Update README.md

README.md

@@ -68,17 +68,10 @@ The Step Probe (`realArceus/twt-probe`) is a fine-tuned `Qwen2.5-0.5B-Instruct`
 
 ## How is this different from PRMs?
 
-Process Reward Models (PRMs) assign quality scores to steps for **training signal**. TWT uses step-level supervision for **real-time inference-time interpretability** — a fundamentally different use case. We also demonstrate this works at 0.5B scale, far smaller than typical PRM deployments.
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-## Paper
-
-*ThinkWhileThinking: Real-Time Reasoning Failure Detection in Small Language Models via Step-Level Process Supervision*
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-**Abstract:** We present ThinkWhileThinking (TWT), a lightweight framework for real-time reasoning failure detection in language models. Unlike post-hoc interpretability methods or safety-focused CoT monitors, TWT employs a small probe model fine-tuned on step-level process supervision signals to predict reasoning failures mid-chain-of-thought, before the final answer is produced. Our Step Probe — a 0.5B parameter classifier trained on PRM800K annotations — achieves 98.15% accuracy and 99.05% F1 on held-out step-level correctness prediction. We demonstrate that real-time correctness failure detection is both feasible and highly accurate at small model scales, opening a new direction for accessible, inference-time interpretability research.
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+Process Reward Models (PRMs) assign quality scores to steps for **training signal**. TWT uses step-level supervision for **real-time inference-time interpretability** — a fundamentally different use case. We also demonstrate this works at 0.5B scale, far smaller than typical PRM 
 ## Built By
 
-**realArceus** — Final year MLE student · HF Build Small Hackathon 2026
+**Krishna Pahuja** — Final year AI student · HF Build Small Hackathon 2026
 
 ## License