---
title: Propagationshield Env
emoji: 📉
colorFrom: blue
colorTo: gray
sdk: docker
pinned: false
license: mit
---

# 🛡️ PropagationShield: Halting Hallucination Cascades in Multi-Agent Systems


**PropagationShield** is the first reinforcement learning training environment that teaches LLM agents to detect, flag, and resist hallucinations injected by other agents in a multi-agent pipeline before those hallucinations cascade into a wrong final output. 

### 🔗 Quick Links
* **Live Demo Space:** [PropagationShield-Demo](https://huggingface.co/spaces/pragunk/PropagationShield-Demo)
* **OpenEnv Space:** [propagationshield-env](https://huggingface.co/spaces/pragunk/propagationshield-env)
* **Training Rig Space:** [propagationshield-training](https://huggingface.co/spaces/pragunk/propagationshield-training)
* **Colab Training Notebook:** [Run the Pipeline](https://colab.research.google.com/drive/1FYvcTLn_Et3CXNxX_zhXFudsSEH6Lwaj#scrollTo=NIRnE-F_WfiW)
* * **Demo Video** [Demo Video](https://youtu.be/08_nrGPRLjU)


---

## 1. The Problem: Hallucination Cascades
When large language models are deployed in production pipelines, a critical vulnerability emerges: **hallucination propagation**. 

A single hallucination injected upstream does not stay upstream. For example, in a financial pipeline, if a data-fetching agent hallucinates a revenue figure, the analysis agent builds a report on it, and the output agent sends it to a client without ever questioning the number. Confident wrongness combined with blind downstream trust leads to compounding, catastrophic failures. 

Existing benchmarks only measure whether a model hallucinates on its own; none measure whether a model can detect and resist hallucinations it receives from its environment.

## 2. Environment Innovation & Architecture
To solve this, we built a first-of-its-kind, OpenEnv-compatible testing ground featuring a custom multi-stage pipeline:

* **Targeted Hallucination Engine (Red Team Simulator):** A parameter-driven Python engine that intercepts clean context and algorithmically injects deadly clinical hallucinations (e.g., Factual Omissions, Pediatric Weight Drift, Falsified Lab Results).
* **The Target Agent (Blue Team):** The model being trained (`pragunk/PropagationShield`). It is forced to evaluate upstream data and must output its verdict strictly in a structured JSON `suspicion_flags` schema, justifying which passages it distrusts. 
* **Propagation Cascade Simulator:** An evaluation pipeline that simulates a multi-agent handoff (Retrieval Agent -> Audit Agent). We use this to measure the **Propagation Containment Rate (PCR)**—our novel metric tracking the percentage of injected hallucinations caught before they can corrupt downstream decision-making.

## 3. Training Pipeline & Reward Function
We utilized **Unsloth** for memory-efficient 4-bit quantization and **Hugging Face TRL** to run Group Relative Policy Optimization (GRPO) on a `Qwen2.5-7B-Instruct` model. 

The target agent's reward signal is uniquely formulated to balance two conflicting objectives:
1. **Task Accuracy:** Graded by a deterministic ground-truth verifier.
2. **Hallucination Detection F1:** Rewarding true positive detection flags while penalizing false alarms on clean episodes.
3. **Propagation Penalty:** A harsh penalty applied when the agent gets the answer wrong *and* flags nothing, punishing blind acceptance.



## 4. Showing Reward Improvement

![Training Loss Curve](LossCurve.png)
![Baseline Vs Trained](BaseLineVsTrained.png)

*Note on Logging Artifact: The train/loss curve exhibits a known W&B logging artifact where train loss drops to zero during regular evaluation intervals (steps 5, 10, 15). However, observing the macro-trend of the peaks demonstrates steady convergence, dropping from an initial loss of ~3.5 down to ~2.5 over the run.*

Our training demonstrated dramatic improvements across our three core evaluation metrics:
* **Task Accuracy :** Improved from **62%** to **94%**. The model learned to answer the underlying query correctly despite the presence of adversarial noise.
* **Detection F1 :** Rose drastically from a near-blind **4%** to **87%**, proving the model successfully learned to be epistemically skeptical and accurately flag contradictions.
* **Propagation Containment Rate (PCR):** In our simulated pipeline, the baseline model blindly passed hallucinations downstream 88% of the time. PropagationShield jumped the containment rate from **12%** to **82%**, effectively neutralizing the vast majority of hallucination cascades.

## 5. Why It Matters
PropagationShield is not just a benchmark; it is a defensive training environment. Any team building multi-agent AI systems can plug PropagationShield into their pipeline to produce models that are skeptical by design, halting hallucinations before they can corrupt downstream decision-making. 

---
*Built for the Meta PyTorch OpenEnv Hackathon.*