We ran a 500-cycle benchmark to test long-horizon coherence, reasoning stability, and identity persistence in large language models.

The experiment used the Sigma Runtime, a model-agnostic control layer that adds long-term memory, structural coherence tracking, and adaptive equilibrium regulation to standard LLMs. It enables stable reasoning and personality continuity across hundreds of interactions without context resets.

Protocol overview - 500 reasoning cycles divided into 10 blocks of 50 questions. - Every 50th response (“Rib Point”) compresses and validates reasoning from the previous 49 cycles. - Each block builds on prior synthesis, forming a cumulative reasoning chain up to cycle 500. - The final cycle (C500) performs full closure, verifying long-range consistency.

Two independent tests - OpenAI GPT-5.2 — phase-stable regime: early micro-fractures during lattice formation self-corrected by C50; zero structural drift afterward. - Google Gemini-3-Flash — forced-equilibrium regime: proportional feedback absorbed API truncations and prevented over-stabilization.

Results - Both runs maintained full coherence and stable identity across 500 cycles. - Rib Points confirmed successful recursive compression: reasoning remained referentially consistent. - Structural drift and semantic degradation ≈ 0 across both architectures.

Architecture components - SRIP-09: Long-Term Memory + Structural Coherence Layer - SRIP-09c: Nucleus Integration Protocol (semantic anchoring)

Full report (DOI): https://doi.org/10.5281/zenodo.18271591 Appendix & data: https://github.com/sigmastratum/documentation/blob/a57bae59b...

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