Update README.md

README.md

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 ---
 license: mit
+tags:
+- financial-services
+- evaluation
+- determinism
+- regulatory-compliance
+- sec-filings
+- small-language-models
+- llm
 ---
 # LLM Output Drift: Cross-Provider Validation & Mitigation for Financial Workflows
 
@@ -11,4 +19,38 @@ license: mit
 
 This repository is a Hugging Face landing page for the paper and its open-source implementation.
 It focuses on deterministic test harnesses, cross-provider validation, and risk-tiered deployment
-for financial LLM workflows (SEC 10-Ks, RAG over filings, JSON/SQL tasks).
+for financial LLM workflows (SEC 10-Ks, RAG over filings, JSON/SQL tasks).
+
+## 🔑 Key finding
+
+> Well-engineered **7–8B models achieve 100% output consistency at T=0.0**, while a 120B model reaches only **12.5% consistency**, regardless of configuration.
+
+Across **480 runs** (5 models, 3 tasks, 2 temperatures, 3 concurrency levels), we show an
+inverse relationship between model size and determinism and map this to regulatory
+requirements (FSB, BIS, CFTC).
+
+---
+
+## 📊 Model tier classification
+
+| Tier | Models                      | Consistency @ T=0.0 | Status             | Recommended use |
+|------|-----------------------------|---------------------|--------------------|-----------------|
+| **1** | Granite-3-8B, Qwen2.5-7B   | **100%**            | ✅ Production-ready | All regulated tasks |
+| **2** | Llama-3.3-70B, Mistral-Medium-2505 | 56–100%  | ⚠️ Task-specific     | SQL / structured only |
+| **3** | GPT-OSS-120B               | **12.5%**           | ❌ Non-compliant    | Not for compliance |
+
+*n = 480 runs (16 per condition), 95% Wilson CIs, p < 0.0001 (Fisher’s exact).*
+
+---
+
+## 🎯 Why this matters
+
+Financial institutions face a **“verification tax”**: human review erodes AI productivity
+gains when outputs are nondeterministic.
+
+This framework shows:
+
+- **Audit-ready determinism is achievable** with the right model + decoding setup.
+- **Cross-provider consistency**: behavior transfers between local (Ollama) and cloud (IBM watsonx.ai).
+- **Task-specific drift**: SQL and structured summaries remain stable even at T=0.2; RAG is far more sensitive.
+