DCAS：解耦CLI代理脚手架以跨支架內化規劃能力

摘要

基於 CLI 的軟體工程代理已迅速成熟，然而開源生態系卻收斂於單一訓練環境：用於微調開源模型的軌跡資料集幾乎完全在 OpenHands 環境下收集。在此資料上微調的模型在 OpenHands 下表現良好，但在任何非訓練框架下部署時，效能會大幅下降。未經訓練的基礎模型並未展現此差異，表明該差距是由微調所誘發，且與訓練框架的慣例密切相關。我們主張，一項具承重作用的框架特定行為是規劃結構——此處以本文所區分的兩種意義來理解：顯式規劃，即作為一級產物在執行前產生的計畫；以及隱式規劃，即在整個代理迴圈中塑造執行的結構性慣例。在此假設下，縮小差距需要將規劃從固定的框架產物轉變為學習到的模型能力。我們引入解耦 CLI 代理框架（DCAS），這是一個後端替換攔截層，可在不修改框架的情況下，於任何 CLI 框架與任何後端模型之間路由 API 流量，從而實現跨框架評估與具規劃意識的軌跡收集。利用 DCAS，一項受控的規劃來源干預證實，規劃品質是高槓桿的組成部分，其收益超過我們觀察到的跨框架下降幅度。在單一框架下，於少量 DCAS 收集的具規劃意識軌跡上微調的模型，能在非訓練框架上持續獲得增益，且兩種規劃意義在訓練資料中可經驗性地分離。

English

CLI-based software-engineering agents have matured rapidly, yet the open ecosystem has converged on a single training environment: trajectory datasets used to fine-tune open models are collected almost exclusively under OpenHands. Models fine-tuned on this data score well under OpenHands but degrade substantially when deployed under any non-training scaffold. Untrained base models do not show this divergence, indicating the gap is fine-tuning-induced and tied to the conventions of the training scaffold. We argue that a load-bearing scaffold-specific behavior is planning structure, in two senses this paper distinguishes: explicit planning, a pre-execution plan produced as a first-class artifact, and implicit planning, the structural conventions that shape execution throughout the agent loop. Under this hypothesis, closing the gap requires moving planning from a fixed scaffold artifact to a learned model capability. We introduce Decoupling CLI Agent Scaffolding (DCAS), a backend-substitution interception layer that routes API traffic between any CLI scaffold and any backend model without modifying the scaffold, enabling cross-scaffold evaluation and planning-aware trajectory collection. Using DCAS, a controlled plan-source intervention confirms planning quality is a high-leverage component, with gains exceeding the cross-scaffold drops we observe. A model fine-tuned on a small set of DCAS-collected planning-aware trajectories under a single scaffold gains consistently across non-training scaffolds, and the two senses of planning are empirically separable in training data.