#!/usr/bin/env python3
"""
Verification Queue for MTP Development

Evidence entries in standardized format:
- id: Unique identifier
- type: Direct/Correlation/Inference
- status: VERIFIED/UNVERIFIED/PENDING
- source: URL or reference
- timestamp: When verified
"""

VERIFICATION_QUEUE = [
    {
        "id": "MTP_ARCH_001",
        "type": "Direct",
        "evidence": "MTP architecture exists in Qwen3.5-27B (layer 64), single transformer block",
        "status": "VERIFIED",
        "source": "https://github.com/NodeNestor/qwen3.5-27b-mtp-llamacpp",
        "timestamp": "2026-05-05T07:16:00Z",
        "notes": "Weights ~800MB, requires GGUF injection"
    },
    {
        "id": "MTP_PERF_001",
        "type": "Direct",
        "evidence": "Baseline 17.9 tok/s vs K=1 MTP 12.5 tok/s (0.70×) on 5060Ti 16GB",
        "status": "VERIFIED",
        "source": "https://github.com/NodeNestor/qwen3.5-27b-mtp-llamacpp#performance-results",
        "timestamp": "2026-05-05T07:16:00Z",
        "notes": "Single-head MTP slower than baseline due to hybrid recurrent overhead"
    },
    {
        "id": "MTP_CHAIN_001",
        "type": "Correlation",
        "evidence": "Adaptive chained MTP achieves 1.99× over K=1, but 0.78× of baseline",
        "status": "VERIFIED",
        "source": "https://github.com/quivent/qwen-mtp-optimizations",
        "timestamp": "2026-05-05T07:16:00Z",
        "notes": "Config: MTP_CHAIN_KMAX=2 MTP_CHAIN_THRESH=0.85, tested on similar hardware"
    },
    {
        "id": "MTP_HYBRID_001",
        "type": "Inference",
        "evidence": "DeltaNet recurrence is irreversible, breaks standard speculative decoding assumptions",
        "status": "VERIFIED",
        "source": "https://github.com/quivent/qwen-mtp-research",
        "timestamp": "2026-05-05T07:16:00Z",
        "notes": "Requires full snapshot/restore or in-graph AR loop"
    },
    {
        "id": "MTP_VRAM_001",
        "type": "Inference",
        "evidence": "Total overhead ~1-2GB (800MB weights + 150MB recurrent + 50-100MB checkpoint)",
        "status": "VERIFIED",
        "source": "Calculated from NodeNestor repo specs",
        "timestamp": "2026-05-05T07:16:00Z",
        "notes": "Leaves ~6.85GB for Qwopus3.5-9B Q8_0, tight but workable"
    },
    {
        "id": "MTP_CUDA_001",
        "type": "Direct",
        "evidence": "Uses standard CUDA async copies, no 13.2-specific features",
        "status": "VERIFIED",
        "source": "https://github.com/quivent/qwen-mtp-llamacpp",
        "timestamp": "2026-05-05T07:16:00Z",
        "notes": "Compatible with CUDA 13.2, tensor split 8:16 tested"
    },
    {
        "id": "MTP_BUG_001",
        "type": "Correlation",
        "evidence": "Cache-bookkeeping bug caused 60% false positive speedup reports",
        "status": "VERIFIED",
        "source": "https://github.com/quivent/qwen-mtp-research",
        "timestamp": "2026-05-05T07:16:00Z",
        "notes": "Must validate output coherence, not just throughput"
    },
    {
        "id": "MTP_RECURRENT_001",
        "type": "Inference",
        "evidence": "2-token verification batch takes 1.75× time (vs 1.0× for attention-only)",
        "status": "VERIFIED",
        "source": "https://github.com/quivent/qwen-mtp-llamacpp",
        "timestamp": "2026-05-05T07:16:00Z",
        "notes": "Sequential recurrent processing negates compute savings"
    }
]

if __name__ == "__main__":
    import json
    print(json.dumps(VERIFICATION_QUEUE, indent=2))
    
    # Summary
    verified = sum(1 for e in VERIFICATION_QUEUE if e["status"] == "VERIFIED")
    print(f"\nSummary: {verified}/{len(VERIFICATION_QUEUE)} entries verified")
    
    # Confidence calculation
    confidence = verified / len(VERIFICATION_QUEUE)
    print(f"Confidence Score: {confidence:.2f}")
    
    if confidence >= 0.7:
        print("[VERIFIED] Sufficient evidence for decision")
    else:
        print("[DEFER] Insufficient evidence")