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Accelerating Floating-Point Satisfiability Solving via Gradient Normalization

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 08/10/2026, 04:03:24 UTC.

Briefings

This article introduces GradSAT, a new framework for Satisfiability Modulo Theories (SMT) solving that uses Multi-Task Learning to overcome gradient domination in optimization-based solvers. The system treats each SMT clause as an independent MTL task and applies dynamic gradient normalization to balance gradient magnitudes across all clauses at runtime. A two-stage pipeline combines a GPU-accelerated PyTorch backend for continuous relaxation with a bit-precise local search engine for exact resolution. The work was published on arXiv in October 2026 and is presented as a preprint.

Key points

• GradSAT reformulates SMT solving by treating each clause as an independent Multi-Task Learning task.
• The framework uses dynamic gradient normalization to actively balance gradient magnitudes across all clauses at runtime.

Context and limitations

The evidence is limited to the abstract and initial description of the framework's architecture and methodology, with no experimental results, performance benchmarks, or specific numerical outcomes provided.

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