tinymind-native-8b-remote-handoff/bundle/evaluation/world_class_eval.py

"""World-class multi-axis evaluation harness for TinyMind.

This module does not invent leaderboard results. It creates a reproducible
packet that can run local/public benchmarks now, import external/provider
results when available, and compare TinyMind against world-model slots with a
strict claim gate.
"""

from __future__ import annotations

from datetime import datetime, timezone
import csv
import json
from pathlib import Path
from typing import Iterable


WORLD_EVAL_TARGETS = [
    {
        "name": "Hugging Face Leaderboards/Evals",
        "url": "https://huggingface.co/docs/leaderboards/index",
        "kind": "public_hub_or_space_submission",
        "status": "requires_hub_compatible_model_or_eval_space",
        "axes": ["MMLU-Pro", "GPQA", "IFEval", "MATH", "BBH", "coding"],
    },
    {
        "name": "LMArena",
        "url": "https://lmarena.ai/",
        "kind": "human_preference_arena",
        "status": "requires_public_chat_endpoint_and_arena_acceptance",
        "axes": ["human preference", "chat quality", "style", "instruction following"],
    },
    {
        "name": "Artificial Analysis",
        "url": "https://artificialanalysis.ai/evaluations",
        "kind": "provider_benchmark_index",
        "status": "requires_provider_endpoint_or_external_listing",
        "axes": ["intelligence index", "speed", "price", "coding", "math", "reasoning"],
    },
]

WORLD_MODEL_SLOTS = [
    {"model": "frontier_closed_top", "class": "frontier", "params": None, "source": "external_provider_required"},
    {"model": "top_open_large", "class": "open_large", "params": None, "source": "hf_or_provider_required"},
    {"model": "70B_class", "class": "70B", "params": 70_000_000_000, "source": "size_reference"},
    {"model": "7B_class", "class": "7B", "params": 7_000_000_000, "source": "size_reference"},
    {"model": "gpt2", "class": "124M", "params": 124_439_808, "source": "local_or_hf_smoke"},
]


def _load(path: str | Path | None) -> dict:
    if not path:
        return {}
    p = Path(path)
    if not p.exists():
        return {}
    return json.loads(p.read_text(encoding="utf-8"))


def _score(value: float | int | None) -> float:
    if value is None:
        return 0.0
    return max(0.0, min(100.0, float(value)))


def _metric_rows(
    knowledge_report: dict,
    compact_report: dict,
    coherence_report: dict,
    memory_report: dict,
    external_results: Iterable[dict],
) -> list[dict]:
    dimensions = compact_report.get("dimensions", {})
    rows = [
        {
            "axis": "knowledge_mmlu_pro",
            "score": _score(dimensions.get("official_mmlu_pro_smoke") or dimensions.get("knowledge_mmlu_pro_smoke")),
            "source": compact_report.get("json_path", "compact_intelligence"),
            "scope": "local_public_harness",
        },
        {
            "axis": "instruction_following",
            "score": _score(dimensions.get("instruction_following_smoke")),
            "source": compact_report.get("json_path", "compact_intelligence"),
            "scope": "local_smoke",
        },
        {
            "axis": "translation_th_en",
            "score": _score(dimensions.get("translation_smoke")),
            "source": compact_report.get("json_path", "compact_intelligence"),
            "scope": "local_smoke",
        },
        {
            "axis": "natural_answer_style",
            "score": _score(dimensions.get("natural_answer_style")),
            "source": compact_report.get("json_path", "compact_intelligence"),
            "scope": "local_quality_gate",
        },
        {
            "axis": "bit_exactness",
            "score": _score(dimensions.get("bit_exactness")),
            "source": compact_report.get("json_path", "compact_intelligence"),
            "scope": "local_training_metric",
        },
        {
            "axis": "layer_coherence",
            "score": _score(coherence_report.get("harmony_score")),
            "source": coherence_report.get("json_path", "layer_coherence"),
            "scope": "architecture_smoke",
        },
        {
            "axis": "long_context_exact_10m",
            "score": 100.0 if int(memory_report.get("measured_tokens", 0)) >= 10_000_000 and memory_report.get("passkey_recall", {}).get("passed") else 0.0,
            "source": memory_report.get("report_path", "extreme_memory"),
            "scope": "exact_archive_recall",
        },
    ]
    full_gate = knowledge_report.get("full_cycle_gate", {})
    rows.append(
        {
            "axis": "pure_data_full_cycle",
            "score": 100.0 if full_gate.get("passed") else 0.0,
            "source": knowledge_report.get("json_path", "knowledge_full_cycle"),
            "scope": "data_train_eval_gate",
        }
    )
    for row in external_results:
        rows.append(
            {
                "axis": f"external_{row.get('name', row.get('model', 'unknown'))}",
                "score": _score(row.get("score")),
                "source": row.get("source", "imported_external"),
                "scope": row.get("scope", "external_import"),
            }
        )
    return rows


