update dataset card

README.md

@@ -1,39 +1,35 @@
 ---
 license: mit
 task_categories:
-- text-generation
-- question-answering
+  - text-generation
+  - question-answering
 language:
-- en
+  - en
 tags:
-- physics
-- high-energy-physics
-- particle-physics
-- LHC
-- CMS
-- benchmark
-- agentic
-- llm-agents
-- tool-use
-- simulation
+  - physics
+  - high-energy-physics
+  - particle-physics
+  - LHC
+  - CMS
+  - benchmark
+  - agentic
+  - llm-agents
+  - tool-use
+  - simulation
 pretty_name: Collider-Bench
 size_categories:
-- n<1K
+  - n<1K
 ---
 
 # Collider-Bench
 
-**Collider-Bench** Collider-Bench is an AI benchmark for evaluating whether LLM agents can reproduce experimental analyses from the **Large Hadron Collider** (LHC) at CERN using only public papers and open scientific software.
+**Collider-Bench** is a benchmark for evaluating whether LLM agents can reproduce experimental analyses from the Large Hadron Collider (LHC) using only public papers and open scientific software. Such analyses are often difficult to reproduce because the public toolchain only approximates the software used internally by the experimental collaborations, while the published papers inevitably omit implementation details needed for a faithful reconstruction. Agents must therefore rely on physical reasoning, domain knowledge, and trial-and-error to fill these gaps. Each task requires the agent to turn a published analysis into an executable simulation-and-selection pipeline and submit predicted collision event yields in specified signal regions.
 
-Each task requires multi-step scientific reasoning by an autonomous coding agent, from reading a published CMS or ATLAS search and identifying the relevant signal region, to generating and processing simulated signal events, implementing the event selection, and predicting the binned signal yields reported by the analysis
-
-The benchmark tests long-horizon scientific reasoning under realistic conditions, including ambiguous or underspecified paper descriptions, underdocumented domain-specific tools and approximate public simulation pipelines.
-
-This HuggingFace dataset hosts the **task corpus only** — the agent-facing instructions and artifacts. The **runtime harness, scorer, and hidden reference values** live in the companion GitHub repository:
+This HuggingFace dataset hosts the **task corpus only** — the agent-facing instructions, the null-filled HEPData-style template the agent fills, the CMS paper PDF, and the published object-efficiency maps. The **runtime harness, scorer, and hidden reference values** live in the companion GitHub repository:
 
 🔗 **https://github.com/dfaroughy/Collider-Bench**
 
-The reference yields used by the scorer are deliberately not published here to preserve the benchmark's blind-test property.
+The reference yields used by the scorer are deliberately not published here — leaking them would let any LLM ingesting HF datasets memorize the answers and defeat the benchmark's blind-test property.
 
 ## Quick start
 
@@ -105,7 +101,9 @@ Each `sim` task asks the agent to reproduce the published per-bin yield distribu
 
 $$d(\hat y, y^\star) = \sqrt{\sum_k (\hat y_k - y_k^\star)^2 \big/ \sum_k (y_k^\star)^2}$$
 
-between the agent's bin yields $\hat y$ and the published reference $y^\star$. Scoring is offline and deterministic — it does **not** require an LLM. See [`ColliderBench/Evals/`](https://github.com/dfaroughy/Collider-Bench/tree/main/ColliderBench/Evals) in the harness repo.
+between the agent's bin yields $\hat y$ and the published reference $y^\star$, plus the integrated yield error $\Delta = |\Sigma\hat y - \Sigma y^\star| / \Sigma y^\star$. Diagnostic metrics (RMSLE, Jensen-Shannon, Baker-Cousins shape p-value) are also computed per run.
+
+Scoring is offline and deterministic — it does **not** require an LLM. See [`ColliderBench/Evals/`](https://github.com/dfaroughy/Collider-Bench/tree/main/ColliderBench/Evals) in the harness repo.
 
 ## Citation
 
@@ -113,13 +111,15 @@ If you use Collider-Bench in your research, please cite:
 
 ```
 @misc{colliderbench2026,
-  title  = {Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction},
+  title  = {Collider-Bench: A benchmark for LHC analysis recasting by LLM agents},
   author = {Faroughy, Darius A. and contributors},
   year   = {2026},
   url    = {https://huggingface.co/datasets/Dariusfar/ColliderBench},
 }
 ```
 
+…and the four underlying CMS papers (CMS-SUS-16-034, -046, -047, -051) as listed in the GitHub repo's [References section](https://github.com/dfaroughy/Collider-Bench#references).
+
 ## License
 
-MIT (matches the GitHub repo). The CMS paper PDFs and detector efficiency maps are reproduced here as published by the CMS Collaboration under the terms of their respective public-data policies.
+MIT (matches the GitHub repo). The CMS paper PDFs and detector efficiency maps are reproduced here as published by the CMS Collaboration under the terms of their respective public-data policies.