01
Core Intent
- Collapse distortion through recursion
- Cross-examine models against each other
- Anchor everything into geometry, cycles, and behaviour
// ResearchV1 · GEAR-01 · Research Gear
CTDMM Hyper-Recursion Loop v1
From many runs to one structure. From drift to collapse.
This gear is not 'more calls'. It is engineered convergence: base models refine internally, then cross-examine each other, then a Conductor collapses the entire agreement field into one Hyper-Stabilized Answer anchored in geometry, cycles, and behaviour.
RecursionConvergenceMulti-ModelOrchestration
Pull Quote
"Truth over generation. Structure over storytelling. Stability over confidence. Iteration over one-shot."
02
Entities in the Loop
| Entity | Definition |
|---|---|
| M₁…Mₙ | Base models (e.g. GPT, Claude, Llama) |
| R_internal | Runs per model for internal refinement (e.g. 10, 100) |
| R_cross | Runs per model for cross-examination |
| Answer Set | All candidate answers per model |
| Distortion Map | How each answer deviates from others |
| Geometry Signature | Structural pattern of reasoning |
| Cycle Signature | Temporal / causal flow of reasoning |
| Behaviour Signature | Implied dynamics, incentives, risks |
03
Phase I — Internal Refinement
For each model Mᵢ: seed with the clean signal set from the Distortion Filter Layer. Generate, evaluate, refine — recursively, R_internal times. The Orchestration Layer scores structural coherence (geometry), temporal consistency (cycles), behavioural realism (dynamics), and distortion pressure.
Collapse
Select Aᵢ⁰ = most stable, lowest-variance, highest-coherence answer. Store the full Refinement Trace.
04
Phase II — Cross-Examination
Every model is forced to interrogate another model's absolute answer. For each ordered pair (Mᵢ, Mⱼ), anchor Aⱼ⁰ as context for Mᵢ and refine R_cross times, scoring agreement with both Aᵢ⁰ and Aⱼ⁰, plus structural, cycle, and behavioural alignment.
Output
Aᵢʲ = Mᵢ's stabilized view of Mⱼ's absolute answer. The full matrix of cross-answers becomes the input to Phase III.
05
Phase III — Agreement Collapse
CTDMM now acts as Conductor. Build the agreement field, project every candidate into a shared geometric space (nodes = claims, edges = dependencies, weights = confidence), remove unstable nodes, strengthen stable cores, and identify the central attractor — the structural center of truth.
- Low variance — disagreement below threshold
- High coherence — geometry, cycles, and behaviour aligned
- Distortion collapse — no major unresolved contradictions
- Temporal fit — answer matches the context cycle
If thresholds fail
Feed disagreement back into another Hyper-Recursion Loop with updated constraints and penalties.
06
Final Artifact — Hyper-Stabilized Intelligence
- H — Hyper-Stabilized Answer
- Geometry Map — structural representation of reasoning
- Cycle Map — temporal / causal flow
- Distortion Ledger — what was removed, why, and where
- Model Agreement Profile — who agreed, who resisted, how they converged
- Reliability Score — CTDMM stability index for this conclusion
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