Add Gemma 4 AR 4/4 split predictions, two-seed click/ExpB predictions, and update README card

README.md

@@ -14,72 +14,58 @@ size_categories:
 
 # DiffusionGemma 2: Latent Code-Graph Benchmarks & Raw Predictions
 
-Standardized AST code-graph benchmark packs, continuous symbol embeddings, pre-registration (`PREREG.json`), linear evaluation probes, and **raw `.npy` model prediction arrays** for independent CPU rescoring of ***Latent Roadmaps: DiffusionGemma State Transitions for Code-Graph Refactoring***.
+Standardized AST code-graph benchmark packs, continuous symbol embeddings, pre-registration (`PREREG.json`), linear evaluation probes, and **raw `.npy` and `.npz` model prediction arrays** for independent CPU rescoring of ***Latent Roadmaps: DiffusionGemma State Transitions for Code-Graph Refactoring***.
 
 - **Author:** `bumblebeers` ([GitHub](https://github.com/bumblebeers))
 - **License:** Apache-2.0
 - **Code & Scoring Harness:** [bumblebeers/dgg2-latent-code-graphs](https://github.com/bumblebeers/dgg2-latent-code-graphs)
-- **Trained LoRA Adapters & Readout Heads:** [bumblebeer/dgg2-latent-code-graphs-adapters](https://huggingface.co/bumblebeer/dgg2-latent-code-graphs-adapters)
-- **Standalone Libraries:** [dgg-latent](https://github.com/bumblebeers/dgg-latent) | [dgg-codebook](https://github.com/bumblebeers/dgg-codebook) | [dgg-vit-decoder](https://github.com/bumblebeers/dgg-vit-decoder)
+- **Trained Two-Seed LoRA Adapters & Readout Heads:** [bumblebeer/dgg2-latent-code-graphs-adapters](https://huggingface.co/bumblebeer/dgg2-latent-code-graphs-adapters)
+- **Standalone Libraries (`v0.1.0`):** [dgg-latent](https://github.com/bumblebeers/dgg-latent) | [dgg-codebook](https://github.com/bumblebeers/dgg-codebook) | [dgg-vit-decoder](https://github.com/bumblebeers/dgg-vit-decoder)
+- **Upstream `transformers` Multimodal Mask Fix:** [huggingface/transformers#50684](https://github.com/huggingface/transformers/pull/50684)
 
 ---
 
 ## Repository Contents
 
-### 1. Experiment A: 16×16 Visual Adjacency Grid Benchmark Packs (`data/gridA_*`)
-- **`data/gridA_train1608.npz`**: 1,608 16-symbol Python AST subgraphs extracted across 8 training repositories with 22 fixed candidate states and 3 open-ended edit families.
+### 1. Experiment A: 16x16 Visual Adjacency Grid Benchmark Packs (`data/gridA_*`)
+- **`data/gridA_train1608.npz`**: 1,608 16-symbol Python AST subgraphs extracted across 3 training repositories (`fastapi`, `requests`, `rich`) with 22 fixed candidate states and 3 open-ended edit families.
 - **`data/gridA_test360_httpx.npz`**: 360 held-out 16-symbol AST subgraphs from `encode/httpx` across seen, shuffled, unseen paraphrase, indirect intent, held-out single-pass, 2-step re-feeding, 3-step chain, and open-ended manifests.
 - **`data/gridA_test360_click.npz`**: 360 held-out 16-symbol AST subgraphs from `pallets/click` for zero-shot cross-repository transfer.
-- **`data/gridA_n32_*.npz` & `data/gridA_n48_*.npz`**: Multi-resolution 32×32 and 48×48 subgraph packs testing sub-patch packing limits (Gate A1).
+- **`data/gridA_n32_*.npz` & `data/gridA_n48_*.npz`**: Multi-resolution 32x32 and 48x48 subgraph packs testing sub-patch packing limits (Gate A1).
 - **`data/*.nodes.json`**: Per-subgraph AST symbol and line metadata.
-- **`reader.npz`** (`outputs/m1/reader.npz`): Gate A1 closed-form Ridge probe mapping clean SigLIP latents $h_v \in \mathbb{R}^{256 \times 1152}$ to the 8 discrete palette classes (`1.000` clean cell accuracy across all 8 classes).
+- **`reader.npz`** (`outputs/m1/reader.npz`): Gate A1 closed-form Ridge probe mapping clean SigLIP latents `h_v` (`256 x 1152`) to the 8 discrete palette classes (`1.000` clean cell accuracy across all 8 classes).
 
