Neuro-Symbolic Task Decomposition with High-Level Planning Guidance for Autonomous LLM Problem Solving
Keywords:
neuro-symbolic AI, task decomposition, high-level planning, large language models, autonomous reasoning, system architecture, governanceAbstract
Large language models have demonstrated remarkable fluency and broad knowledge, yet they often struggle with multi-step reasoning tasks that require structured decomposition, causal understanding, and verifiable correctness. This paper investigates a neuro-symbolic framework that combines the flexible pattern recognition of LLMs with the formal rigor of symbolic task decomposition, guided by high-level planning mechanisms. We propose an architecture in which an LLM generates candidate subgoals and intermediate representations, while a symbolic planner imposes hierarchical constraints and ensures logical consistency. High-level planning guidance, derived from reinforcement learning or causal inference, provides abstract goal structures that reduce the search space and mitigate hallucinations. We examine system-level trade-offs including computational overhead, interpretability, and robustness across domains such as software engineering, scientific reasoning, and autonomous decision-making. Infrastructure considerations for deployment in production environments are discussed, along with governance challenges related to fairness, transparency, and accountability. The paper further explores sustainability issues, particularly the energy cost of iterative LLM calls and potential optimizations through hybrid execution. By synthesizing insights from neuro-symbolic artificial intelligence, planning theory, and large-scale system design, we argue that the integration of high-level planning guidance into LLM-based problem solving offers a principled path toward more reliable, transparent, and scalable autonomous agents. Empirical illustrations and cross-domain comparisons support the feasibility and limitations of the proposed approach.
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