docs: add model card, add input_size to metadata

README.md

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+---
+license: mit
+language:
+  - en
+tags:
+  - welfare
+  - forecasting
+  - time-series
+  - pytorch
+  - ethics
+  - phi-humanity
+library_name: pytorch
+pipeline_tag: time-series-forecasting
+datasets:
+  - synthetic
+metrics:
+  - mse
+model-index:
+  - name: PhiForecasterGPU
+    results:
+      - task:
+          type: time-series-forecasting
+          name: Welfare Trajectory Forecasting
+        metrics:
+          - name: Best Validation Loss (MSE)
+            type: mse
+            value: 0.000196
+---
+
+# PhiForecasterGPU — Welfare Trajectory Forecaster
+
+A CNN+LSTM+Attention model that forecasts **Phi(humanity)** welfare trajectories — predicting how 8 ethical constructs (care, compassion, joy, purpose, empathy, love, protection, truth) evolve over time.
+
+## Model Description
+
+PhiForecasterGPU takes a 50-timestep window of 36 welfare signal features and predicts the next 10 timesteps for both the aggregate Phi score and all 8 individual constructs.
+
+| Property | Value |
+|----------|-------|
+| **Architecture** | CNN1D (2-layer) → Stacked LSTM (2-layer) → Additive Attention → Dual heads |
+| **Parameters** | ~1.3M |
+| **Input** | 50 timesteps × 36 features |
+| **Output** | 10-step Phi forecast + 10-step × 8 construct forecast + attention weights |
+| **Formula** | Phi v2.1 (recovery-aware floors) |
+| **Training** | 100 epochs, 44,800 sequences from 8 scenario types × 50 seeds |
+| **Best Val Loss** | 0.000196 MSE |
+
+### Architecture
+
+```
+Input (50 × 36)
+    ↓
+CNN1D (2 conv layers, kernel=3, hidden=256)
+    ↓
+Stacked LSTM (2 layers, hidden=256)
+    ↓
+Additive Attention (query-key-value)
+    ↓
+┌───────────────┬────────────────────┐
+│  Phi Head     │  Construct Head    │
+│  (MLP → 10)  │  (MLP → 10 × 8)   │
+└───────────────┴────────────────────┘
+```
+
+### 36 Input Features
+
+- **8 raw constructs:** c, kappa, j, p, eps, lam_L, lam_P, xi
+- **8 volatility signals:** rolling 20-step standard deviation per construct
+- **8 momentum signals:** 10-step price momentum per construct
+- **5 synergy signals:** geometric mean of construct pairs (care×love, compassion×protection, joy×purpose, empathy×truth, love×truth)
+- **5 divergence signals:** squared difference of construct pairs
+- **phi:** aggregate welfare score
+- **dphi_dt:** first derivative of phi
+
+### Graph-Enhanced Mode (43 features)
+
+When trained with `graph_features_enabled=True`, the model accepts 43 features (36 base + 7 graph topology features from the detective knowledge graph):
+
+| Feature | Description |
+|---------|-------------|
+| graph_density | Edge density of the full graph |
+| entity_pagerank | PageRank centrality of focal entity |
+| entity_degree | Normalized degree of focal entity |
+| entity_clustering | Clustering coefficient |
+| community_size | Fraction of graph in same community |
+| avg_neighbor_conf | Mean edge confidence to neighbors |
+| hub_score | HITS hub score (normalized) |
+
+## The Phi(humanity) Formula
+
+Phi is a rigorous ethical-affective objective function grounded in care ethics (hooks 2000), capability theory (Sen 1999), and Ubuntu philosophy:
+
+```
+Phi(humanity) = f(lam_L) · [prod(x_tilde_i ^ w_i)] · Psi_ubuntu · (1 - Psi_penalty)
+```
+
+- **f(lam_L):** Community solidarity multiplier
+- **x_tilde_i:** Recovery-aware effective inputs (below-floor constructs get community-mediated recovery)
+- **w_i:** Inverse-deprivation weights (Rawlsian maximin)
+- **Psi_ubuntu:** Relational synergy term + curiosity coupling (love × truth)
+- **Psi_penalty:** Structural distortion penalty for mismatched construct pairs
+
+## Training Scenarios
+
+| Scenario | Description |
+|----------|-------------|
+| stable_community | Baseline — all constructs ~0.5 |
+| capitalism_suppresses_love | Love declines as purpose erodes |
+| surveillance_state | Truth rises while love collapses |
+| willful_ignorance | Love rises while truth collapses |
+| recovery_arc | Collapse then community-led recovery |
+| sudden_crisis | Compassion + protection crash mid-scenario |
+| slow_decay | Gradual institutional erosion |
+| random_walk | Correlated random walks with mean-reversion |
+
+## Usage
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+# Download checkpoint
+path = hf_hub_download("crichalchemist/phi-forecaster", "phi_forecaster_best.pt")
+
+# Load model (see spaces/maninagarden/model.py for class definitions)
+from model import PhiForecasterGPU
+model = PhiForecasterGPU(input_size=36, hidden_size=256, n_layers=2, pred_len=10)
+model.load_state_dict(torch.load(path, map_location="cpu", weights_only=True))
+model.eval()
+
+# Inference: (batch, seq_len=50, features=36) -> phi, constructs, attention
+X = torch.randn(1, 50, 36)
+phi_pred, construct_pred, attn = model(X)
+# phi_pred: (1, 10, 1) — next 10 Phi values
+# construct_pred: (1, 10, 8) — next 10 values for each construct
+# attn: (1, 50) — attention weights over input window
+```
+
+## Live Demo
+
+Try it at [crichalchemist/maninagarden](https://huggingface.co/spaces/crichalchemist/maninagarden) — interactive scenario explorer, custom forecasting, and experiment comparison.
+
+## Citation
+
+This model implements the Phi(humanity) welfare function formalized in the detective-llm project. Key theoretical foundations:
+
+- hooks, b. (2000). *All About Love: New Visions*. William Morrow.
+- Sen, A. (1999). *Development as Freedom*. Oxford University Press.
+- Fricker, M. (2007). *Epistemic Injustice*. Oxford University Press.
+- Metz, T. (2007). Toward an African moral theory. *Journal of Political Philosophy*.
+
+## Limitations
+
+- Trained on **synthetic data** — real-world calibration pending
+- Phi is a **diagnostic tool**, not an optimization target (Goodhart's Law)
+- 8-construct taxonomy is Western-situated; requires adaptation for diverse philosophical frameworks
+- Recovery-aware floors add theoretical richness but the model has not yet been retrained to fully leverage them

training_metadata.json

@@ -18,5 +18,6 @@
     "random_walk"
   ],
   "scenarios_per_type": 50,
-  "formula_version": "2.1-recovery-floors"
+  "formula_version": "2.1-recovery-floors",
+  "input_size": 36
 }