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** containing
 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.
@@ -240,23 +236,7 @@ database may break differently.
 
 ## Evaluation
 
-The blog post evaluates agents with **Exact Match** over the plan. The
-predicted and ground-truth plans are canonicalized and matched sub-query to
-sub-query. The collection, every filter with its operator and value, every
-aggregation with its metric and property, and the group-by must agree, and
-extra sub-queries or statistics count as errors. A search must be present on
-the right collection, but its words are not compared. A few re-expressions
-that return the same answer on the data are accepted, such as a per-group
-count of any populated single-valued property, or a date bound that excludes
-no record. Answer-level comparison against `reference_results` is a looser
-alternative.
-
-To reproduce the setup, create one Weaviate collection per objects config with
-the data types above and a text vectorizer (the study used
-`text2vec_weaviate` with hybrid search), use each row's `uuid` as the object
-id, and point the agent at an environment's collections. Uploading each run
-under a fresh collection name avoids agents reusing statistics cached from an
-earlier run.
+Placeholder, see `github.com/weaviate/query-agent-benchmarking`.
 
 ### Reference results
 
@@ -300,25 +280,3 @@ each command's phrasing, giving 113 explicit and 137 conversational commands.
   references with over 400 distinct values, which yield degenerate groups.
 - Relevance labels for searches come from a language model reading each
   candidate document, not from human annotation.
-- Search topics can repeat a theme within an environment, and two Tattoo
-  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}
-}
-```