Update model card to ZenBrain v6 (record 19481262)

README.md

@@ -30,9 +30,9 @@ pipeline_tag: text-generation
 
 ZenBrain is a 7-layer neuroscience-inspired memory architecture for autonomous AI systems. It bridges the gap between biological memory principles and practical AI system design, delivering measurable performance improvements across long-context recall, memory stability, and knowledge retrieval tasks.
 
-**Paper:** [ZenBrain v5 (Zenodo)](https://zenodo.org/records/19413933) | DOI: `10.5281/zenodo.19353663`
+**Paper:** [ZenBrain v6 (Zenodo)](https://zenodo.org/records/19481262) | DOI: `10.5281/zenodo.19353663`
 
-## Key Results (7 Experiments)
+## Key Results (10 Experiments)
 
 | Experiment | Metric | Result |
 |------------|--------|--------|
@@ -43,8 +43,11 @@ ZenBrain is a 7-layer neuroscience-inspired memory architecture for autonomous A
 | Exp 5 — Hebbian Retrieval | Precision@5 | **0.955** |
 | Exp 6 — Bayesian Confidence | Confidence AUC | **0.797** (+49.5% vs. 0.533) |
 | Exp 7 — MemoryArena | Retrieval accuracy | **+19.5%** vs. Flat Store |
+| Exp 8 — Moderate Ablation | 15-algorithm suite | Cooperative redundancy (no single removal matters) |
+| Exp 9 — Challenging Ablation | 15-algorithm suite | **7 of 15** algorithms individually significant |
+| Exp 10 — Stress Ablation | 15-algorithm suite | **9 of 15** algorithms individually critical |
 
-> Experiments 1, 2, 4, 5, 7 use synthetic benchmark data (FakeEmbeddingProvider). Real-data runs require the LoCoMo and MemoryArena datasets — see [experiments/README.md](https://github.com/zensation-ai/zenbrain/tree/main/experiments).
+> The full 15-algorithm ablation reveals a cooperative survival network with a measurable gradient across three difficulty levels. Sleep consolidation acts as a **1.92x multiplier**. 95 reproducible tests across 4 experiment suites, all with Mulberry32 seeded PRNG (10 seeds).
 
 ## Architecture: 7 Memory Layers
 
@@ -106,8 +109,8 @@ const results = await memory.recall('Hebbian learning', { topK: 5 });
 
 ## Links
 
-- **Paper (v5):** https://zenodo.org/records/19413933
-- **PDF:** https://zenodo.org/records/19413933/files/zenbrain.pdf
+- **Paper (v6):** https://zenodo.org/records/19481262
+- **PDF:** https://zenodo.org/records/19481262/files/zenbrain-v6.pdf
 - **GitHub:** https://github.com/zensation-ai/zenbrain
 - **Website:** https://zensation.ai/technologie
 - **npm:** https://www.npmjs.com/package/@zensation/algorithms
@@ -120,11 +123,11 @@ const results = await memory.recall('Hebbian learning', { topK: 5 });
   author  = {Bering, Alexander},
   year    = {2026},
   doi     = {10.5281/zenodo.19353663},
-  url     = {https://zenodo.org/records/19413933},
+  url     = {https://zenodo.org/records/19481262},
   note    = {Zenodo Preprint}
 }
 ```
 
 ## License
 
-Apache 2.0 — see [LICENSE](https://github.com/zensation-ai/zenbrain/blob/main/LICENSE)
+Apache 2.0 — see [LICENSE](https://github.com/zensation-ai/zenbrain/blob/main/LICENSE)