Update README.md

README.md

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 pinned: true
 short_description: Reasoning-first, agentic small language models (SLMs).
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+
 # DeepBrainz AI & Labs
 
-**DeepBrainz AI & Labs builds reasoning-first, agentic Small Language Models (SLMs), advancing RL post-training and test-time scaling to deliver efficient, reliable intelligence, while actively exploring diffusion-based and iterative inference regimes.**
+**Reasoning-first Small Language Models for agentic systems in production**
+
+DeepBrainz AI & Labs builds **reasoning-first, agentic Small Language Models (SLMs)** optimized for **reliability, controllability, and efficiency** in real-world AI systems.
+
+We focus on **behavioral intelligence** — training models to reason, plan, and act — rather than scaling parameters or gaming benchmarks.
+
+---
+
+## 🔑 Start Here (Recommended Models)
+
+If you’re new to DeepBrainz-R1, start with one of these:
+
+- **DeepBrainz-R1-4B** — flagship model  
+  Best overall reasoning quality and stability for production agentic systems.
+
+- **DeepBrainz-R1-2B** — balanced model  
+  Strong reasoning with lower latency and cost.
+
+- **DeepBrainz-R1-0.6B-v2** — small & efficient  
+  Designed for local inference, edge agents, and cost-sensitive workflows.
+
+> All other variants are **experimental or research-only**.
+
+---
+
+## 🧠 What DeepBrainz-R1 Is Built For
+
+- Multi-step reasoning
+- Tool-calling and agent loops
+- Long-context analysis
+- Deterministic, inspectable behavior
+
+### 🚫 What It Is *Not* Optimized For
+
+- Open-ended chat or roleplay
+- Creative writing
+- Prompt-memorization benchmarks
+
+---
+
+## 🧪 Research Philosophy
+
+We explicitly optimize **against**:
+- Shallow pattern matching
+- Benchmark gaming
+- Prompt memorization
+
+We treat intelligence as a **behavior to be trained**, not a side-effect of model size.
 
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@@ -36,18 +84,16 @@ It is a family of reasoning-first SLMs designed for:
 - Research and agentic experimentation
 
 We publish multiple variants to support **transparency and reproducibility**.  
-Only selected releases are considered *supported*.
+Only selected releases are considered **supported**.
 
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-## Philosophy
-
-We explicitly optimize against:
-- Shallow pattern matching
-- Benchmark gaming
-- Prompt memorization
+## 🧱 Model Support Status
 
-We treat intelligence as a **behavior to be trained**, not a side-effect of model size.
+- ✅ **Supported / Production** — curated, validated releases  
+- 🧪 **Experimental** — exploratory variants  
+- 🧱 **Research Checkpoints** — raw checkpoints for reproducibility  
+- 👥 **Community Maintained** — third-party quantizations (GGUF, low-bit)
 
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