Update README.md

README.md

@@ -97,13 +97,9 @@ configs:
 # Query Agent Gym
 
 Query Agent Gym is a set of procedurally generated database environments and
-natural-language queries with exactly known ground-truth queries, built to
-evaluate data agents that answer questions by writing database queries, such
-as the [Weaviate Query Agent](https://docs.weaviate.io/agents/query). How the
-environments and queries are generated is described in the blog post
-[Query Agent Gym: Post-Training our Query Agent](https://weaviate.io/blog/query-agent-gym).
+natural-language queries with exactly known ground-truth queries.
 
-This release holds the five **held-out test environments** of that study:
+This release holds five **held-out test environments**:
 15 collections with 1,000 objects each (15,000 objects, 18,000 searchable
 documents) and 250 queries, 50 per environment. Every entity, name, and
 document is synthetic.
@@ -122,14 +118,14 @@ document is synthetic.
 from datasets import load_dataset
 import json
 
-queries = load_dataset("<org>/query-agent-gym", "queries", split="test")
+queries = load_dataset("weaviate/query-agent-gym", "queries", split="test")
 surf = queries.filter(lambda r: r["environment"] == "surf-schools")
 q = queries[0]
 print(q["command"])                     # the natural-language request
 print(q["ground_truth_sql"])            # readable ground truth, one entry per sub-query
 plan = json.loads(q["ground_truth"])    # exact ground truth (query_database arguments)
 
-routes = load_dataset("<org>/query-agent-gym", "public-transit_TransitRoutes", split="test")
+routes = load_dataset("weaviate/query-agent-gym", "public-transit_TransitRoutes", split="test")
 ```
 
 ## Layout
@@ -304,21 +300,3 @@ each command's phrasing, giving 113 explicit and 137 conversational commands.
   Studios design searches share the phrase "melting snow".
 - The query sets exclude what the Weaviate Query Agent cannot express, such as
   aggregating over the results of a semantic search.
-
-## Citation
-
-```bibtex
-@misc{query_agent_gym_2026,
-  title  = {Query Agent Gym: Post-Training our Query Agent},
-  author = {Shorten, Connor},
-  year   = {2026},
-  url    = {https://weaviate.io/blog/query-agent-gym}
-}
-
-@article{shorten2025querying,
-  title   = {Querying Databases with Function Calling},
-  author  = {Shorten, Connor and others},
-  journal = {arXiv preprint arXiv:2502.00032},
-  year    = {2025}
-}
-```