Scaling Agentic Data for Long-Horizon Terminal Intelligence

  • 类型:arxiv
  • 标识:2610.13166
  • 链接:https://arxiv.org/abs/2610.13166
  • 主分类:agent
  • 形态:method
  • TLDR:Terminal agents are moving beyond local operations toward completing real-world professional tasks. Short trajectories can demonstrate commands and tool calls, but they do not fully show how agents accumulate evidence through exploration, use it to inform judgments, continually adjust strategies based on feedback, and turn intermediate results into final deliverables. High-quality long-horizon trajectories capture these agentic behavioral patterns and their connections within a single problem-solving process, providing richer experience for sustained autonomous work. However, environment synth
  • 待LLM分类:否
  • 来源文件:
  • /inbox/tom/_candidates/2026-10-13-agent-rag-longcontext-candidates.json