Card: link the alphaXiv paper

README.md

@@ -21,7 +21,7 @@ configs:
 
 # Topic Shift 10k
 
-📝 [Blog post](https://www.mercity.ai/research/online-topic-guardrails-with-saes/) · 💻 [Code](https://github.com/Mercity-AI/SAE-Guardrails) · 🤗 [Collection](https://huggingface.co/collections/Mercity/online-guardrails-using-sparse-autoencoders-6ac602940468c62fefb2b9e8)
+📄 [Paper](https://www.alphaxiv.org/pdf/2611.topicscope) · 📝 [Blog post](https://www.mercity.ai/research/online-topic-guardrails-with-saes/) · 💻 [Code](https://github.com/Mercity-AI/SAE-Guardrails) · 🤗 [Collection](https://huggingface.co/collections/Mercity/online-guardrails-using-sparse-autoencoders-6ac602940468c62fefb2b9e8)
 
 10,000 synthetic single-turn conversations for per-token topic detection. Each record is a short user prompt and an assistant response of about 600 words that covers one to four business topics in a fixed order. The response moves from one topic to the next in ordinary prose, with no heading to mark the change.
 
@@ -63,6 +63,7 @@ The records were generated with [Simula](https://www.mercity.ai/research/buildin
 @misc{mallagundla2026topicscope,
   title  = {Train on prefill, monitor at decode: Using sparse autoencoders for topic monitoring},
   author = {Mallagundla, Rishikesh and Patel, Pranav},
-  year   = {2026}
+  year   = {2026},
+  url    = {https://www.alphaxiv.org/pdf/2611.topicscope}
 }
 ```