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@@ -13,45 +13,80 @@ pretty_name: QuantCall Results
 
 # QuantCall Results
 
-Community-submitted benchmark results for the
-[QuantCall benchmark](https://github.com/Happynood/quant-toolcall-bench).
+Real benchmark results for the
+[QuantCall benchmark](https://github.com/Happynood/quant-toolcall-bench),
+measuring how quantization degrades LLM function-calling reliability.
+Every row comes from an actual `quantcall run` execution — no fabricated or
+hand-edited numbers.
 
-**This dataset is currently empty.** Run the benchmark on your GPU and submit a PR
-to populate the leaderboard.
+## Files
 
-## Schema
+| File | Grain | Description |
+|------|-------|--------------|
+| `data/runs.csv` | one row per real run (per seed) | Raw per-seed data with full manifest (git SHA, config/dataset hashes) |
+| `data/leaderboard.csv` | one row per (model, quant, backend, decoding, tier) | Aggregated over seeds, with bootstrap 95% CIs and deltas vs an explicit baseline quant |
 
-Each row represents one benchmark run. File: `data/results.csv`.
+## Schema: `data/runs.csv`
 
 | Column | Type | Description |
 |--------|------|-------------|
 | `model` | string | Model identifier (HF repo ID or local path) |
 | `quant` | string | Quantization level: fp16, Q8_0, Q5_K_M, Q4_K_M, AWQ, GPTQ |
 | `backend` | string | Inference backend: llama-cpp, transformers, vllm, openai |
+| `decoding` | string | Decoding mode: free or constrained |
+| `tier` | string | Dataset tier(s) evaluated, `+`-joined (e.g. `T1+T6`) |
+| `seed` | int | Random seed for this run |
+| `sample_size` | int | Number of instances evaluated per tier |
 | `svr` | float | Schema-Validity Rate [0, 1] |
 | `tsa` | float | Tool-Selection Accuracy [0, 1] |
 | `ac` | float | Argument Correctness [0, 1] |
 | `abstention` | float | Abstention Accuracy [0, 1] |
 | `fcr` | float | Function-Calling Reliability — 0.25 × (SVR + TSA + AC + Abst) |
-| `delta_fcr` | float | FCR degradation vs fp16 baseline (null if no baseline) |
-| `vram_gb` | float | Peak VRAM usage in GB (null if not measured) |
-| `eta` | float | Efficiency: FCR / peak VRAM (null if vram_gb is null) |
+| `vram_gb` | float | Peak VRAM usage in GB for this run (empty if not measured) |
 | `git_commit` | string | QuantCall repo commit SHA used for this run |
-| `config_sha256` | string | SHA-256 of the run config YAML |
+| `config_sha256` | string | SHA-256 of the run config |
 | `dataset_sha256` | string | SHA-256 of the evaluation sample |
-| `tiers` | string | Comma-separated tier list: T0, T1, T2, T3, T4, T5, T6 |
-| `sample_size` | int | Number of instances evaluated per tier |
 | `timestamp` | string | ISO-8601 UTC timestamp of the run |
 
+## Schema: `data/leaderboard.csv`
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `model` | string | Model identifier |
+| `quant` | string | Quantization level |
+| `backend` | string | Inference backend |
+| `decoding` | string | Decoding mode |
+| `tier` | string | Dataset tier(s), `+`-joined |
+| `n_seeds` | int | Number of seeds aggregated into this row |
+| `fcr_mean` | float | Mean FCR across seeds |
+| `fcr_ci_low` | float | Bootstrap 95% CI lower bound for FCR |
+| `fcr_ci_high` | float | Bootstrap 95% CI upper bound for FCR |
+| `svr_mean` | float | Mean SVR across seeds |
+| `tsa_mean` | float | Mean TSA across seeds |
+| `ac_mean` | float | Mean AC across seeds |
+| `abstention_mean` | float | Mean Abstention across seeds |
+| `vram_gb` | float | Mean peak VRAM in GB (empty if not measured) |
+| `eta` | float | Efficiency: fcr_mean / vram_gb (empty if vram_gb is empty) |
+| `delta_fcr_rel` | float | Relative FCR delta vs `baseline_quant` in the same scope; empty for the baseline row itself |
+| `delta_ac_rel` | float | Relative AC delta vs `baseline_quant` |
+| `baseline_quant` | string | The Δ reference quant for this scope — fp16 if it fits and was run, otherwise the best-available quant, always labeled explicitly here |
+
+These two schemas are generated by `quantcall leaderboard <results_dir>`
+(source of truth: `src/quantcall/report/published.py`,
+`docs/RESULTS_SCHEMA.md` in the repo) — this card is kept in sync with that
+code by a repo test (`test_no_schema_drift`).
+
 ## How to Submit
 
 1. Run the benchmark on your hardware following [docs/RUN_REAL.md](https://github.com/Happynood/quant-toolcall-bench/blob/main/docs/RUN_REAL.md).
 2. Verify your `result.json` contains a `manifest` block with git SHA and hashes.
 3. Open a PR on [GitHub](https://github.com/Happynood/quant-toolcall-bench) adding your result file under `results/`.
-4. CI will validate the manifest and regenerate this dataset and the leaderboard.
+4. Run `quantcall leaderboard results/ --output-dir leaderboard/` and include the
+   regenerated `runs.csv` / `leaderboard.csv` in your PR.
 
 ## Links
 
 - GitHub: https://github.com/Happynood/quant-toolcall-bench
-- Eval suite: https://huggingface.co/datasets/Happynood/quantcall-suite
-- Leaderboard: https://huggingface.co/spaces/Happynood/quantcall-leaderboard
+- This dataset: https://huggingface.co/datasets/happynood/quantcall-results
+- Eval suite: https://huggingface.co/datasets/happynood/quantcall-suite
+- Leaderboard (Space): https://huggingface.co/spaces/happynood/quantcall-leaderboard