docs: refresh README run examples and make them copy-friendly

Replace stale example model (openai/o3) and year (2024) with current
ones, put each command in its own code block for easy copying, and add
more usage examples: question-ID filter, --num_runs variance, temperature
override, a JEE Advanced run, and an Analyse results section covering the
leaderboard and aggregate_runs.py.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

README.md

@@ -80,25 +80,88 @@ image = example["image"]                                  # PIL image
 correct = json.loads(example["correct_answer"])           # e.g. ["A"], ["B", "C"], ["42"]
 ```
 
-### Run the benchmark
+### Setup
 
 ```bash
 git clone https://huggingface.co/datasets/Reja1/jee-neet-benchmark
 cd jee-neet-benchmark
-git lfs pull                       # fetch images + metadata (stored in Git LFS)
+```
+
+```bash
+git lfs pull          # fetch images + metadata (stored in Git LFS)
+```
+
+```bash
 uv sync
+```
+
+```bash
 echo "OPENROUTER_API_KEY=your_key" > .env
+```
+
+### Run the benchmark
 
-# Evaluate a vision-capable model on the full dataset
+Evaluate a vision-capable model on the full dataset:
+
+```bash
 uv run python src/benchmark_runner.py --model "google/gemini-3.1-pro-preview"
+```
+
+Run a single exam and year:
+
+```bash
+uv run python src/benchmark_runner.py --model "openai/gpt-5.5" --exam_name NEET --exam_year 2026
+```
+
+```bash
+uv run python src/benchmark_runner.py --model "anthropic/claude-opus-4.7" --exam_name JEE_ADVANCED --exam_year 2026
+```
+
+Run only specific questions (comma-separated IDs):
+
+```bash
+uv run python src/benchmark_runner.py --model "openai/gpt-5.5" --question_ids "N24T3001,JA26P1M01"
+```
+
+Run a model 3 times for variance analysis:
+
+```bash
+uv run python src/benchmark_runner.py --model "x-ai/grok-4.3" --exam_name NEET --exam_year 2026 --num_runs 3
+```
+
+Override the sampling temperature from the config:
 
-# Filter by exam / year, resume a run, or re-score after an answer-key update
-uv run python src/benchmark_runner.py --model "openai/o3" --exam_name NEET --exam_year 2024
-uv run python src/benchmark_runner.py --model "openai/o3" --resume results/<run_dir>
-uv run python src/benchmark_runner.py --score-only results/<run_dir>     # no API calls
+```bash
+uv run python src/benchmark_runner.py --model "openai/gpt-5.5" --temperature 0.7
+```
+
+Resume an interrupted run (skips already-completed questions):
+
+```bash
+uv run python src/benchmark_runner.py --model "openai/gpt-5.5" --resume results/<run_dir>
+```
+
+Re-score an existing run after updating the answer key — no API calls:
+
+```bash
+uv run python src/benchmark_runner.py --score-only results/<run_dir>
+```
+
+### Analyse results
+
+Build a cross-model leaderboard from all local results:
+
+```bash
+uv run python scripts/generate_leaderboard.py
+```
+
+Aggregate repeated runs of one model for variance stats:
+
+```bash
+uv run python scripts/aggregate_runs.py --pattern "x-ai_grok-4.3_NEET_2026"
 ```
 
-Configure the model list and parameters in `configs/benchmark_config.yaml`; run with `--help` for all options (`--question_ids`, `--num_runs`, `--temperature`, …). Each run writes a timestamped folder under `results/` containing `predictions.jsonl` (raw responses), `summary.jsonl` (per-question scores, tokens, cost, latency), and `summary.md` (human-readable report). Build a cross-model leaderboard with `uv run python scripts/generate_leaderboard.py`.
+Configure the model list and parameters in `configs/benchmark_config.yaml`; run `src/benchmark_runner.py --help` for the full option list. Each run writes a timestamped folder under `results/` with `predictions.jsonl` (raw responses), `summary.jsonl` (per-question scores, tokens, cost, latency), and `summary.md` (human-readable report).
 
 ## Scoring