Parsing the Stream: A Live Trace Model for Long-Horizon Agents and Their Observers· 解析流：长视界代理及其观察者的实时追踪模型
A long-horizon agent's trace outgrows both of its consumers: the human observer monitoring the run, and the agent itself, whose bounded context the trace must be folded back into. We present a live trace model, an append-only event ledger folded incrementally into typed run state and compiled into per-consumer views, and evaluate it for both consumers against deterministic ground truth. For the observer side, evaluated with an LLM reader as proxy, the compiled view answers monitoring questions using approximately 14x and 15x fewer input tokens (by reader) and at 5-7x lower cost than a budget-capped single-call reading of the raw trace, with higher accuracy (0.85-0.87 versus 0.48). Because the questions were co-designed with the view schema, we treat the token and cost reduction, conditiona
提出一种实时追踪模型，优化长视界代理的可观测性和资源效率。
- 核心方法
- 构建实时追踪模型，通过递增折叠事件日志为类型化的运行状态，并编译成每个消费者的视图。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 长视界代理的追踪数据超出人类观察者和代理自身处理能力，导致资源浪费和监测不准。
- 关键实验
- 使用限定预算的单次调用读取原始追踪数据与LLM读者代理进行对比，显示了该模型在减少输入标记和成本方面的优势。
- 主要贡献
- 减少了追踪数据处理所需的输入标记和成本，提高了监测准确性，特别适用于资源有限的环境。
- 意义与局限
- 该模型提高了长视界代理的监测效率和准确性，但可能需要针对不同任务定制视图模式。