MathKernel: An evidence-aware multi-engine mathematics kernel and MCP server
MathKernel is a new tool designed to act as an orchestration layer for LLMs, ensuring mathematical accuracy by providing evidence, derivation trails, and trust levels. It separates the LLM's ability to interpret intent from the kernel's ability to perform verifiable computations.
Why it matters
LLMs are notoriously unreliable at arithmetic; this tool addresses a critical failure point in AI by introducing formal verification and provenance to mathematical outputs.
An evidence-aware multi-engine mathematics kernel — usable both as a Python library ( mathkernel ) and as an MCP server ( mathkernel-mcp ) — so applications and LLMs can do advanced mathematics while preserving assumptions, provenance, and claim-specific evidence.
The LLM interprets intent; the MathKernel establishes mathematical evidence.
Mathematical results carry an explicit trust level , an engine tag, and a derivation trail . Exact computation, checked certificates, symbolic results, certified enclosures, empirical evidence, and formal proofs are distinct claims. Exact arithmetic alone is not a formal proof; approximate-input ancestry must not silently disappear.
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