- Updated: August 24, 2026
- 1 min read
Moxia: A Trust‑First Neuro‑Symbolic Execution Architecture for Self‑Explaining Mathematical Reasoning
# Moxia: A Trust‑First Neuro‑Symbolic Execution Architecture for Self‑Explaining Mathematical Reasoning
**Authors:** Alessio Bruno
**Abstract**
We present Moxia (formerly AXIOM), a trust‑first neuro‑symbolic architecture for self‑explaining mathematical reasoning over natural‑language input. Its language model is strictly a canonicalizer: it rewrites informal problem text into a narrow schema consumed by a deterministic Computer‑Algebra‑System (CAS) pipeline, which derives and verifies the answer or abstains as a first‑class output. Routing follows a 1:1:1 alignment of problem‑shape regex, schema‑specific prompt, and closed‑form CAS handler, with 4,783 routes shipped, 71% of which answer without invoking the language model, and zero LOST_CORRECT regressions as a standing release gate. …
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## Key Highlights
– **Trust‑First Design** – The system never generates answers; it only derives them via CAS, ensuring 99.98 % trust on parseable outputs.
– **Zero Regressions** – No confident‑wrong answers on held‑out benchmarks.
– **Performance** – 1 ms per record on a 20,000‑record arithmetic benchmark.
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## Internal Resources
– [SEO Brief & Keyword Strategy](/seo-brief-moxia)
– [Full Article Draft](/draft-moxia)
– [Generated Illustration](/images/moxia-architecture.png)
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*This article is part of the ubos.tech technical blog series.*
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.