Adaptive Cost-Efficient Evaluation for Reliable Patent Claim Validation

• URL: http://arxiv.org/abs/2604.04295v1
• Date: Sun, 05 Apr 2026 22:25:36 GMT
• Title: Adaptive Cost-Efficient Evaluation for Reliable Patent Claim Validation
• Abstract summary: We propose ACE (Adaptive Cost-efficient Evaluation), a hybrid framework that uses predictive entropy to route only high-uncertainty claims to an expert LLM.<n> ACE achieves the best F1 among the evaluated methods at 94.95%, while reducing operational costs by 78% compared to standalone LLM deployments.
• Score: 35.13558856456741
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated validation of patent claims demands zero-defect tolerance, as even a single structural flaw can render a claim legally defective. Existing evaluation paradigms suffer from a rigidity-resource dilemma: lightweight encoders struggle with nuanced legal dependencies, while exhaustive verification via Large Language Models (LLMs) is prohibitively costly. To bridge this gap, we propose ACE (Adaptive Cost-efficient Evaluation), a hybrid framework that uses predictive entropy to route only high-uncertainty claims to an expert LLM. The expert then executes a Chain of Patent Thought (CoPT) protocol grounded in 35 U.S.C. statutory standards. This design enables ACE to handle long-range legal dependencies more effectively while preserving efficiency. ACE achieves the best F1 among the evaluated methods at 94.95\%, while reducing operational costs by 78\% compared to standalone LLM deployments. We also construct ACE-40k, a 40,000-claim benchmark with MPEP-grounded error annotations, to facilitate further research.

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