AI 中文总结
研究并行编码代理变更问题，提出声明平面架构，工作者声明变更意图，控制平面管理，执行时处理突变，通过CooperBench机制检查展示可行性，为未来模型升级提供基础。
AI 中文摘要
并行编码代理在集成时可能会相互干扰，超出计划范围或依赖无效前提。现有应对措施包括强调通信、隔离工作区等。本文提出声明平面，一种与模型无关的协调架构，将并发软件变更视为预写准入问题。工作者在实现前声明版本化变更意图，控制平面原子地接纳兼容意图，执行期间或有突变不保留写所有权，首次尝试触发范围提升和重新接纳。初步的CooperBench机制检查表明静态声明平面全序列化时6/6对通过，动态范围一半对保留并行接纳，成功进行7次范围提升，2次未声明突变失败关闭。我们认为将概率规划与确定性权限分离可为未来基于学习的语义依赖模型和仅在未解决情况上的前沿模型升级提供基础。
英文摘要
Parallel coding agents can independently produce locally valid changes while still interfering at integration time, expanding beyond planned scope, or relying on premises invalidated by concurrent work. Existing responses emphasize communication, isolated workspaces, late merge-time repair, continuous supervision, or post-hoc runtime recovery. This paper presents Claim Plane, a model-agnostic coordination architecture that treats concurrent software change as a pre-write admission problem. Before implementation, each worker declares a versioned ChangeIntent containing an exact base commit, typed resources, dependencies, and operations marked as committed or contingent. A deterministic control plane atomically admits compatible intents, constrains same-file parallelism to declared regions, serializes unresolved overlap, tracks dependency invalidation, and fails closed on ambiguous authority. During execution, a contingent mutation does not reserve write ownership initially; the first attempted mutation triggers atomic scope promotion and re-admission against the current active set. Brokered execution binds capabilities to intent versions, leases, OS-level worktree locks, monotonic fencing tokens, and Git-tree provenance, while integration verifies immutable patches and evidence. A preliminary six-pair CooperBench mechanism check is reported only as feasibility evidence: static Claim Plane achieved 6/6 pair passes with full serialization, while dynamic scope retained parallel admission on half of the pairs, performed seven successful scope promotions, and failed closed on two undeclared mutations. The sample is intentionally too small for comparative claims. We argue that separating probabilistic planning from deterministic authority provides a foundation for a future learned semantic-dependency model and frontier-model escalation only on unresolved cases.
Comments10 pages, 2 figures. Preprint
