#!/usr/bin/env python3 """Generate the Chinese final report from frozen statistics, never media or states.""" # Chinese report prose is kept intact for editorial review. # ruff: noqa: E501 import argparse import hashlib import json from pathlib import Path from mcss.mechanism_pilot.optimization_bounds_statistics import CURRENT, GTFREE, group_key def read(root, name): return json.loads((root / f"{name}.json").read_text()) def number(value): return f"{value:.6f}" def stat(value): return f"{value['mean']:.6f}，95% CI [{value['ci95'][0]:.6f}, {value['ci95'][1]:.6f}]" def run(root, docs=None, secondary=None): root = Path(root) docs = Path(docs) if docs else root results = read(root, "optimization_results") suff = read(root, "optimization_sufficiency") bounds = read(root, "bounds_analysis") oracle = read(root, "query_oracle_diagnostic") bootstrap = read(root, "bootstrap_results") costs = read(root, "cost_analysis") rules = read(root, "decision_rules") groups, oracle_groups = results["context_only"], oracle["groups"] primary_key = group_key("RGBD", GTFREE, 10000) if results["primary_key"] != primary_key or results["budgets"] != [1000, 3000, 10000]: raise ValueError("Report only accepts the frozen primary and budget list") primary = groups[primary_key] decision = suff["primary"] label = f"RGBD|{GTFREE}|FIXED_BUDGET" comparisons = bootstrap["comparisons"] gain = comparisons[f"query_gain|{label}|first_to_max"] context_gain = comparisons[f"context_gain|{label}|first_to_max"] gap = oracle["context_minus_oracle"][f"{GTFREE}|10000"] bounds_main = bounds["RGBD|FIXED_BUDGET"] gate = rules["engineering_good"] oracle_primary = oracle_groups[group_key("QUERY_ORACLE", GTFREE, 10000)]["metrics"][ "depth_absrel" ] oracle_good_count = sum( v <= gate["scene_absrel_max"] for v in oracle_primary["per_scene"].values() ) oracle_good = ( oracle_primary["mean"] <= gate["mean_absrel_max"] and oracle_good_count / len(oracle_primary["per_scene"]) >= gate["scene_fraction_min"] ) context_inference = not decision["ENGINEERING_GOOD"] and oracle_good representation_bounds = not decision["ENGINEERING_GOOD"] and not oracle_good diagnostic_interpretation = ( "主 context-direct 尚未达到工程好结果门槛，而同一预定 GT-free/10k 的 query-supervised oracle 达到门槛，" "支持 context inference / visibility / context evidence 是剩余问题之一。应优先研究如何从合法 context 推断几何。" "这不是排他的因果定位，也未证明 representation/bounds 没有任何限制。" if context_inference else "context-direct 与有限预算 oracle 都未达到工程好结果门槛，representation/bounds 是待查候选；" "必须同时保留优化不足解释，不能据此证明表示不可能。" if representation_bounds else "主 context-direct 已通过工程门槛；这两个用户第24节的失败诊断条件均未触发，是否训练仍由完整 readiness 决定。" ) secondary_cost_note = "Secondary 成本尚未纳入此报告，不能把主阶段累计称为全实验总成本。" primary_cost_note = ( f"主阶段（context + query oracle，119 条轨迹）累计 worker optimization " f"{costs['actual_trajectory_optimization_seconds']:.3f} 秒；" "主阶段 phase wall-time 独立记录为 " + json.dumps(costs.get("phase_wall_times", {"status": "NOT_RECORDED"}), ensure_ascii=False) + "。" ) secondary_value = "NOT_RUN" secondary_note = "RGB-only secondary 尚未运行；主结论不能外推为无深度的几何适配已解决。" if secondary: secondary_root = Path(secondary) secondary_audit = read(secondary_root, "secondary_decision_audit") if secondary_audit.get("status") != "PASS" or not secondary_audit.get( "primary_bits_exactly_unchanged" ): raise PermissionError("Secondary may not alter primary decisions") secondary_costs = read(secondary_root, "cost_analysis") secondary_seconds = ( secondary_costs["actual_trajectory_optimization_seconds"] - costs["actual_trajectory_optimization_seconds"] ) if secondary_seconds < -1e-6: raise ValueError("Combined trajectory cost cannot be below primary cost") secondary_cost_note = ( f"Secondary（34 条轨迹）累计 worker optimization {secondary_seconds:.3f} 秒；" f"全阶段（153 条轨迹）累计 worker optimization " f"{secondary_costs['actual_trajectory_optimization_seconds']:.3f} 秒；" "secondary phase wall-time 独立记录为 " + json.dumps( secondary_costs.get("secondary_phase_wall_times", {"status": "NOT_RECORDED"}), ensure_ascii=False, ) + "。" ) secondary_results = read(secondary_root, "optimization_results") secondary_cell = secondary_results["context_only"][group_key("RGB_ONLY", GTFREE, 10000)] secondary_value = number(secondary_cell["metrics"]["depth_absrel"]["mean"]) secondary_note = ( "RGB-only secondary query AbsRel=" + stat(secondary_cell["metrics"]["depth_absrel"]) + "；RGB_ONLY−RGBD=" + stat(secondary_results["secondary"]["RGB_ONLY_minus_RGBD"]) + "。这是主分析之后的固定配置诊断，不改变主决策。" ) tests = read(root, "tests") if (root / "tests.json").exists() else {"status": "PENDING"} integrity = ( read(root, "integrity") if (root / "integrity.json").exists() else {"status": "PENDING"} ) terminal = {"N_SCENES": len(results["scene_ids"]), "GRID": 16, "RENDER_SAMPLES": 64} for prefix, mode in [("CURRENT", CURRENT), ("GTFREE", GTFREE)]: for budget in (1000, 3000, 10000, 30000): terminal[f"{prefix}_{budget // 1000}K_ABSREL"] = ( number(groups[group_key("RGBD", mode, budget)]["metrics"]["depth_absrel"]["mean"]) if budget != 30000 else "NOT_RUN_PREDECLARED_COST_LIMIT" ) terminal.update( BEST_CONTEXT_ONLY_CONFIG=f"{primary_key}; PREDECLARED_PRIMARY_NOT_QUERY_MINIMUM", BEST_CONTEXT_ONLY_ABSREL=number(primary["metrics"]["depth_absrel"]["mean"]), QUERY_GAIN_1K_TO_MAX=number(gain["mean"]), QUERY_GAIN_CI=gain["ci95"], CONTEXT_GAIN_1K_TO_MAX=number(context_gain["mean"]), GENERALIZATION_GAP_MAX=number(primary["generalization_gap"]["mean"]), QUERY_ORACLE_CURRENT_MAX=number( oracle_groups[group_key("QUERY_ORACLE", CURRENT, 10000)]["metrics"]["depth_absrel"][ "mean" ] ), QUERY_ORACLE_GTFREE_MAX=number( oracle_groups[group_key("QUERY_ORACLE", GTFREE, 10000)]["metrics"]["depth_absrel"][ "mean" ] ), CONTEXT_TO_ORACLE_GAP=number(gap["mean"]), OPTIMIZATION_STATUS=decision["OPTIMIZATION_STATUS"], BOUNDS_STATUS=bounds_main["BOUNDS_STATUS"], ENGINEERING_GOOD=decision["ENGINEERING_GOOD"], CARRIER_TRAINING_READINESS=decision["CARRIER_TRAINING_READINESS"], MATCHED_CARRIER_TRAINING_RECOMMENDED=decision["MATCHED_CARRIER_TRAINING_RECOMMENDED"], RGB_ONLY_SECONDARY_ABSREL=secondary_value, CONTEXT_INFERENCE_BOTTLENECK=context_inference, REPRESENTATION_OR_BOUNDS_BOTTLENECK=representation_bounds, FINAL_HOLDOUT_TOUCHED=False, NEW_CARRIER_TRAINED=False, DYNAMIC_TTT_RUN=False, TESTS=tests.get("status", "UNKNOWN"), FINAL_INTEGRITY=integrity.get("status", "PENDING"), REPORT_DIR=str(docs), ) recommendation = ( "下一轮单独建立 EXP-3D-16G-MATCHED-CARRIER-TRAINING-V1；本轮仅给出建议，未训练新 carrier。" if decision["MATCHED_CARRIER_TRAINING_RECOMMENDED"] else "本轮不建议立即训练新 carrier。