FORGE：面向故障的互锁装配可执行返工代码生成

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Abstract:Large language models have demonstrated strong capabilities in structured reasoning and code generation, yet identifying an assembly fault does not guarantee the ability to generate executable rework programs. In interlocking assemblies, a locally plausible repair program may violate blocking dependencies, omit required recovery operations, or terminate in an incorrect configuration. We identify this challenge as fault-conditioned executable rework generation, where models must jointly reason about structural failures and generate programs that restore constrained assembly states. To benchmark this capability, we introduce FORGE-Bench, containing 4,000 tasks across 26 interlocking assemblies with five assemblies held out for evaluating generalization to unseen structures. We further propose FORGE (Fault-Oriented Rework Code Generation for Executable Interlocking Assembly), a structured state-to-program framework that decomposes rework generation into Rework Structuring and Action Grounding. Rework Structuring captures fault-induced recovery dependencies, while Action Grounding maps these dependencies into state-conditioned executable actions. Experiments show that locally reasonable repair code is insufficient for reliable assembly recovery, and that connecting structural recovery reasoning with executable action realization is essential for verified target restoration.

Subjects:	Artificial Intelligence (cs.AI)
Cite as:	arXiv:2611.02536 [cs.AI]
	(or arXiv:2611.02536v2 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2611.02536 arXiv-issued DOI via DataCite

Submission history

From: Jiahao Fu [view email]
[v1] Mon, 2 Nov 2026 05:07:03 UTC (4,008 KB)
[v2] Tue, 10 Nov 2026 14:13:33 UTC (3,955 KB)