README.md

metadata

title: RecallTrace OpenEnv
emoji: 🚨
colorFrom: red
colorTo: blue
sdk: docker
app_port: 7860
pinned: false

🚀 Quick Start (Run in one command)

pip install -r requirements.txt
python run_selfplay.py

(No API keys, no GPUs, runs in <2 seconds on CPU)

RecallTrace: Causal Inference via Adversarial Self-Play

An RL agent that doesn't just learn to detect contamination — it learns to infer the hidden causal intervention behind it.

Trained via adversarial self-play, where an adversary learns to hide better as the investigator learns to reason better.

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🎥 What you'll see

• Agent improves from random (spray-and-pray) to precise, belief-calibrated quarantine.
• F1 score increases to ~1.0 over 200 episodes.
• Nodes quarantined drops from 8.3/episode to 3.1/episode.
• Adversary adapts to agent weaknesses dynamically.

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📊 Proof of Learning

1. The Learning Curves

(Generated automatically when you run the script)

2. Before vs After Behavior

(Untrained vs Trained Agent Comparison)

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🧠 Why This Is Unique

1. Causal Inference (not Graph Traversal): 30-50% of the graph edges are hidden. The agent must perform abductive reasoning to identify which hidden causal intervention (relabeling, mixing, record deletion) produced the observed contamination pattern.
2. Partial Observability: The agent relies on a probabilistic belief state (P(contaminated) per node) and tool calls to reduce entropy.
3. Adversarial Self-Play (Theme 4): The environment's difficulty is not static. An adversary agent chooses where to place interventions, adapting its curriculum based on the investigator's failure modes.
4. Belief-Based Decisions (Theme 3.1): Quarantines are only rewarded if the agent is confident (P > 0.8). Uncalibrated guesses are heavily penalized.

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⚙️ How It Works

• The Environment: A procedural generator builds a unique contamination propagation graph every episode with decoys, false positives, and hidden interventions.
• The Investigator (Agent 1): Inspects nodes, traces lineages, and cross-references data to find contamination and quarantine it. Rewarded for precision and recall (+2.0 for correct, -1.5 for incorrect).
• The Adversary (Agent 2): Chooses intervention types and placements. Rewarded exclusively when the Investigator fails.

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🧪 Reproducibility

• Runs in <2 seconds on CPU.
• No external APIs or heavy models required.
• Deterministic seeds used for exact evaluation and metric reproducibility.

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📦 Project Structure

recalltrace-openenv/
├── run_selfplay.py        # ENTRY POINT
├── app.py                 # Hugging Face Gradio UI
├── README.md              # Project Story
├── PITCH.md               # 3-Minute Mentor Pitch Script
├── MENTOR_PREP.md         # Fast-prep for live judging
├── PITCH_LANGUAGE.md      # Language guidelines
├── architecture.html      # Visual Flow Diagram
│
├── selfplay/              # Core Logic (Investigator, Adversary, Tracker)
├── env/                   # Original OpenEnv Environment definition
│
├── plots/                 # Auto-generated Demo Imagery
│   ├── selfplay_training.png
│   ├── before_after_demo.png
│   └── episode_comparison.png

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.

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🧠 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

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🎯 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

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🧩 Tasks (Scenarios)

🔹 Easy — Direct Recall

• Single contaminated lot
• Straight supply chain
• Goal: trace and quarantine correctly

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🔹 Medium — Relabeled Inventory

• Lot gets renamed (LotA → LotA1)
• Goal: track transformations and quarantine

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🔹 Hard — Mixed Inventory

• Contaminated + safe stock mixed
• Goal: isolate unsafe quantity without over-blocking

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⚙️ 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

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📦 Observation Structure

Each step returns:

• recall_notice
• inventory
• action history
• trace results
• inspection data

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🏆 Reward & Grading

Reward System

• • Correct tracing
• • Correct quarantine
• • Correct notification
• − Wrong node
• − Over-quarantine
• − Missed unsafe stock

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Final Score

Range: 0.0 → 1.0

Based on:

• accuracy
• precision
• efficiency

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🧱 Project Structure

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

🧠 What the agent learns

• Early: quarantines 6–8 nodes randomly (F1 ~0.3)
• Mid: starts identifying patterns (F1 ~0.6)
• Late: infers intervention type before acting (F1 ~0.8)

The agent does not memorize — it infers hidden causal events under partial observability.