The paper examines how users delegate tasks to the AI agent OpenClaw by analyzing 73,093 Reddit posts. It identifies 21 human values grouped into six categories—such as Autonomous Operation, Dependable Operation, Affordable Operation, Bounded Reach, Reviewability, and Equitable Access—and finds that values are largely satisfied when users describe the agent’s outputs but often unmet when users discuss supervising the agent. The authors term this pattern "value‑sensitive delegation," emphasizing that supporting human values requires attention to both what an agent does and the conditions users set around its use.
By Renkai Ma, Ruyuan Wan, Xuan Lu, Fan Yang, Chen Chen, Lingyao Li
AI agents are increasingly autonomous, posing significant risks that current designs hinder effective human oversight. The paper argues that oversight is degraded by both design choices and the cognitive decline of users who rely heavily on automation. It calls for prioritizing human cognitive needs in AI agent development, proposing design affordances and protocols to maintain critical judgment and counter skill atrophy.
By Margaret Mitchell, Avijit Ghosh, Samir Passi
arXiv:2609.07741v1 Announce Type: new
Abstract: Persistent AI assistants are intended to extend human attention, memory, and coordination across changing digital and physical environments. To be trul...
By Jo\~ao Dias Ferreira
arXiv:2606. 04321v1 Announce Type: new Abstract: Agentic AI deployments face a recurring design tension: heavy human oversight limits scale, while broad autonomy outruns accountability.
By Travis Weber, Rohit Taneja
The paper introduces Goal-Aware Dynamic Multi-Agent Collaboration (GAD), a framework that assigns distinct duties to separate agents with individualized contexts to tackle long-horizon tasks. It employs a growing execution graph to structure subtasks and their dependencies, ensuring each agent retains the original instruction, task goal, and acceptance conditions. GAD dynamically adapts collaboration and information sharing, using verification and recovery checks to monitor subtask results and overall task completion, providing feedback for repair when checks fail.
By Xu Shen, Lingyong Yan, Shuaiqiang Wang, Jianmin Wu, Zhikai Chen, Jiliang Tang, Jie Ren
The paper introduces Agent-Integrated Software (AIS), a pattern that embeds an intelligent agent within an existing application to address persistent coordination challenges. It defines Intent-Level Interaction Abstraction (IIA) as the task semantics for user inspection and control, and presents interaction contracts that constrain the relationship between AIS execution and IIA states. The authors provide a transition-system model, a compact disclosure contract, and conditional propositions to illustrate how local component validity is insufficient and how admission invariants can be separated from planning, concluding with a research agenda for making agent integration a maintainable software engineering discipline.
By Shengcheng Yu, Chunrong Fang, Zhenyu Chen
The paper investigates how large language model agents handle long‑horizon tasks and finds that verification is crucial for success but often occurs too late. It shows that sharing a common context dilutes duty‑relevant information and that simply separating duties among agents is insufficient, as agents can lose sight of the overall goal. To address these issues, the authors propose Goal‑Aware Dynamic Multi‑Agent Collaboration (GAD), which uses an execution graph to organize subtasks, keeps each agent’s context focused on its specific goal, and dynamically adjusts task dependencies and information sharing based on execution evidence, with verification and recovery checks providing feedback for repair.
arXiv:2607. 10878v1 Announce Type: new Abstract: AI agents are evolving from answer engines into persistent teams that use tools, delegate work, learn from experience, and modify the artifacts that shape their future behavior.
By Yuma Ichikawa, Yamato Arai, Kosaku Kimura, Akira Sakai, Hiromichi Kobashi
arXiv:2608.29596v1 Announce Type: new
Abstract: Autonomous large language model (LLM) agents increasingly face reliability, context consumption, and execution stability bottlenecks when deployed on c...
By Sanket Badhe, Deep Shah, Priyanka Tiwari, Nehal Kathrotia
The paper proposes a framework called cooperative observation for personal AI systems, arguing that the system’s ability to model a user’s goals and constraints is limited by what it can observe. It emphasizes that merely increasing observation does not improve assistance; instead, the system must selectively compress information for the task at hand. The authors describe a feedback loop where the system’s usefulness, user trust, and consent shape future observation, and present a preliminary single‑subject study with a prototype called Organizm.
By Yashar Talebirad, Osman Jime, Ali Parsaee, Eden Redman, Yongbin Kim, Osmar R. Zaiane
arXiv:2608. 12355v1 Announce Type: cross Abstract: Recent progress in AI coding agent research has led to rapid improvements in agents' ability to autonomously perform complex software engineering tasks, from editing large codebases to executing long-horizon development workflows.
By Zora Z. Wang, John Yang, Kilian Lieret, Alexa Tartaglini, Valerie Chen, Yuxiang Wei, Zijian Wang, Lingming Zhang, Karthik Narasimhan, Ludwig Schmidt, Graham Neubig, Daniel Fried, Diyi Yang
arXiv:2607. 16530v1 Announce Type: new Abstract: As generative AI is increasingly applied to automate multi-step and high-stake workflows, human judgment and involvement remain essential for ensuring the quality of AI-generated outputs.
By An Luo, Jie Ding