Adaptive Cost-Efficient Evaluation for Reliable Patent Claim Generation
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917537364574208 |
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| author | Yoo, Yongmin Xu, Qiongkai Cao, Longbing |
| author_facet | Yoo, Yongmin Xu, Qiongkai Cao, Longbing |
| contents | 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 distribution over legal error types, whose predictive entropy serves as the routing signal. Claims exceeding an entropy threshold are escalated to the second stage, where an expert LLM executes a schema-constrained Chain-of-Patent-Thought (CoPT) protocol to map claim elements against 35 U.S.C. standards whose schema constraint reduces per-claim latency by 42% while producing legally grounded verdicts. We further present a 40,000-claim dataset ACE-40k with MPEP-grounded annotations, where ACE surpasses competitive baselines including a supervised 70B-parameter LLM while reducing costs by 78%. On real USPTO rejection data, the routing mechanism transfers without re-calibration, reducing inference time by 60% while maintaining competitive recall. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04295 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive Cost-Efficient Evaluation for Reliable Patent Claim Generation Yoo, Yongmin Xu, Qiongkai Cao, Longbing Computation and Language 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 distribution over legal error types, whose predictive entropy serves as the routing signal. Claims exceeding an entropy threshold are escalated to the second stage, where an expert LLM executes a schema-constrained Chain-of-Patent-Thought (CoPT) protocol to map claim elements against 35 U.S.C. standards whose schema constraint reduces per-claim latency by 42% while producing legally grounded verdicts. We further present a 40,000-claim dataset ACE-40k with MPEP-grounded annotations, where ACE surpasses competitive baselines including a supervised 70B-parameter LLM while reducing costs by 78%. On real USPTO rejection data, the routing mechanism transfers without re-calibration, reducing inference time by 60% while maintaining competitive recall. |
| title | Adaptive Cost-Efficient Evaluation for Reliable Patent Claim Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.04295 |