An empirical evaluation framework designed to stress-test and map the exact logical decay, semantic degradation, and performance failure limits of large reasoning models under lossy token compression and multi-step inference execution.
- Deterministic Tracking: Measuring token-to-logic degradation metrics.
- Semantic Compression Analysis: Testing inference stability when prompt structures are highly compressed.
- Failure Layer Mapping: Identifying the exact depth (step count) where computational reasoning collapses.
- Core Engine:
Python 3.x - Architecture: Autonomous JSON automation pipelines for prompt stress-testing.
Status: Architecture active. Core mathematical benchmarking scripts deploying next..