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This article introduces the Amortized Intelligence framework, which enables low-cost, high-consistency automated processing of legal rulings by translating legal texts into Deterministic Autonomous Contract Language (DACL) in a one-time manner.
Section 01
This article introduces the Amortized Intelligence framework, which achieves low-cost (90% reduction) and high-consistency automated processing of legal rulings by translating legal texts into Deterministic Autonomous Contract Language (DACL) once. It combines the expressive power of neural networks with the deterministic advantages of symbolic systems.
Section 02
Legal texts contain a large number of computational clauses that require complex logical reasoning; although traditional Large Reasoning Models (LRMs) can describe clauses, production-level systems face two major issues: high reasoning error rates and high costs.
Section 03
Core Idea: Use LLM to translate legal texts into Deterministic Autonomous Contract Language (DACL) (a typed graph intermediate representation) once. Subsequent rulings are executed based on the deterministic graph and generate visual audit trails. The technical architecture includes three components: 1. One-time translation layer (LLM to DACL); 2. Deterministic execution engine (precise reasoning with graph structure); 3. Audit trail system (visualized ruling records).
Section 04
Compared with baselines such as GPT-5.2 and Gemini 3 Pro, DACL agents achieve near-perfect consistency (alleviating the "reasoning cliff"), reduce computational costs by over 90%, and meet audit compliance requirements.
Section 05
It brings a breakthrough to legal technology by converting expensive neural reasoning into one-time translation, and subsequent symbolic processing ensures reliability and low cost. Applicable scenarios: high-throughput contract review, strict audit compliance checks, and high-consistency legal ruling tasks.
Section 06
Amortized Intelligence demonstrates a new path for the integration of neural networks and symbolic systems, providing a feasible technical solution for the practical deployment of legal AI.
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