Card: add blog/code/collection links and citation; fix models import path

README.md

@@ -21,6 +21,8 @@ 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)
+
 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.
 
 Every topic span in the prompt and the response is wrapped in an inline tag, for example `<Financial>…</Financial>`. Stripping the tags and projecting them onto tokens gives a per-token topic label.
@@ -54,3 +56,13 @@ The splits are the canonical record-level 70/15/15 split (`numpy.random.RandomSt
 ## How it was made
 
 The records were generated with [Simula](https://www.mercity.ai/research/building-simula-our-synthetic-data-generation-library/), Mercity's synthetic data library. A hand-authored taxonomy fixes each record's topics and their order before anything is written. That keeps the topic sequence as the one controlled variable, while the scenario and wording are left to the generator. GPT-5.6 Luna wrote and critiqued every record. `taxonomy.json` and `strategies.json` are the taxonomy and sampling strategies used for the run.
+
+## Citation
+
+```bibtex
+@misc{mallagundla2026topicscope,
+  title  = {Train on prefill, monitor at decode: Using sparse autoencoders for topic monitoring},
+  author = {Mallagundla, Rishikesh and Patel, Pranav},
+  year   = {2026}
+}
+```