PYTHALAB-MERA：基于验证的内存、检索与接受控制用于冻结LLM编码代理
PYTHALAB-MERA: Validation-Grounded Memory, Retrieval, and Acceptance Control for Frozen-LLM Coding Agents
AI总结：
PYTHALAB-MERA通过轻量级外部控制器实现验证基础的内存管理、适应性检索与延迟信用分配，提升冻结本地模型在编码任务中的验证成功率。
摘要：
本地基于LLM的编码代理越来越多地在需要通过执行反馈、持久状态和有限修复来获得正确性的环境中工作，而非单一流畅回答。静态检索、长上下文提示、自我细化、执行反馈修复和模型权重强化学习各自解决该环境的一部分，但它们并未共同提供基于验证的事件性记忆、适应性检索-动作选择、延迟信用分配以及围绕冻结本地模型的结构化技能重用。我们引入了PYTHALAB-MERA，一个轻量级的外部控制器用于本地验证条件下的代码生成。冻结的语言模型生成完整的源文件；控制器决定哪些内存记录和AST衍生技能应进入下一个提示，通过快速失败管道验证每个候选项，将验证结果转换为有界形状的奖励，并通过TD(lambda)风格的资格痕迹传播延迟信用。我们评估了该实现作为本地CLI制品，在具有严格验证门控的强化学习编码任务中的表现。在测量的严格RL环境中，三个任务、三次重复和三次尝试预算下，PYTHALAB-MERA通过了8/9严格的验证；自我细化基线和调查的GRACE扩展各通过了0/9。这些结果支持一个刻意限定的声明：在该记录环境中，外部内存-检索控制器提高了验证成功率。它们不建立通用代码合成、最先进性能、正式程序正确性或正式安全性。
英文摘要：
Local LLM-based coding agents increasingly work in settings where correctness is earned through execution feedback, persistent state, and bounded repair, not through a single fluent answer. Static retrieval, long-context prompting, self-refinement, execution-feedback repair, and reinforcement learning over model weights each address part of this setting, but they do not jointly provide validation-grounded episodic memory, adaptive retrieval-action selection, delayed credit assignment, and structural skill reuse around a frozen local model. We introduce PYTHALAB-MERA, a lightweight external controller for local validation-conditioned code generation. The frozen language model proposes complete source files; the controller decides which memory records and AST-derived skills should enter the next prompt, validates each candidate through a fail-fast pipeline, converts validation outcomes into bounded shaped rewards, and propagates delayed credit through TD(lambda)-style eligibility traces. We evaluate the implementation as a local CLI artifact on reinforcement-learning coding tasks with strict validation gates. In the measured hard RL setting with three tasks, three repetitions, and a three-attempt budget, PYTHALAB-MERA passed 8/9 strict validations; the self-refinement baseline and the investigated GRACE extension each passed 0/9. These results support a deliberately bounded claim: in this recorded setting, the external memory-and-retrieval controller improved validation success. They do not establish general-purpose code synthesis, state-of-the-art performance, formal program correctness, or formal safety.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
