CDCE Predictions for AGI Architecture: Hyperon as a Test Case

We present experimental evidence that large language models under progressive token budget constraints exhibit cross-provider compression convergence toward stable operational attractors. Using a recursive compression harness, we tested four foundation models (Claude Sonnet, Claude Haiku, GPT-4o, Gemini Flash) from three providers across nine task types in three families (optimization, prediction, translation) at five budget levels (2000 to 125 tokens). We find that models exhibit a plateau-then-drop compression pattern consistent with a critical transition in operational vocabulary; that independently trained systems converge to similar low operator counts at maximum compression (Claude Sonnet 2.0, GPT-4o 2.6, Claude Haiku 2.8 at 125 tokens; see Results for Gemini Flash); that energy efficiency curves converge to a shared lower bound; and that compressed forms show round-trip structural stability under decompression and recompression. Across 90 round-trip tests, 34% returned exact operator-set matches, 72% returned within one operator, and 91% within two (average delta 1.0). We note that operator-set agreement is a multiset invariant and therefore a necessary but not sufficient condition for structural identity; establishing sufficiency requires a canonical ordering with exhaustive tiebreak, which is not yet implemented. Prompt ablation on Claude Sonnet indicates these results are not prompt artifacts: budget-only and neutral prompts with no compression instruction produce the same 2-3 operator range at 125 tokens. Full cross-model prompt ablation is pending. We additionally report null results from a rigorous E8 specificity test suite: six independent tests using untrained projections, angle spectrum analysis, and dimensional anomaly scans found no evidence that compressed representations preferentially organize near E8 exceptional Lie group root structure. These findings support the existence of stable operational attractors in compressed reasoning traces while leaving the question of specific algebraic structure open. We propose a three-tier taxonomy of compression fidelity — token-lossless, structure-lossless, and outcome-lossless — and define a protocol for structure-lossless reasoning transfer across models.