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@@ -158,12 +158,36 @@ local GPU (ROCm), the GBMs on CPU. Reproduce with `benchmark/benchmark.py`.
 
 ## TabArena
 
-FelaTab is wrapped as an AutoGluon model (`benchmark/tabarena/fela_ag_model.py`) and runnable
-against the official [TabArena](https://huggingface.co/spaces/TabArena/leaderboard) leaderboard
-with `benchmark/tabarena/run_tabarena.py`. On the 3-dataset quickstart subset FelaTab-big scored
-ELO ~938 (small ~897), mid-pack alongside default XGBoost, with 33% of tasks imputed (regression
-tasks are out of scope for the classification wrapper). Full TabArena-Lite / full-benchmark runs
-are in progress; see `benchmark/RUNNING.md`.
+FelaTab ran the **full TabArena v0.1 suite** (51 datasets, all CV splits, 2448 task-fits,
+0% imputed) with `benchmark/tabarena/run_tabarena.py` against the official
+[TabArena](https://huggingface.co/spaces/TabArena/leaderboard) cached leaderboard
+(83 method configs). Classification is 100% native FelaTab, zero-shot; regression tasks are
+delegated to a LightGBM regressor inside the wrapper (composite system — FelaTab's native
+regression head benchmarks below default RandomForest today; the delegate mirrors how the
+model ships in production).
+
+| Config | Elo | Avg rank |
+|---|---|---|
+| FelaTab big, bagged (`n_bag=8`) | **985** | 75 / 83 |
+| FelaTab big (411.9M params) | **972** | 76 / 83 |
+| FelaTab small (51.6M params) | **893** (default = small tier in this run) | 80 / 83 |
+
+The bagged config (p-value 0.044) permutes row + column order over `n_bag=8` support
+subsets and averages the predictions — measured +1.4 AUC on adult over a single pass.
+An earlier 2-config run (big+small, 1632 tasks) landed 913 / 896 and is still in the
+results repo (`eval/felatab_full/`).
+
+Reference points from the same leaderboard: XGBoost (default) 1203, RandomForest (default)
+1000, Linear (tuned) 938, KNN (tuned) 890; TabPFNv2-family and tuned GBMs sit at 1300–1900 on
+GPU. FelaTab's pitch is not the Elo column: it is the only zero-shot entry that runs
+CPU-native (everything above it is GPU or trained per-dataset), at 1/32 the parameter count of
+comparable tabular FMs, with the int8 tier deployable in-browser (WASM) and inside Postgres.
+
+Raw per-task results, the full leaderboard table, and reproducibility scripts live in
+[lowdown-labs/fela-tab-tabarena-results](https://huggingface.co/datasets/lowdown-labs/fela-tab-tabarena-results)
+(`raw/` = per-fold results.pkl, `eval/felatab_full/` = merged leaderboard + figures).
+Reproduce with `benchmark/tabarena/launch_ec2.py` + `ec2_run.sh` (~$70 on one
+c7i.8xlarge, sharded across two).
 
 ## Regression (5 datasets)