Symbolic Neural CPU Enables Interpretable Program Execution with Quantization-Simulated Writeback

Jul 14, 2026

Researchers introduce a trace-supervised symbolic neural CPU that makes state transitions visible during learned program execution. The model combines recurrent control, an explicit operation router, and destination-masked register writeback, achieving exact reproduction on a 16-wide benchmark and preserving symbolic paths through 1,000-instruction programs under 8-bit quantization. The framework supports interpretable, low-precision neural execution with trajectory supervision.

Why it matters: This work provides a verifiable framework for interpretable neural execution, crucial for trust in AI systems that learn algorithmic tasks.

Full story at: arXiv AI/ML ↗