Update README.md

README.md

@@ -25,31 +25,62 @@ emb = model.encode(["a cat on a windowsill", "a feline by the window"])   # (2,
 (emb[0] @ emb[1]).item()
 ```
 
-## Results
-
-Measured in one harness; every model mean-pooled and L2-normalized, no task
-tuning. `erank` is the participation ratio of the embedding spectrum -- how many
-of the 768 directions are actually used.
-
-| model | params | STS-B rho | SICK-R rho | self_cos | erank |
-|---|---|---|---|---|---|
-| bert-base | 109.5M | .4729 | .5865 | +.580 | 32.0 |
-| ModernBERT-base | 149.0M | .4215 | .5479 | +.948 | -- |
-| roberta-base | 124.6M | .5436 | .6296 | +.976 | -- |
-| albert-base-v2 | 11.7M | .4784 | .5364 | +.905 | -- |
-| distilbert | 66.4M | .5717 | .6424 | +.840 | -- |
-| **captionbert-8192-v2** | **58.3M** | **.5747** | **.6526** | **+.129** | 33.4 |
-| all-MiniLM-L6-v2 (ref) | 22.7M | .8203 | .7758 | +.023 | 94.3 |
-
-**It edges every teacher it was distilled from**, at 13% of their combined
-parameters, having never seen a similarity label. The bare trunk does not reach
-`all-MiniLM-L6-v2` (1B+ curated pairs, a different comparison class); with the
-3-arm AMOE collective below, STS-B closes to **.7684 vs .8203** and SICK-R to
-**.7391 vs .7758**.
-
-**Isotropy is the mechanism.** Mean-pooled BERT-family embeddings sit in a narrow
-cone (self_cos .58-.98); this model reads **+.129**, and cosine discriminates far
-better in a space that is not collapsed.
+## Benchmark
+
+| model | params | STS-B | SICK-R | STS12 | STS13 | STS14 | STS15 | STS16 | BIOSSES | mean |
+|---|---|---|---|---|---|---|---|---|---|---|
+| bert-base | 109.5M | 0.4729 | 0.5865 | 0.3087 | 0.5988 | 0.4773 | 0.6029 | 0.6373 | 0.5469 | 0.5289 |
+| ModernBERT-base | 149.0M | 0.4215 | 0.5479 | 0.3527 | 0.4247 | 0.3795 | 0.5349 | 0.4174 | 0.5630 | 0.4552 |
+| roberta-base | 124.6M | 0.5436 | 0.6296 | 0.3211 | 0.5631 | 0.4522 | 0.6134 | 0.6198 | 0.5777 | 0.5401 |
+| albert-base-v2 | 11.7M | 0.4784 | 0.5364 | 0.3101 | 0.4831 | 0.3809 | 0.5542 | 0.5491 | 0.4863 | 0.4723 |
+| distilbert | 66.4M | 0.5717 | 0.6424 | 0.4344 | 0.6490 | 0.5410 | 0.6663 | 0.6854 | 0.5162 | 0.5883 |
+| **captionbert-8192-v2** | 58.3M | **0.5747** | **0.6526** | **0.5051** | **0.5995** | **0.5452** | **0.7136** | **0.6776** | **0.5933** | **0.6077** |
+| **captionbert-8192-v2 + arms** | 63.2M | **0.7684** | **0.7391** | **0.6682** | **0.7557** | **0.6921** | **0.8055** | **0.7626** | **0.6382** | **0.7287** |
+| captionbert-8192-b | 58.3M | 0.5752 | 0.6548 | 0.5012 | 0.6037 | 0.5470 | 0.7146 | 0.6782 | 0.5500 | 0.6031 |
+| all-MiniLM-L6-v2 | 22.7M | 0.8203 | 0.7758 | 0.7237 | 0.8058 | 0.7559 | 0.8539 | 0.7899 | 0.8144 | 0.7925 |
+
+All ten models measured in **one harness**, same eight tasks, **mean-pooled and
+L2-normalized**, no task tuning. Spearman correlation; `mean` is the unweighted
+average over the eight.
+
+`all-MiniLM-L6-v2` was contrastively trained on 1B+ curated sentence pairs. It is
+listed for scale, not as a peer -- nothing here saw a similarity label.
