PILOT in the Loop: Live Self-Improvement for Long-Horizon Agents

arXiv:2608.26530 · cs.AI · Submitted 2026-08-27 · Read on arXiv

cs.AI

Submitted: 2026-08-27

Updated: 2026-08-27

Code: https://github.com/XiaoYang66/Pilot

Terminology

• Steering — 35× in this paper · explained in 2 episodes
• Benchmark — 23× in this paper · explained in 2 episodes
• Trace — 7× in this paper · explained in 2 episodes
• Failure Modes — 5× in this paper · explained in 3 episodes
• Frozen model — 4× in this paper · explained in 3 episodes
• Agent Harness — 3× in this paper · explained in 2 episodes
• Benchmarking — 3× in this paper · explained in 4 episodes
• Agent Architectures — 2× in this paper · explained in 1 episode
• Generalization — 2× in this paper · explained in 13 episodes
• Multi-Agent System — 2× in this paper · explained in 6 episodes
• Self-Evolving Agents — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Multi-agent Collaborative Framework — 1× in this paper · explained in 2 episodes
• Online Adaptation — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes

Sources

• EvoSkill: Automated Skill Discovery for Multi-Agent Systems
• SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
• Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
• Continual Harness: Online Adaptation for Self-Improving Foundation Agents
• Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses
• Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems
• AutoHarness: improving LLM agents by automatically synthesizing a code harness
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• Group-Evolving Agents: Open-Ended Self-Improvement via Experience Sharing
• Self-Harness: Harnesses That Improve Themselves
• Memento-Skills: Let Agents Design Agents
• EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments

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