用可执行的契约框架，让大模型系统更稳定、可控且省钱。
PARCER as an Operational Contract to Reduce Variance, Cost, and Risk in LLM Systems
- 用YAML声明式契约规范交互流程，实现版本化管理。
- 通过自适应令牌预算和容错机制，降低延迟与成本超支风险。
- 适合需高一致性、可审计的科研与工程级LLM应用。
基于大语言模型（LLMs）的系统已成为自动化研究与软件生成的强大工具，但在需要绝对一致性、可审计性及成本与延迟可预测控制时，其治理仍面临挑战。现有文献指出两个加剧该问题的现象：模型判断中的固有随机性（常被视为“系统噪声”）以及长输入下上下文利用率的显著下降，尤其当关键信息被夹在提示中间时会出现严重丢失。本文提出PARCER作为工程应对方案，其作为一个声明式的“操作契约”（YAML格式），将非结构化交互转化为可版本化、可执行的实体。PARCER引入七阶段严格治理流程，借鉴法律判决中的决策卫生实践，实现降噪；通过自适应令牌预算、正式化的恢复路径（回退机制）保障上下文完整性，并利用OpenTelemetry实现系统可观测性。本工作旨在呈现PARCER的概念与技术架构，推动从简单的“提示工程”向‘可治理的上下文工程’转变。
原文摘要 · Abstract (English)
Systems based on Large Language Models (LLMs) have become formidable tools for automating research and software production. However, their governance remains a challenge when technical requirements demand absolute consistency, auditability, and predictable control over cost and latency. Recent literature highlights two phenomena that aggravate this scenario: the stochastic variance inherent in the model's judgment (often treated as "systemic noise") and the substantial degradation of context utilization in long inputs, with critical losses when decisive information is diluted in the middle of the prompt. This article proposes PARCER as an engineering response to these limitations. The framework acts as a declarative "operational contract" in YAML, transforming unstructured interactions into versioned and executable artifacts. PARCER imposes strict governance structured into seven operational phases, introducing decision hygiene practices inspired by legal judgments to mitigate noise, adaptive token budgeting, formalized recovery routes (fallbacks) for context preservation, and systemic observability via OpenTelemetry. The objective of this work is to present the conceptual and technical architecture of PARCER, positioning it as a necessary transition from simple "prompt engineering" to "context engineering with governable governance".
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