 ### 2. Experiment B: 256-Symbol Continuous Embedding Packs (`data/nodeB_*`)
 - **`data/nodeB_train1608.json.gz`**: 1,608 256-symbol AST windows for single-site and multi-site symbol refactoring.
-- **`data/nodeB_test360_httpx.json.gz`** & **`data/nodeB_test360_click.json.gz`**: Held-out `httpx` ($N=360$) and zero-shot `click` ($N=360$) evaluation packs (`chance = 12.5%`, `mean-vector floor = 25.0%`).
+- **`data/nodeB_test360_httpx.json.gz`** & **`data/nodeB_test360_click.json.gz`**: Held-out `httpx` (`N=360`) and zero-shot `click` (`N=360`) evaluation packs (`chance = 12.5%`, `mean-vector floor = 25.0%`).
 - **`data/nodeB_test360_httpx_unseenargs.json.gz`** & **`data/nodeB_test180_httpx_renameclass.json.gz`**: Unseen argument and class-rename splits.
 - **`data/nodeB_multi_test360_httpx_k2.json.gz`** & **`k4.json.gz`**: 2-site and 4-site simultaneous and re-fed symbol-editing splits.
-- **`emb.npz`** & **`emb_train.npz`**: Frozen `sentence-transformers/all-MiniLM-L6-v2` ($d=384$) symbol embeddings and train-only standardization statistics ($\mu, \sigma$).
+- **`emb.npz`** & **`emb_train.npz`**: Frozen `sentence-transformers/all-MiniLM-L6-v2` (`d=384`) symbol embeddings and train-only standardization statistics.
 
 ### 3. Raw Model Prediction Arrays (`predictions/` & `outputs/m2/decoded_grids_all_arms.npz`)
-To enable full independent verification without requiring a 26B GPU, this dataset hosts **both** continuous latent `.npy` outputs and Ridge-decoded discrete `(M, 16, 16)` `uint8` grid `.npy` files:
-- **`predictions/expA_hv/<arm>/preds_*.npy`**: Raw continuous predicted SigLIP latent tensors $\hat{h}_v \in \mathbb{R}^{360 \times 256 \times 1152}$ (`float32`) emitted by `stock_generate_with_readout` for `cg_lora_dg_seed42_ep3`, `cg_lora_dg_seed1337_ep3`, `cg_frozen_dg_ep3`, zero-shot `click`, and `NonDGTransformBaseline`.
-- **`predictions/expA_grids/<arm>/*.npy`** & **`outputs/m2/decoded_grids_all_arms.npz`**: Decoded `(360, 16, 16)` `uint8` palette grids (`0..7`) across all evaluated arms (including LoRA DG, Frozen DG, Non-DG Frozen/EncLoRA, and Gemma 4 AR baselines as completed) for instant lightweight CPU rescoring.
-- **`predictions/expB_vec/<arm>/*.npy`**: Raw standardized continuous symbol-vector predictions $\hat{z} \in \mathbb{R}^{M \times 256 \times 384}$ (`float32`) for Experiment B (`cg_expB_lora_dg_*` and `NonDGTransformBaseline`).
+To enable full independent verification without requiring a 26B GPU, this dataset hosts **both** continuous latent `.npy` outputs and Ridge-decoded discrete `(M, 16, 16)` `uint8` grid `.npy`/`.npz` files:
+- **`predictions/expA_hv/<arm>/preds_*.npy`**: Raw continuous predicted SigLIP latent tensors (`360 x 256 x 1152`, `float32`) emitted by `stock_generate_with_readout` for two-seed LoRA DG (`cg_lora_dg_seed42_ep3`, `cg_lora_dg_seed1337_ep3`), `cg_frozen_dg_ep3`, and Non-DG baselines.
+- **`predictions/expA_grids/<arm>/*.npy`** & **`outputs/m2/decoded_grids_all_arms.npz`**: Decoded `(360, 16, 16)` `uint8` palette grids (`0..7`) across all evaluated arms — two-seed LoRA DG (`httpx` and two-seed zero-shot `click`), Frozen DG, two-seed Non-DG (`Frozen Instr` and `Enc LoRA`), and matched `google/gemma-4-26B-A4B-it` autoregressive baselines (`gemma4_ar_prompted` and `gemma4_ar_lora_r64_s42` across all 4 primary splits).
+- **`predictions/expB_vec/<arm>/*.npy`**: Raw standardized continuous symbol-vector predictions (`M x 256 x 384`, `float32`) for two-seed Experiment B LoRA DG (`cg_expB_lora_dg_seed42_ep3`, `cg_expB_lora_dg_seed1337_ep3`) and two-seed Non-DG baselines.
 
