Update README.md

README.md

@@ -25,11 +25,10 @@ task_categories:
 
 This dataset combines historical LLM inference prices with benchmark performance scores to construct the largest publicly available benchmark-level LLM price dataset we are aware of. It covers **100+ models** across three major benchmarks (**GPQA-Diamond**, **SWE-bench Verified**, and **AIME**) over a two-year window from **April 2024 to April 2026**, with varying coverage per benchmark.
 
-The dataset was created to support analysis of how AI capability diffuses over time on a *per-dollar* basis — distinguishing price-independent technical progress from progress driven purely by larger, more expensive models. It is released alongside the paper:
+The dataset was created to support analysis of how AI capability diffuses over time on a *per-dollar* basis — distinguishing price-independent technical progress from progress driven purely by larger, more expensive models. It is released alongside an anonymized NeurIPS 2026 submission.
 
 > **"The Price of Progress: Revisiting Benchmark Progress in AI"**  
-> Hans Gundlach, Jayson Lynch, Matthias Mertens, Neil Thompson  
-> MIT FutureTech — NeurIPS 2026 submission
+> Anonymous Authors — NeurIPS 2026 submission (under review)
 
 ### Key statistics
 
@@ -166,18 +165,7 @@ Benchmark leaderboards report raw accuracy scores, but do not account for the co
 
 ## Citation
 
-If you use this dataset, please cite:
-
-```bibtex
-@inproceedings{gundlach2026priceofprogress,
-  title     = {The Price of Progress: Revisiting Benchmark Progress in AI},
-  author    = {Gundlach, Hans and Lynch, Jayson and Mertens, Matthias and Thompson, Neil},
-  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
-  year      = {2026}
-}
-```
-
-Please also cite the upstream data sources:
+This dataset accompanies an anonymized NeurIPS 2026 submission. A full citation will be provided upon de-anonymization after review. In the meantime, please cite the upstream data sources if you use this dataset:
 
 ```bibtex
 @misc{EpochLLMBenchmarkingHub2024,