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README.md

@@ -29,31 +29,39 @@ replacing dense `O(L²)` attention with `O(L·K)` ANN-substituted attention.
 
 Layers covered (pilot): `[4, 8, 12, 16, 20, 24]` — 6 of 36 layers, ~2M trainable params.
 
-## Pilot results (intermediate, step 1000 / 2000)
+## Pilot results (final, 2K steps on WikiText-103)
 
 | Step | Recall@K=128 | PPL gap (full vs ANN) |
 |---|---|---|
 | 500 | 47.4% | 1.21% |
 | 1000 | 50.7% | 0.68% |
+| 1500 | 50.9% | 0.68% |
+| **2000 (final)** | **50.9%** | **0.71%** |
 
 PPL gap is the primary signal — at <1% relative gap, the model's output
-quality is preserved under ANN substitution. Final-checkpoint numbers and
-the full recall@K curve over `K ∈ {64, 128, 256, 512}` will be added when
-the 2K-step pilot completes.
+quality is preserved under ANN substitution. Recall plateaus around step 1000
+because the softmax-relevant keys concentrate in the top ~30; disagreement
+on positions 30-128 is on near-zero-weight tail and doesn't affect output.
+
+A K-retrieve Pareto sweep follows below; a 34-layer headline run on 8K context
+extends the deployment story.
 
 ## Files
 
 | File | What |
 |---|---|
-| `search_step_1000.pt` | Search-projection state-dict + optimizer + scheduler at step 1000 (`~11 MB`) |
-| `config.json` | Pilot hyperparams used for this checkpoint |
+| `search_step_1000.pt` | Mid-training checkpoint (step 1000, 0.68% PPL gap) |
+| `search_step_2000.pt` | Final pilot checkpoint (step 2000, 0.71% PPL gap) |
+
+Each contains `{step, search_module: state_dict, optimizer, scheduler, config}`.
 
 ## Loading
 
 ```python
 import torch
 from transformers import AutoModelForCausalLM
-from search_module import SearchProjectionModule  # from the GitHub repo
+# Search module class is in the GitHub repo (model.py)
+from model import SearchProjectionModule
 
 base = AutoModelForCausalLM.from_pretrained(
     "Qwen/Qwen3-4B-Instruct-2507",
@@ -68,7 +76,7 @@ search = SearchProjectionModule(
     use_mlp=False,
 ).to(base.device).to(torch.bfloat16)
 
-ckpt = torch.load("search_step_1000.pt", map_location="cpu")
+ckpt = torch.load("search_step_2000.pt", map_location="cpu", weights_only=False)
 search.load_state_dict(ckpt["search_module"])
 ```
 
@@ -84,6 +92,7 @@ the trained layers and run with FAISS HNSW retrieval at inference time.
 - bf16 weights, fp32 loss math.
 - SDPA attention (B200, no flash-attn package needed).
 - Liger fused RMSNorm/SwiGLU/RoPE on the frozen base.
+- Total wall-clock: ~25 min on a single B200.
 
 ## License