 ---
 
 ## Independent CPU Rescoring Quickstart
 
-Clone the scoring harness from [bumblebeers/dgg2-latent-code-graphs](https://github.com/bumblebeers/dgg2-latent-code-graphs) and download this dataset (or individual `.npy` files):
-
 ```bash
 git clone https://github.com/bumblebeers/dgg2-latent-code-graphs.git
 cd dgg2-latent-code-graphs
 
 # Rescore Experiment A directly from raw continuous h_v (.npy, shape 360x256x1152) + Ridge probe:
-python3 cg/cg_score_preds.py \\
-  --pack data/gridA_test360_httpx.npz \\
-  --reader reader.npz \\
-  --manifest seen_bal360 \\
+python3 cg/cg_score_preds.py \
+  --pack data/gridA_test360_httpx.npz \
+  --reader reader.npz \
+  --manifest seen_bal360 \
   --pred predictions/expA_hv/cg_lora_dg_seed42_ep3/preds_cg_lora_dg_seed42_ep3_seen_bal360.npy
 
-# Or rescore instantly from the decoded (360, 16, 16) uint8 palette grid (.npy):
-python3 cg/cg_score_preds.py \\
-  --pack data/gridA_test360_httpx.npz \\
-  --manifest refeed_heldout_comp_bal360 \\
-  --grids predictions/expA_grids/cg_lora_dg_seed42_ep3/preds_cg_lora_dg_seed42_ep3_refeed_heldout_comp_bal360.npy
-
-# Rescore Experiment B raw (360, 256, 384) symbol vectors (.npy):
-python3 -c "
-import gzip, json, numpy as np, sys
-sys.path.insert(0, 'cg')
-from cg_node_gate import Emb
-from cg_node_metrics import Evaluator, default_items
-ds = json.load(gzip.open('data/nodeB_test360_httpx.json.gz', 'rt'))
-ev = Evaluator(ds, Emb.load('emb.npz'), Emb.load('emb_train.npz').vec)
-pred = np.load('predictions/expB_vec/cg_expB_lora_dg_seed42_ep3/preds_cg_expB_lora_dg_seed42_ep3_instr_seen.npy')
-res = ev.score(pred, default_items(ds))
-print({k: round(res[k], 4) for k in ('site_hit', 'all_sites_hit', 'top_is_site', 'drift')})
-"
+# Rescore decoded (360, 16, 16) uint8 palette grids (.npy), including Gemma 4 AR LoRA:
+python3 cg/cg_score_preds.py \
+  --pack data/gridA_test360_httpx.npz \
+  --manifest refeed_heldout_comp_bal360 \
+  --grids predictions/expA_grids/gemma4_ar_lora_r64_s42/refeed_heldout_comp_bal360.npy
 ```

outputs/m2/arms/gemma4_ar_lora_r64_s42/manifest_summary.json

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+}

outputs/m2/arms/gemma4_ar_lora_r64_s42/seen_bal360_summary.json

@@ -0,0 +1,184 @@
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+}

outputs/m2/arms/gemma4_ar_lora_r64_s42/unseen_infer_360_summary.json

@@ -0,0 +1,142 @@
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+}

outputs/m2/arms/gemma4_ar_lora_r64_s42/unseen_para_bal360_summary.json

@@ -0,0 +1,201 @@
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+}

outputs/m2/arms/gemma4_ar_prompted/refeed_heldout_comp_bal360_summary.json

@@ -0,0 +1,122 @@
+{
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+}

outputs/m2/arms/gemma4_ar_prompted/seen_bal360_summary.json

@@ -0,0 +1,228 @@
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+}

outputs/m2/arms/gemma4_ar_prompted/unseen_infer_360_summary.json

@@ -0,0 +1,155 @@
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+}

outputs/m2/arms/gemma4_ar_prompted/unseen_para_bal360_summary.json

@@ -0,0 +1,229 @@
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+}

outputs/m2/click/click_2seed_summary.json

@@ -0,0 +1,64 @@
+{
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+}

outputs/m2/offline_verification_report.json

@@ -0,0 +1,376 @@
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