def build_world_class_eval(
    out_dir: str | Path,
    knowledge_report: str | Path | None = "reports/knowledge_full_cycle_pursuit_512/knowledge_full_cycle_report.json",
    compact_report: str | Path | None = "reports/compact_intelligence/compact_intelligence_dossier.json",
    coherence_report: str | Path | None = "reports/axiomweave_coherence/layer_coherence_report.json",
    memory_report: str | Path | None = "reports/extreme_memory_10m/extreme_memory_report.json",
    external_results: str | Path | None = None,
) -> dict:
    knowledge = _load(knowledge_report)
    compact = _load(compact_report)
    coherence = _load(coherence_report)
    memory = _load(memory_report)
    external = _load(external_results).get("results", []) if external_results else []
    rows = _metric_rows(knowledge, compact, coherence, memory, external)
    measured_axes = [row for row in rows if row["score"] > 0]
    weak_axes = [row["axis"] for row in rows if row["score"] < 50.0]
    balanced_score = sum(row["score"] for row in rows) / max(len(rows), 1)
    hard_blockers = []
    if weak_axes:
        hard_blockers.append("some_axes_below_50")
    if not external:
        hard_blockers.append("no_imported_external_top_model_results")
    if not compact.get("claim_gate") or compact.get("claim_gate", {}).get("can_claim_smarter_than_larger_models") is not True:
        hard_blockers.append("compact_intelligence_claim_gate_blocked")
    report = {
        "schema_version": "tinymind-world-class-eval-v1",
        "created_at": datetime.now(timezone.utc).isoformat(),
        "purpose": "Measure broad intelligence readiness against world-class model standards without fabricating ranks.",
        "targets": WORLD_EVAL_TARGETS,
        "world_model_slots": WORLD_MODEL_SLOTS,
        "metrics": rows,
        "summary": {
            "balanced_score": balanced_score,
            "measured_axis_count": len(measured_axes),
            "axis_count": len(rows),
            "weak_axes": weak_axes,
            "hard_blockers": hard_blockers,
        },
        "claim_gate": {
            "ready_for_world_top_comparison": len(measured_axes) >= 7,
            "can_claim_better_than_top_world_models": False,
            "can_claim_production_ready_eval_packet": not not rows,
            "reason": "Official/provider ranks and strong scores across every axis are required before superiority claims.",
        },
        "submission_next_steps": [
            "publish HF-compatible model/repo or eval Space",
            "expose OpenAI-compatible public endpoint for LMArena-style testing",
            "prepare Artificial Analysis provider endpoint metadata",
            "import official result JSON/CSV into this harness and rerun claim gate",
        ],
    }
    out = Path(out_dir)
    out.mkdir(parents=True, exist_ok=True)
    json_path = out / "world_class_eval_report.json"
    md_path = out / "world_class_eval_report.md"
    csv_path = out / "world_class_eval_metrics.csv"
    report["json_path"] = str(json_path)
    report["markdown_path"] = str(md_path)
    report["csv_path"] = str(csv_path)
    json_path.write_text(json.dumps(report, ensure_ascii=False, indent=2, sort_keys=True), encoding="utf-8")
    md_path.write_text(_markdown(report), encoding="utf-8")
    _write_csv(csv_path, rows)
    return report


def _write_csv(path: Path, rows: list[dict]) -> None:
    with path.open("w", encoding="utf-8", newline="") as f:
        writer = csv.DictWriter(f, fieldnames=["axis", "score", "source", "scope"])
        writer.writeheader()
        writer.writerows(rows)


def _markdown(report: dict) -> str:
    lines = [
        "# TinyMind World-Class Eval Report",
        "",
        f"- Balanced score: {report['summary']['balanced_score']:.2f}",
        f"- Measured axes: {report['summary']['measured_axis_count']}/{report['summary']['axis_count']}",
        f"- Can claim better than top world models: {report['claim_gate']['can_claim_better_than_top_world_models']}",
        "",
        "## Metrics",
        "",
        "| Axis | Score | Scope | Source |",
        "|---|---:|---|---|",
    ]
    for row in report["metrics"]:
        lines.append(f"| {row['axis']} | {row['score']:.2f} | {row['scope']} | {row['source']} |")
    lines.extend(["", "## External Targets", ""])
    for target in report["targets"]:
        lines.append(f"- [{target['name']}]({target['url']}): {target['status']}")
    lines.extend(["", "## Blockers", ""])
    for blocker in report["summary"]["hard_blockers"]:
        lines.append(f"- {blocker}")
    return "\n".join(lines) + "\n"