+
+**The trunk beats every teacher it was distilled from**, and the best of them
+(distilbert, .5883) by +.0194 -- at **13% of their combined 461M parameters**,
+having never seen a similarity label. The margin comes mostly from STS12, where
+every teacher collapses to .31-.43 and the trunk holds .50.
+
+**With arms it clears the best teacher by +.14** and closes to within **.063** of
+a model trained on a billion curated pairs.
+
+Mean-pooled BERT-family encoders are known-weak sentence encoders -- that is the
+reason Sentence-BERT exists -- so beating them is an efficiency result rather
+than a state-of-the-art one. The MiniLM row is in the table to keep that honest.
+
+### Geometry
+
+| model | self_cos | erank |
+|---|---|---|
+| bert-base | +0.6071 | 32.8 |
+| ModernBERT-base | +0.9001 | 26.1 |
+| roberta-base | +0.9594 | 19.8 |
+| albert-base-v2 | +0.7473 | 20.9 |
+| distilbert | +0.6920 | 31.1 |
+| **captionbert-8192-v2** | +0.1396 | 36.6 |
+| **captionbert-8192-v2 + arms** | +0.0964 | 57.6 |
+| captionbert-8192-b | +0.1411 | 36.1 |
+| all-MiniLM-L6-v2 | +0.0251 | 86.7 |
+
+`self_cos` is the isotropy gauge: the mean cosine between unrelated sentences.
+Mean-pooled BERT-family embeddings sit in a narrow cone (+.61 to +.96), where
+cosine cannot discriminate. `erank` is the participation ratio -- how many of the
+768 directions carry variance.
+
+Both track capability almost perfectly across all ten models, and **isotropy is
+the mechanism**: no isotropy objective appears anywhere in the training stack.
+The arms then lift erank 36.6 -> 57.6, the first evidence in this line that
+adaptation *adds* usable directions rather than only rotating them.
 
 ## With AMOE arms (`amoe/`)
 
@@ -129,6 +160,28 @@ rather than return a silently-wrong model.
 
 `pip install git+https://github.com/AbstractEyes/amoe-lora` for the arm methods.
 
+### Arms do not transfer between trunks
+
+These anchors were trained against **this** trunk's residual stream. Measured
+against the sibling [`captionbert-8192-b`](https://huggingface.co/AbstractPhil/captionbert-8192-v2-B)
+(same architecture, complete 66-chunk corpus):
+
+| configuration | 8-task mean |
+|---|---|
+| v2 arms on v2 | .7287 |
+| v2 arms on **-b** | .6863 |
+| + re-aligned routing keys | .6987 |
+| -b's own anchors on -b | .7295 |
+
+Transferring costs **31% of the gain**; re-training only the 1,536 routing keys
+recovers 29% of that, so **71% of the loss is in the anchors themselves**.
+
+The two trunks are indistinguishable on all eight tasks (+.0009 mean excluding
+the 100-row BIOSSES) and on geometry, yet 1.6M adapter parameters tell them
+apart -- adapters read the residual stream, the task gauges read the pooled
+output, and the stream carries trunk identity the output does not. Budget one
+anchor set per trunk (~18 min).
+
 ### Arm library
 
 `amoe/arms/` holds one canonical copy of every anchor with `ARMS.json`
@@ -167,6 +220,10 @@ See [amoe-lora](https://github.com/AbstractEyes/amoe-lora).
 - **10 of 66 source chunks lacked ModernBERT**, so 54 chunks (~27M rows) were
   used. No 4-expert fallback: that would change the target definition mid-dataset.
 - Trained on image captions. Expect caption-like text to be its strongest domain.
+- **A sibling trained on the complete 66-chunk corpus** ([`-b`](https://huggingface.co/AbstractPhil/captionbert-8192-v2-B))
+  scores within .0046 of this one bare and .0008 with arms. 19% more data bought
+  nothing: the ceiling is teacher agreement (28.7 of 768 shared directions), not
+  corpus size.
 - **The trunk is single-seed.** The AMOE results are 2-seed with a measured
   spread of .003-.005, against margins of +.012 to +.019.
 - `amoe/sts-combo` stopped at step 500 of a planned 4000; its card and config