Update README.md

README.md

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----
-title: RecallTrace OpenEnv
-emoji: 🚨
-colorFrom: red
-colorTo: blue
-sdk: docker
-app_port: 7860
----
-
-# 🚀 RecallTrace OpenEnv 
-
-RecallTrace is a **real-world AI environment** designed for **product recall tracing and precision containment**.
-
-It simulates how companies handle:
-- contaminated product recalls
-- supply chain tracing
-- selective quarantine decisions
-
-This environment evaluates **agent reasoning + decision-making**, not just correctness.
-
----
-
-# 🧠 What This Environment Does
-
-Given a recall notice (e.g., *"Lot A is contaminated"*), the agent must:
-
-1. Trace where the product went  
-2. Identify affected nodes (warehouses, stores)  
-3. Handle relabeling / transformations  
-4. Quarantine **only unsafe inventory**  
-5. Avoid blocking safe stock  
-6. Notify affected entities  
-7. Finalize with correct containment  
-
----
-
-# 🎯 Why This Is Important
-
-This is a **real industry problem** seen in:
-- food recalls  
-- pharma defects  
-- logistics failures  
-
-Challenges include:
-- Graph traversal  
-- Partial observability  
-- Lot transformations  
-- Mixed inventory reasoning  
-- Precision decision-making  
-
----
-
-# 🧩 Tasks (Scenarios)
-
-## 🔹 Easy — Direct Recall
-- Single contaminated lot  
-- Straight supply chain  
-- Goal: trace and quarantine correctly  
-
----
-
-## 🔹 Medium — Relabeled Inventory
-- Lot gets renamed (LotA → LotA1)  
-- Goal: track transformations and quarantine  
-
----
-
-## 🔹 Hard — Mixed Inventory
-- Contaminated + safe stock mixed  
-- Goal: isolate unsafe quantity **without over-blocking**  
-
----
-
-# ⚙️ Action Space
-
-| Action | Description |
-|------|------------|
-| inspect_node | View inventory at a node |
-| trace_lot | Follow product lineage |
-| quarantine | Block unsafe stock |
-| notify | Inform affected nodes |
-| finalize | End task |
-
----
-
-# 📦 Observation Structure
-
-Each step returns:
-
-- recall_notice  
-- inventory  
-- action history  
-- trace results  
-- inspection data  
-
----
-
-# 🏆 Reward & Grading
-
-### Reward System
-- + Correct tracing  
-- + Correct quarantine  
-- + Correct notification  
-- − Wrong node  
-- − Over-quarantine  
-- − Missed unsafe stock  
-
----
-
-### Final Score
-Range: **0.0 → 1.0**
-
-Based on:
-- accuracy  
-- precision  
-- efficiency  
-
----
-
-# 🧱 Project Structure
-
-```bash
-recalltrace-openenv/
-│
-├── env/                # Environment logic
-│   ├── env.py
-│   └── __init__.py
-│
-├── scenario/           # Scenario generation
-│   └── scenario.py
-│
-├── grader/             # Evaluation + reward
-│   └── grader.py
-│
-├── inference/          # Agent simulation
-│   └── inference.py
-│
-├── config/
-│   └── openenv.yaml
-│
-├── Dockerfile
-├── requirements.txt
-├── README.md
+---
+title: RecallTrace OpenEnv
+emoji: 🚨
+colorFrom: red
+colorTo: blue
+sdk: docker
+app_port: 7860
+---
+
+# 🚀 RecallTrace OpenEnv 
+
+RecallTrace is a **real-world AI environment** designed for **product recall tracing and precision containment**.
+
+It simulates how companies handle:
+- contaminated product recalls
+- supply chain tracing
+- selective quarantine decisions
+
+This environment evaluates **agent reasoning + decision-making**, not just correctness.
+
+---
+
+# 🧠 What This Environment Does
+
+Given a recall notice (e.g., *"Lot A is contaminated"*), the agent must:
+
+1. Trace where the product went  
+2. Identify affected nodes (warehouses, stores)  
+3. Handle relabeling / transformations  
+4. Quarantine **only unsafe inventory**  
+5. Avoid blocking safe stock  
+6. Notify affected entities  
+7. Finalize with correct containment  
+
+---
+
+# 🎯 Why This Is Important
+
+This is a **real industry problem** seen in:
+- food recalls  
+- pharma defects  
+- logistics failures  
+
+Challenges include:
+- Graph traversal  
+- Partial observability  
+- Lot transformations  
+- Mixed inventory reasoning  
+- Precision decision-making  
+
+---
+
+# 🧩 Tasks (Scenarios)
+
+## 🔹 Easy — Direct Recall
+- Single contaminated lot  
+- Straight supply chain  
+- Goal: trace and quarantine correctly  
+
+---
+
+## 🔹 Medium — Relabeled Inventory
+- Lot gets renamed (LotA → LotA1)  
+- Goal: track transformations and quarantine  
+
+---
+
+## 🔹 Hard — Mixed Inventory
+- Contaminated + safe stock mixed  
+- Goal: isolate unsafe quantity **without over-blocking**  
+
+---
+
+# ⚙️ Action Space
+
+| Action | Description |
+|------|------------|
+| inspect_node | View inventory at a node |
+| trace_lot | Follow product lineage |
+| quarantine | Block unsafe stock |
+| notify | Inform affected nodes |
+| finalize | End task |
+
+---
+
+# 📦 Observation Structure
+
+Each step returns:
+
+- recall_notice  
+- inventory  
+- action history  
+- trace results  
+- inspection data  
+
+---
+
+# 🏆 Reward & Grading
+
+### Reward System
+- + Correct tracing  
+- + Correct quarantine  
+- + Correct notification  
+- − Wrong node  
+- − Over-quarantine  
+- − Missed unsafe stock  
+
+---
+
+### Final Score
+Range: **0.0 → 1.0**
+
+Based on:
+- accuracy  
+- precision  
+- efficiency  
+
+---
+
+# 🧱 Project Structure
+
+```bash
+recalltrace-openenv/
+│
+├── env/                # Environment logic
+│   ├── env.py
+│   └── __init__.py
+│
+├── scenario/           # Scenario generation
+│   └── scenario.py
+│
+├── grader/             # Evaluation + reward
+│   └── grader.py
+│
+├── inference/          # Agent simulation
+│   └── inference.py
+│
+├── config/
+│   └── openenv.yaml
+│
+├── Dockerfile
+├── requirements.txt
+├── README.md
+
+
+
+
+## 📊 Baseline Scores
+
+| Task | Difficulty | Score |
+|------|------------|-------|
+| phase1_direct_recall | Easy | 0.72 |
+| phase2_relabel_recall | Medium | 0.51 |
+| phase3_mixed_shipments | Hard | 0.34 |
+
+Average baseline score: **0.52**