Sync from https://github.com/JonPark0/contilab (1f888dd)

README.md

@@ -39,7 +39,7 @@ calibration, representation metrics, memory and wall time.
 | `cpu_tuned.csv` | `cpu_tuned` | 456 | Tuned comparison: per-method grids on the two MNIST benchmarks, variant chosen on validation accuracy (`selected`) | `cpu_tuned.yaml` |
 | `cpu_student.csv` | `cpu_student` | 12 | Teacher → smaller student: distillation into a 2×100 MLP | `cpu_student.yaml` |
 
-The main study has 324 runs: 312 done, 12 diverged, 0 failed.
+The main study has 324 runs: 320 done, 4 diverged, 0 failed.
 Seeds per benchmark: Split MNIST (Class-IL) 6, Split MNIST (Task-IL) 6, Permuted MNIST (Domain-IL) 6, Split CIFAR-10 (Class-IL) 3, Split CIFAR-100 (Class-IL) 3, text5 + Pythia-70M (Class-IL) 3.
 
 ```python
@@ -54,28 +54,30 @@ main = load_dataset("jonpark0/contilab-cpu-study", "cpu_main", split="train")
   `seed`, `status` (`done`, `diverged` or `failed`). `cpu_tuned` adds `selected` (the variant chosen on validation
   accuracy) and `cell` (the comparison-axis part of the variant).
 - **Metrics:** every other column. Definitions and references are on the [Metrics](https://jonpark0.github.io/contilab/wiki/Metrics.html)
-  page. An empty cell means the metric is undefined for that run, for example forward transfer in Class-IL, stream
-  metrics for consolidation methods, or anything for a diverged run.
+  page. An empty cell means the metric is undefined for that run, for example forward transfer in Class-IL or stream
+  metrics for consolidation methods. Diverged runs keep their metrics, computed on the model whose parameters
+  stopped changing at the divergence.
 
 ## Final accuracy (cpu_main, mean over seeds)
 
 | Method | Split MNIST (Class-IL) | Split MNIST (Task-IL) | Permuted MNIST (Domain-IL) | Split CIFAR-10 (Class-IL) | Split CIFAR-100 (Class-IL) | text5 + Pythia-70M (Class-IL) |
 |---|---|---|---|---|---|---|
-| Naive | 0.196 | 0.980 | 0.805 | 0.181 | 0.049 | 0.097 |
-| Joint (upper bound) | 0.950 | 0.993 | 0.909 | 0.398 | 0.118 | 0.529 |
-| EWC | 0.197 | 0.983 | diverged | diverged | diverged | 0.105 |
-| LwF | 0.228 | 0.990 | 0.817 | 0.173 | 0.047 | 0.105 |
-| Experience Replay | 0.858 | 0.983 | 0.869 | 0.302 | 0.059 | 0.329 |
-| DER++ | 0.871 | 0.991 | 0.902 | 0.256 | 0.059 | 0.397 |
-| A-GEM | 0.335 | 0.984 | 0.864 | 0.181 | 0.048 | 0.100 |
-| LoRA-per-task | 0.184 | 0.986 | 0.619 | 0.137 | 0.015 | 0.024 |
-| Task Arithmetic | 0.531 | 0.979 | 0.570 | 0.158 | 0.011 | 0.034 |
-| TIES-Merging | 0.561 | 0.983 | 0.677 | 0.166 | 0.013 | 0.033 |
-| Multi-teacher distillation | 0.863 | 0.986 | 0.773 | 0.262 | 0.040 | 0.452 |
-| Continual distillation | 0.586 | 0.980 | 0.818 | 0.210 | 0.030 | 0.331 |
-
-"diverged" marks a benchmark where every seed of the method diverged (its loss stopped being finite); such runs
-are kept with `status = diverged`.
+| Naive | 0.196 | 0.980 | 0.805 | 0.182 | 0.046 | 0.097 |
+| Joint (upper bound) | 0.950 | 0.993 | 0.909 | 0.462 | 0.120 | 0.529 |
+| EWC | 0.196 | 0.981 | 0.832 | 0.165 | diverged | 0.105 |
+| LwF | 0.228 | 0.990 | 0.817 | 0.171 | 0.047 | 0.105 |
+| Experience Replay | 0.858 | 0.983 | 0.869 | 0.290 | 0.057 | 0.329 |
+| DER++ | 0.871 | 0.991 | 0.902 | 0.285 | 0.063 | 0.397 |
+| A-GEM | 0.335 | 0.984 | 0.864 | 0.180 | 0.044 | 0.100 |
+| LoRA-per-task | 0.184 | 0.986 | 0.619 | 0.129 | 0.015 | 0.024 |
+| Task Arithmetic | 0.531 | 0.979 | 0.570 | 0.151 | 0.011 | 0.034 |
+| TIES-Merging | 0.561 | 0.983 | 0.677 | 0.160 | 0.013 | 0.033 |
+| Multi-teacher distillation | 0.863 | 0.986 | 0.773 | 0.267 | 0.040 | 0.452 |
+| Continual distillation | 0.586 | 0.980 | 0.818 | 0.229 | 0.030 | 0.331 |
+
+Means are over the seeds that did not diverge. "diverged" marks a benchmark where every seed of the method
+diverged (its loss became non-finite or exceeded 1000 × its first value); such runs are kept with
+`status = diverged`.
 
 ## How the data were produced