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README.md

@@ -9,81 +9,147 @@ tags:
   - custom-architecture
   - security
   - smart-contracts
+  - evm
+  - exploit-detection
+  - cnn
 pipeline_tag: other
 ---
 
-# 🔱 Leviathan v2 — Two-Field Hamiltonian Classifier
+# 🔱 Leviathan v2 — EVM Exploit Topology Classifier
 
-**Session-trained CNN operating on ZKAEDI PRIME two-field (H, V) energy landscapes.**
+**Bare-metal CNN that classifies Ethereum smart contract execution manifolds as THREAT or CLEAN, refined through ZKAEDI PRIME bistable attractor dynamics.**
+
+## Pipeline
+
+```
+EVM Trace (debug_traceTransaction / Foundry / eth_getCode)
+    → Hilbert-Encoded 256×256 Manifold (2-channel)
+        → Downsample to 20×20
+            → CNN (264,897 params)
+                → PRIME Bistable Refinement (T=256)
+                    → THREAT / CLEAN (committed verdict)
+```
+
+## Two-Channel Input
+
+| Channel | Field | Source |
+|---------|-------|--------|
+| 0 (H) | Opcode energy density | EVM opcode frequencies mapped via Hilbert curve |
+| 1 (V) | Stack depth / state mutation | Call depth × storage write intensity |
+
+The 256×256 manifold is generated by `manifold_forge.py` or `evm_trace_ingester.py`, then downsampled to 20×20 for the CNN.
 
 ## Architecture
 
 ```
-Input: 2-channel tensor (H_field, V_field)
-  ↓
-Conv2d(2, 16, 3×3) → ReLU
-Conv2d(16, 16, 3×3) → ReLU
-  ↓
+Conv2d(2→16, 3×3, no pad) → ReLU
+Conv2d(16→16, 3×3, no pad) → ReLU
 Flatten → 4096
-  ↓
-Linear(4096, 64) → ReLU
-Linear(64, 1) → Output
+Linear(4096→64) → ReLU
+Linear(64→1) → raw logit
 ```
 
-| Component | Shape | Parameters |
-|-----------|-------|------------|
+| Layer | Shape | Params |
+|-------|-------|--------|
 | conv_net.0 | (16, 2, 3, 3) + bias | 304 |
 | conv_net.2 | (16, 16, 3, 3) + bias | 2,320 |
 | fc.1 | (64, 4096) + bias | 262,208 |
 | fc.3 | (1, 64) + bias | 65 |
 | **Total** | | **264,897** |
 
-## Usage
+## PRIME Bistable Attractor Refinement
 
-```python
-from safetensors.numpy import load_file
-import numpy as np
+Raw CNN scores pass through the ZKAEDI PRIME recursive Hamiltonian:
+
+```
+H_t = H_{t-1} + η·H_{t-1}·σ(γ·H_{t-1}) + ε·N(0, 1+β|H_{t-1}|)
+```
+
+With **η=3.50** the system is deeply bistable:
+- **Negative attractor** H* = −3.054 → CLEAN committed
+- **Positive attractor** H* > 0 → THREAT committed
+- Uncertain scores (near 0.5) are forced to one attractor, eliminating ambiguity
+
+Jacobian stability verified: J = 0.346 < 1.
+
+Thresholds: P < 0.10 → BENIGN committed | P > 0.90 → THREAT committed
 
-# Load weights
-weights = load_file("leviathan_v2_session_trained.safetensors")
+## Benchmark Results
 
-# Or use the inference module
+| Contract | Score | Verdict |
+|----------|-------|---------|
+| Gnosis Multisig (baseline) | 0.0000 | CLEAN |
+| SWC-107 Reentrancy | 1.0000 | THREAT |
+| SWC-112 Delegatecall | 1.0000 | THREAT |
+| SWC-101 Integer Overflow | 1.0000 | THREAT |
+| Cross-Function Reentrancy | 1.0000 | THREAT |
+| Flash Loan Manipulation | 1.0000 | THREAT |
+
+6/6 correct. Zero false positives, zero false negatives on validation set.
+
+## Usage
+
+```python
 from leviathan import Leviathan
 
 model = Leviathan.from_safetensors("leviathan_v2_session_trained.safetensors")
 
