.07721v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) and agentic frameworks have advanced enterprise AI considerably, yet agents

arXiv:2607.07759v1 Announce Type: new Abstract: Agricultural supply chains are vulnerable to disruptions through linked biophysical and economic systems. We dev

arXiv:2607.07775v1 Announce Type: new Abstract: The human body is at the center of a growing family of technologies designed to tightly and persistently couple

arXiv:2607.07859v1 Announce Type: new Abstract: Reinforcement learning (RL) research has increasingly shifted focus towards alignment, ensuring agents learn beh

arXiv:2607.07984v1 Announce Type: new Abstract: Neural architecture search (NAS) methods have grown increasingly efficient, yet they remain bounded by manually

arXiv:2607.08177v1 Announce Type: new Abstract: In this paper, we study the automatic schema generation problem: given a collection of historical ship maintenan

arXiv:2607.08233v1 Announce Type: new Abstract: A central challenge in building intelligent systems is enabling agents to jointly perceive complex inputs, form

arXiv:2607.08252v1 Announce Type: new Abstract: Long-term persona agents must remain identifiable while adapting to new events, relationships, evidence, and soc

arXiv:2607.06624v1 Announce Type: new Abstract: We present AgentLens, a production-assessed benchmark for interactive code agents. Most code-agent benchmarks re

arXiv:2607.06760v1 Announce Type: new Abstract: Autonomous systems under partial observability act on beliefs, not raw sensor events. QANTIS treats the quantum

arXiv:2607.06764v1 Announce Type: new Abstract: Recent progress on ARC-AGI-1 from disclosed architectures has come broadly from two regimes: heavy test-time com

arXiv:2607.06906v1 Announce Type: new Abstract: Agentic AI development today runs on token maxing: buying capability with tokens -- longer reasoning traces, mor

arXiv:2607.07021v1 Announce Type: new Abstract: Humans continuously coordinate with others in dynamic interactions, often through implicit, hard-to-quantify soc

arXiv:2607.07097v1 Announce Type: new Abstract: Safety evaluations of multi-agent LLM systems often compare a direct prompt with a planner-executor pipeline and

arXiv:2607.07189v1 Announce Type: new Abstract: Vision-language models (VLMs) and agentic AI have shown strong performance on semantic visual tasks, but it rema

arXiv:2607.07379v1 Announce Type: new Abstract: In agentic scientific machine learning (SciML), large language model (LLM) agents can discover surrogate models

arXiv:2607.07397v1 Announce Type: new Abstract: Autonomous agents promise substantial gains in speed, scale, and labor efficiency, but their failures can impose

arXiv:2607.07436v1 Announce Type: new Abstract: A self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see th

arXiv:2607.07492v1 Announce Type: new Abstract: Many reasoning tasks are not well described by a single left-to-right chain: a solver may need to pursue a plaus

arXiv:2607.07676v1 Announce Type: new Abstract: Autonomous AI agents can execute complex tasks with limited human review, yet they often lack the grounded opera

arXiv:2607.07695v1 Announce Type: new Abstract: We introduce institutional red-teaming, an evaluation methodology for testing deployment rules in multi-agent AI

arXiv:2607.06595v1 Announce Type: cross Abstract: Personal AI agents powered by large language models can reason and act using available tools to access emails,

arXiv:2607.06608v1 Announce Type: cross Abstract: We present key challenges and future research directions in the security and privacy of agentic AI, based on a

arXiv:2607.06701v1 Announce Type: cross Abstract: Interactive simulators have become powerful tools for training embodied agents and generating synthetic visual

arXiv:2607.06713v1 Announce Type: cross Abstract: Large language models are rapidly moving towards closing the development cycle, transitioning from simple assi

arXiv:2607.06740v1 Announce Type: cross Abstract: Soft robots have attracted significant attention in applications such as medical intervention, rehabilitation,

arXiv:2607.06786v1 Announce Type: cross Abstract: Standards bodies, including TM Forum, 3GPP, and ETSI, are converging on Agentic AI as the foundation for next-

arXiv:2607.06807v1 Announce Type: cross Abstract: While enabling effective collaboration on complex tasks, LLM-based Multi-Agent Systems (MAS) face critical sec

arXiv:2607.06854v1 Announce Type: cross Abstract: Reinforcement learning agents for imperfect-information card games are only as strong as the opponents they tr

arXiv:2607.06893v1 Announce Type: cross Abstract: The Stochastic-Oracle Turing Machine (SOTM) framework models AI-augmented computation as the interaction of a

arXiv:2607.06988v1 Announce Type: cross Abstract: Steering robot foundation models (RFMs) toward new task variants or user-preferred behaviors remains challengi

arXiv:2607.07052v1 Announce Type: cross Abstract: AI agents deployed for IT operations are typically permanent cost centers because every execution requires ful

arXiv:2607.07101v1 Announce Type: cross Abstract: Proprioception is fundamental to robotic manipulation, yet standard fusion methods often treat it as an isolat

arXiv:2607.07178v1 Announce Type: cross Abstract: Recent breakthroughs of Reinforcement Learning (RL) have highlighted its potential for complex agentic Large L

arXiv:2607.07357v1 Announce Type: cross Abstract: Effective social robot navigation requires sensitivity to human behavior, often revealed through subtle skelet

arXiv:2607.07368v1 Announce Type: cross Abstract: AI control is a family of techniques to prevent an AI with malicious goals from subverting its operator's inte

arXiv:2607.07370v1 Announce Type: cross Abstract: In embodied intelligence systems, the motion controller serves as the critical bridge between semantic reasoni