Adaptive Cost-Efficient Evaluation for Reliable Patent Claim Generation
Automated patent claim validation demands low error tolerance. However, existing approaches face a rigidity-resource dilemma: lightweight encoders cannot track long-range legal dependencies, while exhaustive LLM verification incurs 4-5X higher overhead at million-claim scale. A naive confidence-based cascade cannot resolve this because binary validity scores fail to distinguish structurally distinct error types which require different reasoning depths. We propose a two-stage framework: Adaptive Cost-efficient Evaluation (ACE), which exploits the categorical structure of patent errors for uncertainty-aware routing. In the first stage, a fine-tuned encoder projects claims into a K+1 distrib...
| Authors | Yongmin Yoo, Qiongkai Xu, Longbing Cao |
|---|---|
| Primary category | cs.CL |
| Published | 5 April 2026 |
| Last updated at source | 27 May 2026 |
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