final commit

Blog.md

@@ -35,10 +35,11 @@ In the best case, this workflow is partially automated. Horizontal Pod Autoscale
 
 More importantly, **no existing system provides a mathematical guarantee**. There is no production autoscaler today that can prove it will keep queues bounded. There is no incident response tool that can prove its actions are cost-optimal. The industry runs on heuristics — rules of thumb encoded as YAML, battle-tested through painful outages, but ultimately ad-hoc.
 
-AntiAtropos is the first infrastructure control environment to replace heuristics with **provable stability guarantees**. It models the cluster as a fluid queue network, defines equilibrium through a Lyapunov energy function, and trains agents to minimize a Drift-Plus-Penalty objective.
+AntiAtropos is the first infrastructure control environment to replace SRE heuristics with **RL-based physics control**. It models the cluster as a fluid queue network, defines equilibrium through a Lyapunov energy function, and trains a Qwen3.5-4B model via **QLoRA REINFORCE** to minimize a Drift-Plus-Penalty objective—producing a policy that keeps queues bounded at minimum cost.
 
-### The Bottom Line: Performance without the Premium
-The unique selling point of AntiAtropos is **Efficiency**. In head-to-head benchmarks, our agent achieved perfect SLA compliance while being **50% cheaper** than traditional heuristic-based autoscalers. By understanding the underlying physics of the cluster, the agent eliminates the "panic-scaling" that plagues modern cloud infrastructure.
+### The Bottom Line: 50% Cost Savings at Perfect SLA
+
+This is not theoretical. In head-to-head benchmarks against a production-grade heuristic autoscaler, AntiAtropos maintained **perfect SLA compliance** while spending **half the infrastructure cost**. The agent learned to scale predictively rather than reactively, eliminating the "panic-scaling" that traditional threshold-based systems rely on. By understanding the physics of the cluster, it anticipates load before it arrives.
 
 ![Cost Comparison: AntiAtropos vs Heuristic Autoscaler](/images/Cost%20comparison.png)
 *Figure 1: AntiAtropos (Blue) maintains stability at half the resource cost of a production-grade heuristic scaler (Orange).*

README.md

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-> **Hackathon Submission:** We are building for **"Theme #3: World Modelling for Professional Tasks."**
-> AntiAtropos is a world model for infrastructure. By modeling a cluster as a physical system governed by Lyapunov stability, we move beyond reactive scripts to agents that understand the "Physics of Failure"—allowing for mathematically-grounded reliability in complex, real-world environments.
+> **Hackathon Submission:** We are building for **"Theme #3: World Modelling for Professional Tasks."**  
+> AntiAtropos governs clusters the way physics governs a pendulum—by minimizing Lyapunov energy. Perfect SLA at **50% lower cost**.
 
 ## Demo Video
 [![AntiAtropos Demo Video](https://img.youtube.com/vi/46SX0HocpSs/0.jpg)](https://youtu.be/46SX0HocpSs)
 
-AntiAtropos is a production-grade Autonomous SRE (Site Reliability Engineering) Control Environment. It treats a microservice cluster not as a collection of scripts, but as a **Physics Engine**. By modeling infrastructure using **Fluid Queue Dynamics** and **Lyapunov Stability Theory**, AntiAtropos provides a training ground for agents that can reason about the "Thermodynamics of the Cloud."
+AntiAtropos is a **Reinforcement Learning environment** where an AI agent learns to stabilize a 5-node microservice cluster by treating it as a physical system. Using **QLoRA REINFORCE** on a Qwen3.5-4B model, the agent is trained to minimize Lyapunov graph energy under a Drift-Plus-Penalty objective that balances stability against infrastructure cost. The trained policy scales predictively, reroutes around failures, and holds the line during traffic surges.
 
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