Update README.md

README.md

@@ -19,7 +19,7 @@ This organization is where we publish our open‑weight models, inference resear
 
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-## 🔬 Efficiency through innovation: **ternary**
+## 🔬 Our research bet: **ternary**
 
 Modern AI isn't limited by intelligence — it's limited by **the cost of storing and moving it**. Most of the cost of running a language model is the memory bandwidth spent streaming billions of weights for every token, and the arithmetic of multiplying them.
 
@@ -35,7 +35,7 @@ The payoff spans hardware generations: ternary's footprint lets capable models *
 
 ## 📦 What you'll find in this organization
 
-**Ternary‑distilled Gemma‑4‑E2B** — our first release, and the first in a planned series.
+**Terna‑E2B** — our first release, and the first in the **Terna** family of ternary‑weight models (*Terna*, from Latin *terni*, "three each").
 
 A ternary (≈1.6‑bit) distillation of **Gemma‑4‑E2B**, a capability‑dense model with a **transparent, Western open‑weight lineage**. Distributed as **GGUF** for efficient local and datacenter serving.
 
@@ -67,7 +67,7 @@ We validate this across **deliberately different accelerator families** — lega
 
 ## 📄 Research
 
-- **TR‑2026‑001 — *Ternary Foundations for Efficient, Sovereign AI*** — the mathematics of ternary, why Gemma‑4, the engine that turns footprint into throughput, and the implications for legacy and modern hardware.
+- **[TR‑2026‑001 — *Ternary Foundations for Efficient, Sovereign AI*](https://doi.org/10.5281/zenodo.21110909)** — the mathematics of ternary, why Gemma‑4, the engine that turns footprint into throughput, and the implications for legacy and modern hardware. (Zenodo, DOI [10.5281/zenodo.21110909](https://doi.org/10.5281/zenodo.21110909))
 - **TR‑2026‑002 — *Quantization‑Aware Training and Ternary Weights: Method vs. Representation*** — clears the "QAT vs ternary" category error and places both on one efficiency curve.
 
 Further reports — on serving engines across accelerator families, multi‑GPU parallelism for heterogeneous fleets, shared‑context concurrency, ternary at larger scales, and governable agentic orchestration — are on the Institute's roadmap.
@@ -87,7 +87,7 @@ Further reports — on serving engines across accelerator families, multi‑GPU
 
 The GoAutomate AI Institute welcomes collaboration with Canadian organizations, healthcare systems, public institutions, researchers, policymakers, and technology leaders who share our commitment to responsible and accessible AI.
 
-**→ [info@goautomate.ai](mailto:info@goautomate.ai)  ·  [goautomate.ai](https://www.goautomate.ai)**
+**→ [info@goautomate.ai](mailto:info@goautomate.ai)  ·  [goautomate.institute](https://www.goautomate.institute)**
 
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