update dataset card

README.md

@@ -95,31 +95,8 @@ This HuggingFace dataset hosts the **task corpus only** — the agent-facing ins
 
 The reference yields used by the scorer are deliberately not published here to preserve the benchmark's blind-test property.
 
-## Quick start
 
-```python
-from datasets import load_dataset
-
-ds = load_dataset("Dariusfar/ColliderBench", split="train")
-print(ds)                                  # 10 sim tasks
-print(ds[0]["task_id"], ds[0]["paper_id"])
-print(ds[0]["task_md"][:400])              # what the agent gets shown
-print(ds[0]["template_yaml"][:400])        # the null-filled template
-print(ds[0]["paper_pdf"][:8])              # PDF magic bytes (b'%PDF-1.5')
-```
-
-To actually run an agent against a task and score its submission, install the harness from the GitHub repo:
-
-```bash
-git clone https://github.com/dfaroughy/Collider-Bench.git
-cd Collider-Bench
-pip install -e ".[dev]"
-podman pull ghcr.io/dfaroughy/lhc-bench:latest      # MadGraph + Pythia + Delphes + ROOT image
-export ANTHROPIC_API_KEY=...                         # or OPENAI_API_KEY / GEMINI_API_KEY / DEEPSEEK_API_KEY
-scripts/run-agent --config configs/anthropics/claude_sonnet.yaml --task sus-16-046_sim-T5Wg
-```
-
-## Schema (one row per task)
+## Task schema
 
 | Field | Type | Description |
 |---|---|---|
@@ -159,15 +136,6 @@ scripts/run-agent --config configs/anthropics/claude_sonnet.yaml --task sus-16-0
 | `sus-16-051_sim-T2bW_SRG`      | single lepton  | `T2bW`                | $E_T^{\rm miss}$ | CMS-SUS-16-051 |
 | `sus-16-051_sim-T2tt_comp`     | single lepton  | `T2tt`, compressed    | $E_T^{\rm miss}$ | CMS-SUS-16-051 |
 
-## Scoring
-
-Each `sim` task asks the agent to reproduce the published per-bin yield distribution. The primary metric used by the scorer is the relative L² distance
-
-$$d(\hat y, y^\star) = \sqrt{\sum_k (\hat y_k - y_k^\star)^2 \big/ \sum_k (y_k^\star)^2}$$
-
-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.
 
 ## Citation