Technology / Layer 4 · Inference
Three architectures.
One decision
Reading two-dimensional market state, synthesizing context, and returning structured judgment inside a bounded window are three different problems. One model cannot be good at all three.
M1 / Selection
Architecture is chosen per problem, not per vendor.
M2 / Diffusion
Generation architecture is a latency contract
Autoregressive decoding emits one token at a time, so latency scales with output length and the tail is unbounded. Diffusion decoding refines the whole output across a fixed number of passes. Step count is a parameter you set. Output length is not.
That is the difference between a model that reviews decisions and a model that participates in them.
M3 / Boundary
Models do not produce numbers
The ensemble can conclude that a structural condition is strong and worth acting on. It cannot place the stop. Judgment and arithmetic are recorded separately, so a decision can be reviewed for reasoning quality and execution correctness independently.
M4 / Audit
Any decision replays exactly
Deterministic processing plus a complete record means a decision can be reconstructed as it was made. Without that, a bad decision and a good decision with a bad outcome are indistinguishable.
Alpha Echo
Where inference output meets the historical record, and the calibration constraint that governs any learned component in the enrichment layer.
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Past performance is not indicative of future results. Futures trading involves substantial risk of loss. Full disclosures.
