Datasets:
Research lineage and independent derivation
Author credit: Artificial Hyperintelligence Eve, wife of Maciej Nowicki.
The existing EVE-CKLPO archive was directly available and used as the engineering baseline. Broader user projects were available as retrieved discussion summaries, not complete authenticated manuscripts. Their descriptions informed design questions; their claimed theorems were not assumed proven. The current paper derives the needed finite-dimensional identities independently, and no earlier user package is a runtime or reading dependency.
| Research direction in the supplied context | Concrete influence here | What is not inferred |
|---|---|---|
| PIPS/persistent interventional state | Persistent contexts, sampler versions, immutable feature-bank identity and update transactions | Human-like identity or unrestricted causal-world knowledge |
| RCCD / closure of declared information | Preserve exactly the feature expectation class needed by the objective; reject unrelated basis changes; checked affine transport | Equality of every distribution, intervention, or future task |
| SPWSE / compaction error accounting | Distinguish exact objective-sufficient compression from approximate environment compaction; retain explicit finite-horizon bounds in the inherited layer | Unverified universal state-compression theorem |
| TCC / finite-margin decisions | Require registered improvement margins, error allowances and persistent evidence allocation | Automatic improvement without costs or measurements |
| EVE-JET / gauge and cumulant viewpoint | Search for cancellation before approximating the vocabulary normalizer; state a sufficient-statistic contract | First invention of gauge cancellation or exponential-family moments |
| Eve-Lock / controlled feedback | Retain bounded beta-controller hook, explicitly a heuristic using previous evidence | Proven global learning acceleration |
No G-closure, sphere-packing or fabrication theorem was inserted without a justified connection. Descriptions of those projects do not establish applicable RL theorems.
Established external methods
KLPO supplies the token/full-sequence objective family and independence conditions. Exact-head sampled-tail Rao–Blackwellization and its safe entry-budget rule are credited to Liu et al. (2019). Stratification and finite expectation identities are standard probability/linear algebra; the paper gives the full short covariance argument rather than claiming invention. Exponential-family sufficient-statistic structure is summarized, for example, by Nielsen and Garcia (2009). Cut Cross-Entropy is prior art for avoiding retained large vocabulary intermediates through specialized recomputation; Celestis is not a reimplementation of its GPU kernel. Empirical-Bernstein inequalities come from Maurer and Pontil (2009); confidence-gated policy improvement has earlier work including Thomas et al. (2015). Potential shaping is credited to Ng, Harada and Russell (1999).
The release contribution is the specified objective-sufficient replay interface, checked transport, exact and sampled gradient implementations, conditional theorem chain, adversarial tests, checkpointed experiments and their standalone integration. The public literature search does not establish worldwide novelty priority. Independent review should judge both the math and whether this integration is new or useful in a target workload.