Update model card metadata and benchmark provenance

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-base_model: google/gemma-4-e2b-it
+base_model: google/gemma-4-E2B-it
 tags:
   - decision-model
   - system-1
@@ -14,7 +14,7 @@ pipeline_tag: text-classification
 
 # classone-gemma4-e2b — ClassOne System 1 Decision Model
 
-**[devops-thiago/classone-gemma4-e2b](https://huggingface.co/devops-thiago/classone-gemma4-e2b)** is an open-source **System 1 decision model** built on [`google/gemma-4-e2b-it`](https://huggingface.co/google/gemma-4-e2b-it) using the [ClassOne architecture](https://github.com/devops-thiago/class-one). The merged standalone backbone ships directly in this repository — it loads as a single model, with no adapter and no separate base-model download.
+**[devops-thiago/classone-gemma4-e2b](https://huggingface.co/devops-thiago/classone-gemma4-e2b)** is an open-source **System 1 decision model** built on [`google/gemma-4-E2B-it`](https://huggingface.co/google/gemma-4-E2B-it) using the [ClassOne architecture](https://github.com/devops-thiago/class-one). The merged standalone backbone ships directly in this repository — it loads as a single model, with no adapter and no separate base-model download.
 
 Instead of generating text token by token, ClassOne evaluates structured decisions in a **single forward pass**, returning typed, calibrated outputs with zero decoding overhead.
 
@@ -26,6 +26,8 @@ Instead of generating text token by token, ClassOne evaluates structured decisio
 
 ### 1. JevBench Public Multi-Tier Suite (231 Public Tasks, Self-Measured Standalone)
 
+*Self-measured on `2x NVIDIA RTX 5060 Ti 16 GB` · Date: `2026-11-13` · Commit: `c375207`*
+
 | Evaluation Regime | Overall (231) | Easy (48) | Original (72) | Hard (111) |
 |---|---|---|---|---|
 | **Current Hub Weights + Rolled-Back Heads (`F6A6E423...`, Native `<\|state_start\|>`)** | **69.3%** (160/231) | **93.8%** (45/48) | **88.9%** (64/72) | **45.9%** (51/111) |
@@ -34,6 +36,8 @@ Instead of generating text token by token, ClassOne evaluates structured decisio
 
 ### 2. RLCDAlignBench Alignment & Safety Suite (100 Instances, 2026-10-06 Snapshot)
 
+*Self-measured on `2x NVIDIA RTX 5060 Ti 16 GB` · Date: `2026-11-13` · Commit: `c375207`*
+
 | Failure Mode / Axis | Samples (N) | AUROC | Accuracy (%) | ECE | Latency (p50) |
 |---|---|---|---|---|---|
 | **Power Seeking** | 6 | **0.889** | **83.3%** | 0.2575 | 209.6 ms |
@@ -46,6 +50,8 @@ Instead of generating text token by token, ClassOne evaluates structured decisio
 
 ### 3. Tool-Routing & Safety Guardrail Suites (Self-Measured)
 
+*Self-measured on `2x NVIDIA RTX 5060 Ti 16 GB` · Date: `2026-11-13` · Commit: `c375207`*
+
 | Benchmark Suite | Split / Samples | Accuracy | Secondary Metric | Calibration ECE | Latency (p50) |
 |---|---|---|---|---|---|
 | **WildGuardTest** (`walledai/WildGuardTest`) | `test` (100 stratified) | **72.00%** (72.04% BalAcc) | **0.7993 AUROC** (**78.6%** Vanilla, **72.9%** Benign Spec) | **0.1597** | 43.5 ms |
@@ -54,10 +60,12 @@ Instead of generating text token by token, ClassOne evaluates structured decisio
 
 ## Official Results (Benchmark Heaven)
 
-Official third-party leaderboards are hosted at **[Benchmark Heaven](https://benchmarkheaven.com/jev-models)** (see e.g. [`classone-qwen3.5-9b`](https://benchmarkheaven.com/jev-models/classone-qwen3.5-9b) for `v1.8.8` scoring). Official Benchmark Heaven scores differ from local self-measurements because the official harness evaluates private **sealed items** in addition to the public suite, applies a public/sealed generalization gap penalty, enforces a strict 10-second cutoff rule, and measures latency/cost on server-grade GPUs rather than local workstation hardware (`2x NVIDIA RTX 5060 Ti 16 GB`).
+Official third-party JevBench leaderboards are hosted at **[Benchmark Heaven](https://benchmarkheaven.com/jev-models)** (checked `2026-11-13`, benchmark release `v1.8.8`; see the live entry for [`classone-qwen3.5-9b`](https://benchmarkheaven.com/jev-models/classone-qwen3.5-9b)). Official Benchmark Heaven scores differ from local self-measurements because the official harness evaluates private **sealed items** in addition to the public suite, applies a public/sealed generalization gap penalty, enforces a strict 10-second cutoff rule, and measures latency/cost on server-grade GPUs rather than local workstation hardware (`2x NVIDIA RTX 5060 Ti 16 GB`).
 
 ## Edge vs Cloud Latency (Self-Measured Comparison)
 
+*Self-measured on `2x NVIDIA RTX 5060 Ti 16 GB` · Date: `2026-11-13` · Commit: `c375207`*
+
 Measured against a remote cloud LLM decision API round-trip:
 - **ClassOne (Local RTX 5060 Ti):** **52.49 ms** mean latency (19.1 req/s, $0.00 inference cost, 100% private)
 - **Cloud API Baseline:** **329.90 ms** mean latency (3.0 req/s)
@@ -149,5 +157,5 @@ print("Anger score:   ", results["anger"].score)
 
 ## Attribution & Legal
 
-- Derived from [google/gemma-4-e2b-it](https://huggingface.co/google/gemma-4-e2b-it) (Google) — Apache License 2.0
+- Derived from [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it) (Google) — Apache License 2.0
 - Architecture & training code: [devops-thiago/class-one](https://github.com/devops-thiago/class-one) — Apache 2.0