Accurate Legal Reasoning at Scale: Neuro-Symbolic Offloading and Structural Auditability for Robust Legal Adjudication
Stanis{\l}aw S\'ojka, Witold Kowalczyk
Why It Matters
What makes this one worth your time
This approach could significantly improve the efficiency and reliability of legal reasoning systems, which are crucial for handling complex legal texts at scale.
A neuro-symbolic system for translating legal texts into deterministic graphs enhances legal reasoning accuracy and efficiency.
Summary
The paper introduces a neuro-symbolic approach called Amortized Intelligence, which translates legal texts into a deterministic graph representation for legal adjudication, aiming to improve reasoning accuracy and reduce computational costs.
Key contributions
- Introduction of a neuro-symbolic approach for legal text translation.
- Development of a Deterministic Autonomous Contract Language (DACL) for legal reasoning.
- Demonstrated reduction in computational costs and improved consistency over probabilistic models.
Notable insights
- The use of a typed graph intermediate representation (DACL) allows for deterministic and visually auditable legal adjudication.
- Amortized Intelligence reduces compute costs by over 90% in high-volume legal workflows.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2605.02472v1 Announce Type: new Abstract: Legal texts often contain computational legal clauses--provisions whose understanding requires complex logic. While frontier Large Reasoning Models (LRMs) can describe such clauses, building production-ready systems is limited by reasoning errors and the high cost of inference. We propose Amortized Intelligence, a neuro-symbolic approach where we use an LLM once to translate a legal text into Deterministic Autonomous Contract Language (DACL): a typed graph intermediate representation. Adjudication then relies on deterministic graph executions with a visually auditable trace. In comparison against runtime LRM baselines (including GPT-5.2 and Gemini 3 Pro), our DACL-based Agent achieves near-perfect consistency and mitigates the "reasoning cliff" observed in probabilistic models. The system reduces compute costs by over 90% in high-volume workflows while satisfying the strict auditability requirements of legal adjudication.