Noetic Geodesic Framework: Deterministic AI Reasoning via Warped Manifolds (Early Preprint)

This early preprint introduces the Noetic Geodesic Framework (NGF), a geometric approach to deterministic AI reasoning. By inducing Semantic Mass, we warp latent spaces into Warped Semantic Manifolds populated by Cognition Wells, guiding Geodesic Traversals toward Noetic Singularities (truth-aligned endpoints).

Within the scope of the NGF-alpha project, Stage-10 established a robust geodesic parser/executor. Stage-11 consolidates the doctrine Warp → Detect → Denoise, introducing funnel-fit wells, matched-filter detection with null calibration, and denoising control systems (EMA+median smoothing, confidence gates, phantom-guard probes, jitter averaging, SNR logging).

Benchmarks on Latent-ARC (n=100) show:

• Stock baseline: 49/100 exact, F1 ≈ 0.80

• Geodesic (Stage-10): 64/100 exact, F1 ≈ 0.90

• NGF Stage-11 (denoise path): 100/100 exact, F1 ≈ 0.998, hallucination ≈ 0.5% (noise floor)

These results represent the “breaking point” transition from heuristic parsing to explicit warped-manifold energy frameworks, with hallucination suppression by design. Note: these tests are on a simulated embedding level only, live tests on LLM will soon follow.

Note: This is an early preprint; future versions will expand experiments and formal mathematical proofs.

Provisional patents pending: US #63/864,726; #63/865,437; #63/871,647; #63/872,334.