docs: add concrete statistics and cite arXiv:2601.19100

README.md

@@ -18,7 +18,7 @@ An OpenEnv-compliant RL training environment where AI agents act as **FDA Lead R
 
 ## 🛑 The Verification Gap
 
-The scientific replication crisis affects an estimated 50–70% of published research. In clinical trials, a methodological flaw or undisclosed data exclusion isn't just a statistical error—it costs lives.
+The scientific replication crisis affects an estimated 50–70% of published research. In clinical trials, a methodological flaw or undisclosed data exclusion isn't just a statistical error—it costs lives. Over 10,000 biomedical papers have been retracted in the last decade, but only after potentially affecting **hundreds of thousands of enrolled patients** and leading to dangerous, wide-scale drug approvals (Source: *RetractionWatch / BMJ*). 
 
 Current LLMs are incredible at *generation* but fail catastrophically at rigorous, multi-step *verification*. When presented with a complex clinical protocol and raw CSV data, baseline models hallucinate, struggle to follow strict reporting schemas, and fail to independently verify biostatistical claims.
 
@@ -26,6 +26,14 @@ Current LLMs are incredible at *generation* but fail catastrophically at rigorou
 
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+## 📚 Citations & Inspiration
+
+Our deterministic RLVR reward shaping strategies and verifiable environment design are heavily inspired by recent advancements in Reinforcement Learning for verifiable reasoning.
+
+*   **Reward Ideas:** Inspired by [arXiv:2601.19100](https://arxiv.org/abs/2601.19100).
+
+---
+
 ## 📦 Submission Artifacts
 
 | Artifact | Link |

docs/HFBlogPost.md

@@ -6,10 +6,12 @@
 
 When building AI agents to handle high-stakes regulatory tasks—like auditing clinical trials or reviewing New Drug Applications (NDAs)—the industry relies heavily on "LLM-as-a-Judge" evaluation. 
 
-The problem? LLMs are easily manipulated soft graders. They hallucinate partial credit, struggle with strict biostatistics, and can be fooled by polite, confident formatting. In a software demo, that’s an edge case. At the FDA, a hallucinated drug approval costs lives.
+The problem? LLMs are easily manipulated soft graders. They hallucinate partial credit, struggle with strict biostatistics, and can be fooled by polite, confident formatting. In a software demo, that’s an edge case. At the FDA, a hallucinated drug approval costs lives. Over 10,000 biomedical papers have been retracted in the last decade, but only after potentially affecting **hundreds of thousands of enrolled patients** and leading to dangerous, wide-scale drug approvals (Source: *RetractionWatch / BMJ*). 
 
 For the Meta PyTorch × Scaler Hackathon, our team decided to kill the soft-grading system. We built **PeerGuard**, an autonomous Review Board agent trained entirely inside a deterministic OpenEnv sandbox. If the math is wrong, the agent gets a zero. No partial credit. No vibes. Just Reinforcement Learning with Verifiable Rewards (RLVR).
 
+Our RLVR reward shaping strategies and verifiable environment design are heavily inspired by recent advancements in Reinforcement Learning, specifically the reward ideas outlined in [arXiv:2601.19100](https://arxiv.org/abs/2601.19100).
+
 Here is how we built it, why our early training runs collapsed, and how the agent achieved zero-shot generalization on raw CSV data.
 
 ## The Architecture: Deterministic Sandboxing