PRECEPT：通过经验、上下文工程与轨迹探测进行规划韧性 一个具有组合规则学习和帕累托引导提示演化的测试时间适应统一框架
PRECEPT: Planning Resilience via Experience, Context Engineering & Probing Trajectories A Unified Framework for Test-Time Adaptation with Compositional Rule Learning and Pareto-Guided Prompt Evolution
AI总结：
PRECEPT通过经验、上下文工程与轨迹探测，结合组合规则学习和帕累托引导提示演化，提升测试时间适应的鲁棒性和泛化能力。
摘要：
LLM代理在将知识存储为自然语言时，随着条件数量的增长，检索退化会变得陡峭，通常难以可靠地组合已学习的规则，并且通常缺乏明确的机制来检测过时或对抗性的知识。我们引入了PRECEPT，一个统一的测试时间适应框架，包含三个紧密耦合的组件：（1）基于结构化条件键的确定性精确匹配规则检索；（2）具有贝叶斯来源可靠性和基于阈值的规则无效化的冲突感知记忆；（3）COMPASS，一个帕累托引导的提示演化外循环。精确检索消除了确定性路径上的部分匹配解释错误（构造中为0%，在定理B.6的独立模型下N=10时为94.4%），并通过语义层级支持组合堆叠；冲突感知记忆解决静态-动态分歧并支持漂移适应；COMPASS通过相同的端到端执行管道评估提示。结果（9-10种子）：PRECEPT在Full Reflexion上实现了+41.1pp的一次尝试优势（d>1.9），+33.3pp的组合泛化能力（d=1.55），在2-way物流组合上实现了100%的P1（d=2.64），+40-55pp的连续学习收益，对抗性静态知识下的强最终鲁棒性（100%物流对抗性SK活跃；部分恢复于整合），+55.0pp的漂移恢复（d=0.95，p=0.031），以及61%更少的步骤。核心比较在统计上显著，通常在p<0.001。
英文摘要：
LLM agents that store knowledge as natural language suffer steep retrieval degradation as condition count grows, often struggle to compose learned rules reliably, and typically lack explicit mechanisms to detect stale or adversarial knowledge. We introduce PRECEPT, a unified framework for test-time adaptation with three tightly coupled components: (1) deterministic exact-match rule retrieval over structured condition keys, (2) conflict-aware memory with Bayesian source reliability and threshold-based rule invalidation, and (3) COMPASS, a Pareto-guided prompt-evolution outer loop. Exact retrieval eliminates partial-match interpretation errors on the deterministic path (0% by construction, vs 94.4% under Theorem~B.6's independence model at N=10) and supports compositional stacking through a semantic tier hierarchy; conflict-aware memory resolves static--dynamic disagreements and supports drift adaptation; COMPASS evaluates prompts through the same end-to-end execution pipeline. Results (9--10 seeds): PRECEPT achieves a +41.1pp first-try advantage over Full Reflexion (d>1.9), +33.3pp compositional generalization (d=1.55), 100% $P_1$ on 2-way logistics compositions (d=2.64), +40--55pp continuous learning gains, strong eventual robustness under adversarial static knowledge (100% logistics with adversarial SK active; partial recovery on integration), +55.0pp drift recovery (d=0.95, p=0.031), and 61% fewer steps. Core comparisons are statistically significant, often at p<0.001.
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。
