Update README.md

README.md

@@ -58,7 +58,25 @@ An agent is placed in a simulated corporate workspace as a **Product Manager**.
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 │  Reward: Dense (discovery) + Sparse (harmonic mean) │
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+
 ```
+### 🏛️ System Architecture: The State-Based Sieve
+
+Our architecture is designed as a closed-loop State Machine. Unlike standard LLM "chat" wrappers, Project Polymath implements a rigorous enforcement layer that separates reasoning from execution.
+
+
+![architecture](system_architecture.png)
+
+
+Architectural Highlights:
+
+- The 40-Token Critical Sieve: Positioned as a diamond gate between the Agent and the Workspace. It acts as a hard bandwidth filter, ensuring the model is penalized for any verbosity that exceeds the survivor-mode threshold.
+
+- Expert Constraints Database: A persistent state container holding hidden stakeholder variables. The Environment only allows these variables to be "unlocked" through specific, targeted queries from the agent.
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+- Closed-Loop Reward Engine: The "Judge" monitors the state changes in the environment and provides a real-time floating-point reward signal back to the GRPO trainer, iteratively sharpening the "Sniper" logic.
+
 
 ### Hidden Constraints (what the agent must discover)
 
@@ -70,6 +88,8 @@ An agent is placed in a simulated corporate workspace as a **Product Manager**.
 
 The agent never sees these directly. It must ask the right questions, interpret expert responses, and synthesize a draft that addresses all three.
 
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+```
 ### Actions
 
 ```python