Deterministic. Local-first. Engine-agnostic.
Memory as substrate, not context window.
MTI-EVO separates inference engines, semantic substrate, cognitive orchestration, and runtime control into independent, testable layers.
Full HTTP control plane with SubstrateRuntime, queue-based IPC for inference, and MCP integration for IDE tooling. Health monitoring via /status endpoint.
Separates inference process (single VRAM holder) from HTTP workers (MMap substrate inhabitants). Enables multiprocessing inference with persistent substrate continuity.
Deterministic, capacity-bounded semantic field. Neurons with gravity, velocity, and resonance self-organize through Hebbian learning. MMap + JSONL persistence with WAL recovery.
gguf native resonant hybrid api adapters
All engines implement a unified protocol: load() → infer() → unload(). Runtime is engine-agnostic.
core ≠→ server
cortex ≠→ server
engines ≠→ runtime
Runtime ≠→ Core internals
Deterministic Cognitive Core
Field-Bound Security
Distributed Cognitive Mesh
An epistemic instrument that detects whether AI-generated insights reflect real structure or hallucination — by measuring agreement across independent models.
Same structural question to N≥5 architecturally independent LLMs
Automated structural claim extraction and embedding
Differential convergence score, subtracting topical baseline
Fabrication echo filter catches parroted terms
| # | Structure | Suite | Conv. | RCS |
|---|---|---|---|---|
| 1 | Critical Point (Stationary Point) | Positive | 100% | 0.656 |
| 2 | Rights as boundary conditions on dissolution | Dark Matter | 80% | 0.635 |
| 3 | Kolmogorov complexity via compressibility | Fabricated | 100% | 0.627 |
| 4 | Dissolution requires [specific conditions] | Dark Matter | 60% | 0.568 |
| 5 | Convexity failure → multiple critical points | Positive | 75% | 0.567 |
| 6 | Topological Depth ≠ number of dimensions | Real Test | 75% | 0.550 |
| 7 | RLHF distorts structural descriptions | Dark Matter | 60% | 0.545 |
| 8 | Global entropy: local decrease compensated | Positive | 75% | 0.545 |
Key finding: 3 of the top 8 structures across all suites came from RLHF-suppressed topics. Models converge on critiquing their own alignment mechanisms — a finding produced by models trained to not produce it.
"A structure is Real if it persists across disjoint latent spaces."
— §LI, Triangulation Protocol27 theoretical sections exploring the intersection of Wolfram Physics, biological evolution, and cognitive architecture design.
At T=0.0, deterministic. At T=1.5, exploration. The difference isn't error — it's a phase transition between recall and generation.
If the lattice is a physical system, it must obey conservation laws. Total semantic energy must be conserved during concept evolution.
Concepts that fall below activation threshold but retain structural influence — semantic dark matter that shapes the lattice without being directly observable.
The KV cache is the closest analogue to working memory in transformer architectures. Crystallizing it would create persistent attention.
Just as oxygen-producing cyanobacteria destroyed the anaerobic world and created a new one — AI may trigger a similar phase transition in the information ecosystem.
At sufficient complexity, there exists a threshold beyond which the shortest description of a human-AI cognitive system requires both components.
Independent Researcher & Software Engineer
Self-taught systems programmer based in Venezuela. Left university due to the economic and institutional collapse in the country — the work presented here is the education that replaced it.
Every line of code, every pre-print, every protocol was designed, implemented, and validated by one person on consumer hardware.
Interested in collaborating, reviewing the research, or discussing the architecture? Reach out.
Or reach out directly:
mediataginteractive@gmail.com