This research proposes an Agentic Neuro-Symbolic Framework that decouples semantic interpretation from geometric verification and establishes a scalable foundation for autonomous compliance, demonstrating that AI reliability in engineering significantly improves when probabilistic models orchestrate deterministic tools rather than predicting physical realities.
Abstract
The transition toward fully autonomous Digital Building Permitting (DBP) requires Automated Compliance Checking (ACC) systems to verify Building Information Models (BIM) against natural-language regulations. While Large Language Models (LLMs) offer strong semantic comprehension, integrating them into ACC introduces “Spatial Hallucinations” and “Serialization Bottlenecks” when processing high-dimensional BIM graphs. This research proposes an Agentic Neuro-Symbolic Framework that decouples semantic interpretation from geometric verification. Instead of relying on generative text for spatial reasoning, an Agentic LLM acts as a dynamic logic synthesizer orchestrating a deterministic geometry kernel (IfcOpenShell). The artifact was evaluated against the Australian National Construction Code (NCC 2022) across three stratified tiers: Semantic-Geometric Alignment, Multi-Parametric Dependencies, and Relational Topology Reasoning. Results demonstrate the framework autonomously resolves ontological ambiguity and synthesizes execution logic dynamically. By implementing connectivity graph traversal, the system isolates structural sub-graphs, reducing computational complexity from O(N) to O(K) and bypassing context-window limits. Offloading calculations to a deterministic environment achieves a highly deterministic accuracy rate for spatial queries, yielding immutable BIM Collaboration Format (.bcfzip) audit trails. Ultimately, this research establishes a scalable foundation for autonomous compliance, demonstrating that AI reliability in engineering significantly improves when probabilistic models orchestrate deterministic tools rather than predicting physical realities.
A principled, verifiable semantic communication method is developed using a random-support Dirichlet--Categorical model of inductive logical probability, providing a modern statistical reinterpretation of Carnap's and Hintikka's systems.
A neuro-symbolic framework that cleanly decouples reasoning into two formal dimensions: Symbolic Validity and Semantic Groundedness is proposed, which significantly improves reasoning reliability without the sprawling heuristics of prior frameworks.
Yuxin Zi, Cong Xu, Suparna Bhattacharya et al.· 0 citations
The Abstraction and Reasoning Corpus (ARC) benchmarks cognitive generalization, the ability to infer and apply abstract rules from limited examples. This paper presents a multi-stage rule-chaining framework that performs compositional reasoning across symbolic, structural, and conceptual levels. The framework integrates three complementary solvers: (1) a deterministic rule discovery module that induces atomic transformations through geometric, color, and object-based analysis; (2) a pattern-composition engine that reconstructs outputs via block merging, repetition, and spatial heuristics; and (3) a structural abstraction layer that infers hierarchical and nested relationships across grids. These solvers operate sequentially within a progressive fallback hierarchy, where each stage reuses prior reasoning traces to enhance interpretability and generalization. Training passed for 995 tasks out of 1000, further evaluated on 105 tasks out of 120 and solved 230 test tasks out of 240 ARC-AGI-2 tasks. The system achieved strong coverage across deterministic, compositional, and abstract categories, demonstrating an overall accuracy exceeding 95 percent. The proposed architecture bridges symbolic reasoning and pattern synthesis, providing interpretable insight into cognitive generalization. The results suggest that rule chaining and hierarchical composition can advance machine reasoning toward transparent, human-aligned abstraction without relying on task-specific tuning.
Experiments reveal that models with similar end-to-end accuracy can exhibit markedly different agentic capability profiles, demonstrating that process-level evaluation is crucial for interpreting the true potential of LLMs and guiding the development of next-generation mathematical agents.
Jiayi Kuang, Yinghui Li, Yun-Ze Song et al.· 0 citations
: The design and specification of experiments in Model-Based Systems Engineering is challenging: state-of-the-art tools are deemed either precise, but too cumbersome or too imprecise due to natural-language descriptions that lack formal semantics. This is compounded by the high complexity of systems, especially in safety-critical domains. Large Language Models (LLMs) offer a promising avenue for automating the elicitation step, but their probabilistic nature precludes unmediated use: hallucinations cannot be allowed to propagate into formal artifacts. We propose a neuro-symbolic framework combining LLM-driven elicitation constrained by a rule-based reasoner fed by an ontology-compliant knowledge graph. A deterministic orchestrator drives an elicitation loop where the symbolic engine poses context-sensitive questions, the LLM proposes candidate answers, and every candidate is validated against formal domain constraints before acceptance. We present a proof-of-concept implementing the proposed framework and an empirical evaluation across three case studies using four state-of-the-art LLMs. Results indicate that the framework reliably prevents hallucinations from propagating into formal specifications.
Diego Ferreira, Rakshit Mittal, Lucas Lima et al.· International Conference on...· 0 citations
A Neuro-Symbolic architecture that integrates a Logical Knowledge Graph (LKG) with dynamic solver routing, and introduces an ontology-based LKG that treats logical rules and constraints as first-class topological nodes, enabling explicit modeling of dependencies extracted from text.
Hai-Zhao Fan, Yu-Chi Xiong, Jize Wang et al.· 0 citations
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