Reproduce “View-oriented Conversation Compiler for Agent Trace Analysis” on OpenResearch
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About this paper
Lvmin Zhang, Maneesh Agrawala
The View-oriented Conversation Compiler (VCC) transforms raw agent execution traces into structured, view-specific outputs, enhancing Large Language Model (LLM) reflector agent performance and efficiency. This system improves task goal completion rates by 1.1 to 4.2 percentage points, reduces reflector token consumption by 46-65%, and yields smaller, more focused learned memory files.
Key insights
- The format and structured presentation of agent execution traces are critical variables in context engineering, significantly influencing LLM agent learning efficiency and overall task goal completion rates.
- Providing agents with semantically organized and concise views of their operational history enables more effective analysis, rule extraction, and self-improvement mechanisms.
- A progressive disclosure workflow, utilizing summarized views linked to full details by pointers, demonstrably reduces redundant token processing and improves an agent's ability to filter relevant information.
Summary by alphaXiv.
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