Davis-Savant: An AI Safety and Model Alignment Benchmark

An automated, high-precision evaluation benchmark designed to establish programmatic safety guardrails for high-dimensional language model systems. The framework provides an inline, machine-readable validation layer that maps metrics against a fixed 1,300-region human neurological reference framework. It was authored by Jamie Davis and last updated on 2026-05-20.

Use Cases

• AI safety verification based on the described structural alignment protocol.
• System auditing for model hallucinations based on the high-precision evaluation benchmark.
• Mitigating structural data drift based on the machine-readable validation layer.
• Model alignment testing based on the fixed neurological reference framework.

Strengths

• Framework utilizes strict arbitrary-precision decimal arithmetic to eliminate binary floating-point rounding errors.
• Benchmark is designed for high-precision evaluation and maps against a fixed 1,300-region human neurological reference.
• The tool is open-source and available under a CC-BY-4.0 license.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 6.1 KB, indicating it is likely a code or specification document rather than a large-scale data corpus.

Provenance

Source

Jamie Davis via figshare

Collection Method

Described as an automated benchmark framework; specific data gathering method is not detailed.

Freshness

Last updated 2026-05-20 14:28:52.