Vibe Patenting: Evaluating LLM Judges for Professional Patent-Drafting Agents

Abstract:LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We study this problem through Vibe Patenting, an end-to-end patent-drafting testbed for AI-agent evaluation. A separately-invoked LLM judge evaluates generated patent drafts and provides structured feedback for iterative revision. Across multiple inventions and drafting-agent configurations, judge-guided revision consistently improves judge-assessed quality, while unguided revision tends to saturate. Notably, iterative judge feedback enables a low-reasoning agent to approach the performance of a substantially more expensive high-reasoning agent. Stronger models and increased reasoning generally improve judge-assessed drafting quality, while domain-specific agentic workflows provide further gains. We validate the judge against independent evaluation by a professional patent attorney and find meaningful but strongly metric-dependent agreement and systematic calibration differences. These results highlight both the utility and limitations of LLM judges as evaluators and optimization signals for complex professional workflows.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)

arXiv:2609.13422 [cs.AI]

(or arXiv:2609.13422v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.13422 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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