Decompose, Structure, and Repair: A Neuro-Symbolic Framework for Autoformalization via Operator Trees
arXiv:2604.19000v2 Announce Type: replace Abstract: Statement autoformalization acts as a critical bridge between human mathematics and formal mathematics by translating natural language problems into formal language. While prior works have focused on data synthesis and diverse training paradigms to optimize end-to-end Large Language Models (LLMs), they typically treat formal code as flat sequences, neglecting the hierarchical logic inherent in mathematical statements. In this work, we introduce Decompose, Structure, and Repair (DSR), a neuro-symbolic framework that restructures autoformalizat
The increasing sophistication of LLMs and the growing demand for verifiable, error-free mathematical proofs are driving advancements in formalization methods.
Improving automated formalization is crucial for bridging natural language and robust computational verification, impacting fields from software engineering to scientific discovery.
This neuro-symbolic approach moves beyond treating formal code as flat sequences, introducing hierarchical logic that could lead to more accurate and reliable autoformalization.
- · AI researchers
- · Formal verification developers
- · Mathematics education
- · Software engineering
- · Manual proof assistants
- · LLMs without symbolic integration
More complex mathematical statements become amenable to automated formalization.
Accelerated development and verification of advanced AI algorithms and critical software systems.
Potential for AI to independently discover and verify new mathematical theorems, transforming research paradigms.
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Read at arXiv cs.LG