用神经符号因果架构让AI agent在电商中稳定赚钱，不被提示词忽悠。
Beyond Prompt Engineering: Neuro-Symbolic-Causal Architecture for Robust Multi-Objective AI Agents
- 融合大模型、符号约束和因果推理，构建抗干扰决策系统。
- 相比纯大模型，利润最高提升220万美金，品牌信任增20.86%。
- 适合高风险场景的AI部署，如金融、医疗、电商自动化。
大语言模型作为自主决策代理展现出潜力，但在高风险领域部署仍存巨大风险。缺乏架构保障时，大模型代理表现出灾难性脆弱：相同能力因提示词不同而结果迥异。本文提出Chimera神经符号因果架构，集成三个互补组件——大模型策略器、形式化验证的符号约束引擎、以及用于反事实推理的因果推断模块。我们在包含价格弹性、信任动态和季节性需求的真实电商环境中，对Chimera与基线架构（纯大模型、大模型加符号约束）进行52周仿真对比。在以销量或利润为目标的组织偏见下，纯大模型代理失败严重（销量场景总损失9.9万美元），或破坏品牌信任（利润场景下降48.6%）。加入符号约束可避免灾难，但利润仅达Chimera的43%-87%。Chimera持续实现最高收益（分别为152万美元和196万美元，某些情况增加220万美元），同时提升品牌信任（分别+1.8%和+10.8%，某些情况+20.86%），展示出对提示词无关的鲁棒性。我们的TLA+形式化验证证明所有场景下无约束违规。结果表明，决定自主代理可靠性的关键在于架构设计而非提示工程。我们开源代码与交互演示以支持复现。
原文摘要 · Abstract (English)
Large language models show promise as autonomous decision-making agents, yet their deployment in high-stakes domains remains fraught with risk. Without architectural safeguards, LLM agents exhibit catastrophic brittleness: identical capabilities produce wildly different outcomes depending solely on prompt framing. We present Chimera, a neuro-symbolic-causal architecture that integrates three complementary components - an LLM strategist, a formally verified symbolic constraint engine, and a causal inference module for counterfactual reasoning. We benchmark Chimera against baseline architectures (LLM-only, LLM with symbolic constraints) across 52-week simulations in a realistic e-commerce environment featuring price elasticity, trust dynamics, and seasonal demand. Under organizational biases toward either volume or margin optimization, LLM-only agents fail catastrophically (total loss of \$99K in volume scenarios) or destroy brand trust (-48.6% in margin scenarios). Adding symbolic constraints prevents disasters but achieves only 43-87% of Chimera's profit. Chimera consistently delivers the highest returns (\$1.52M and \$1.96M respectively, some cases +\$2.2M) while improving brand trust (+1.8% and +10.8%, some cases +20.86%), demonstrating prompt-agnostic robustness. Our TLA+ formal verification proves zero constraint violations across all scenarios. These results establish that architectural design not prompt engineering determines the reliability of autonomous agents in production environments. We provide open-source implementations and interactive demonstrations for reproducibility.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。