---
id: 20261009-T0-16
title: "Accelerating Floating-Point Satisfiability Solving via Gradient Normalization"
url: https://ai.daily.yangsir.net/daily/20261009-T0-16
issue_date: 2026-10-09
publish_date: 2026-10-08T04:00:00.000Z
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2610.08808
---

# Accelerating Floating-Point Satisfiability Solving via Gradient Normalization

arXiv:2610.08808v1 Announce Type: new Abstract: Satisfiability Modulo Theories (SMT) solvers are foundational to software verification, program analysis, and compiler testing, particularly over the theory of Quantifier-Free Floating-Point (QF_FP). While recent optimization-based SMT solvers have suc

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2610.08808)

**详情页**：https://ai.daily.yangsir.net/daily/20261009-T0-16

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