⚡ Executive Thesis: Solving Institutional AI's Epistemological Crisis
Standard stochastic Large Language Models hallucinate arithmetic and drift under high-stress economic scenarios, making them dangerous for fiduciary due diligence. ADAM v30.1 resolves this through a Probabilistic-to-Deterministic Integration Layer (PDIL). By strictly separating high-velocity semantic parsing (System 1 Swarm) from non-LLM Rust-backed pricing engines and FIBO Graph Neural Networks (System 2 & 3), ADAM achieves zero-hallucination institutional credit underwriting across Broadly Syndicated Loans (BSL), Distressed Debt, and complex TMT verticals.
Institutional Benchmark Radar
ADAM Neuro-Symbolic vs Standard LLM Wrappers
*Note: Neuro-symbolic binding forces 0% drift in Covenant Testing & Financial Math by handing off arithmetic to Rust and JSONLogic.
Sub-System Execution Latency (ms)
Time from SEC Filing Trigger to Risk Rating Output
*Note: Fast perception (System 1) executes asynchronously while Rust pricing primitives execute in microsecond timeframes.