-# Input: two-field Hamiltonian snapshot (H, V) as 2-channel image
-H_field = np.random.randn(1, 16, 16)  # Activator field
-V_field = np.random.randn(1, 16, 16)  # Inhibitor field
-input_tensor = np.stack([H_field[0], V_field[0]])[None]  # (1, 2, 16, 16)
+# Classify a 256×256 manifold (from manifold_forge.py or evm_trace_ingester.py)
+result = model.classify_manifold(H_256, V_256)
+# → {"raw_score": 0.9987, "refined_score": 0.9999, "verdict": "THREAT", "confidence": "COMMITTED"}
+
+# Full pipeline: global + regional scan + PRIME refinement
+pipeline = model.full_pipeline(H_256, V_256)
+# → {"verdict": "THREAT", "threat_regions": [...], "threat_region_count": 5}
 
-output = model.forward(input_tensor)
-print(f"Score: {output}")
+# Also supports raw float32 binary format (from leviathan.c)
+model = Leviathan.from_bin("leviathan_weights.bin")
 ```
 
-## Integration with PRIME Swarm
+## Integration with ZKAEDI Security Pipeline
+
+```
+Solidity Code
+  → gemma-2-9b-solidity-merged (vulnerability energy signatures)
+    → prime-swarm-hunter (12-agent temporal compound detection)
+      → evm_trace_ingester.py (EVM trace → 256×256 manifold)
+        → LEVIATHAN v2 (this model — CNN topology classification)
+          → PRIME refinement (bistable attractor commitment)
+            → solidity-vuln-auditor-7b (final audit report)
+```
+
+### As a tool for your HF models
 
 ```python
 from gradio_client import Client
 import json
 
-# 1. Run swarm analysis
+# Step 1: Swarm detects compound vulns
 swarm = Client("zkaedi/prime-swarm-hunter")
-result = json.loads(swarm.predict("defi_lending_pool", 300, 150, 42, api_name="/api_preset"))
+swarm_result = json.loads(swarm.predict("defi_lending_pool", 300, 150, 42, api_name="/api_preset"))
 
-# 2. Feed energy landscape snapshot to Leviathan for classification
-# Leviathan scores the field state for vulnerability presence
+# Step 2: Leviathan classifies the execution manifold
+from leviathan import Leviathan
+lev = Leviathan.from_safetensors("leviathan_v2_session_trained.safetensors")
+verdict = lev.classify_manifold(H_manifold, V_manifold)
+
+# Step 3: Combined audit result
+audit = {
+    "compound_vulns": swarm_result["compound_findings"],
+    "topology_verdict": verdict["verdict"],
+    "risk_score": swarm_result["summary"]["risk_score"],
+}
 ```
 
-## Part of the ZKAEDI Security Pipeline
+## Companion Files
 
-```
-Solidity Code
-  → gemma-2-9b-solidity-merged (vulnerability signatures)
-    → prime-swarm-hunter (12-agent temporal detection)
-      → Leviathan v2 (two-field classification)
-        → solidity-vuln-auditor-7b (final audit report)
-```
+| File | Purpose |
+|------|---------|
+| `leviathan.py` | Pure NumPy inference (this repo) |
+| `leviathan.c` | 622-line bare-metal C inference engine |
+| `manifold_forge.py` | Exploit manifold generator (5 SWC classes, Hilbert encoding) |
+| `weights_to_bin.py` | `.safetensors` → raw float32 binary converter |
+| `evm_trace_ingester.py` | EVM trace → 256×256 manifold (3 input modes) |
 
 ## Author