先按优化状态及 context−oracle gap 制定独立协议，检查优化充分性、context inference、visibility 与监督；不能用有限预算 oracle 的失败证明表示不可能。" ) lines = [ "# EXP-3D-16G-OPTIMIZATION-BOUNDS-V1", "", f"主配置为提前指定的 RGBD / 16³ / renderer64 / GT-free bounds / 10000 steps / FIXED_BUDGET。" f"query AbsRel={stat(primary['metrics']['depth_absrel'])}。" f"OPTIMIZATION_STATUS={decision['OPTIMIZATION_STATUS']}；" f"BOUNDS_STATUS={bounds_main['BOUNDS_STATUS']}；" f"CARRIER_TRAINING_READINESS={decision['CARRIER_TRAINING_READINESS']}。", "", "这是 17 个已暴露场景上的归因实验，不是新独立确认。30k 在正式运行前因成本排除，" "NOT_RUN；不根据 query 增加预算。BEST 字段只是 PREDECLARED_PRIMARY，不是事后最低 query。", "", "| Bounds | Steps | Context AbsRel | Query AbsRel (95% CI) | Query−context gap |", "|---|---:|---:|---|---:|", ] for mode in (CURRENT, GTFREE): for budget in results["budgets"]: cell = groups[group_key("RGBD", mode, budget)] lines.append( f"| {mode} | {budget} | {cell['context_metrics']['depth_absrel']['mean']:.6f} | " f"{stat(cell['metrics']['depth_absrel'])} | {cell['generalization_gap']['mean']:.6f} |" ) answers = [ ( "1000 steps 是否明显 underoptimized？", "不能仅凭 objective 下降判断。GT-free 1k→10k query gain=" + stat(gain) + f"；按完整预注册规则得到 {decision['OPTIMIZATION_STATUS']}。", ), ( "3k / 10k / 30k 带来多少 query gain？", "；".join( f"{t['from_budget']}→{t['to_budget']}=" + stat(t["query_gain"]) for t in suff["transitions"][label] ) + "。30k=NOT_RUN。两 bounds、两 selection 的全部相邻和首末差值见 bootstrap_results.json。", ), ( "context objective 下降时 query 是否继续改善？", "各相邻预算的 scene 方向计数：" + json.dumps( [ {"from": t["from_budget"], "to": t["to_budget"], **t["scene_joint_directions"]} for t in suff["transitions"][label] ], ensure_ascii=False, ) + "。这些三类不是全部可能方向；完整每 scene 差值保存在统计 JSON。", ), ( "是否出现 context overfit？", f"预注册状态为 {decision['OPTIMIZATION_STATUS']}。只有 QUERY_OVERFIT 分类建立该项系统性证据；其他状态不排除个别场景 query 变差。", ), ( "16³ 最大充分 budget 的 query AbsRel 是多少？", stat(primary["metrics"]["depth_absrel"]) + f"；最大已运行预算为10k，plateau scene fraction={decision['plateau_scene_fraction_at_max']:.6f}。不能把最大已运行预算自动称为充分优化或全局最优。", ), ( "GT-free 是否稳定优于 current？", "10k CURRENT−GT-free=" + stat(bounds_main["gains_by_budget"]["10000"]) + f"；正式分类 {bounds_main['BOUNDS_STATUS']}，稳定收益同时要求均值≥.02、CI下界>0、≥75%场景不变差。", ), ( "bounds 收益是否依赖 optimization budget？", "收益差的差 (10k bounds gain − 1k bounds gain)=" + stat(bounds_main["interaction_max_minus_first"]) + "。判断基于 RGBD 同 role 同 seed 的比较。", ), ( "Query oracle 更多优化后能到多少？", "10k CURRENT=" + stat( oracle_groups[group_key("QUERY_ORACLE", CURRENT, 10000)]["metrics"]["depth_absrel"] ) + "；GT-free=" + stat( oracle_groups[group_key("QUERY_ORACLE", GTFREE, 10000)]["metrics"]["depth_absrel"] ) + "。两 query 共享 state；监督使用 query GT，只有诊断意义。", ), ( "Context-direct 与 oracle gap 是否缩小？", "GT-free 10k context−oracle=" + stat(gap) + "；1k gap−10k gap=" + stat(oracle["oracle_gap_shrinkage_first_to_max"][GTFREE]) + "。正数表示 gap 缩小，不单独证明因果机制。", ), ( "主要剩余瓶颈是什么？", f"证据标签：optimization={decision['OPTIMIZATION_STATUS']}，bounds={bounds_main['BOUNDS_STATUS']}。" f"CONTEXT_INFERENCE_BOTTLENECK={str(context_inference).lower()}；" f"REPRESENTATION_OR_BOUNDS_BOTTLENECK={str(representation_bounds).lower()}。" f"Oracle 工程门槛：均值={oracle_primary['mean']:.6f}，" f"{oracle_good_count}/{len(oracle_primary['per_scene'])} 个场景 AbsRel≤{gate['scene_absrel_max']}。" + diagnostic_interpretation + " 这两个 flag 仅按用户第24节和既有工程门槛派生，不改变冻结的三项主分类。", ), ( "是否值得训练新的16³ carrier？", f"{decision['CARRIER_TRAINING_READINESS']}；engineering-good={decision['ENGINEERING_GOOD']}。逐项资格检查：" + json.dumps(decision["checks"], ensure_ascii=False) + "。READY_WITH_LIMITATION 明确表示尚未通过工程好结果门槛。", ), ("下一轮训练 carrier 还是先修 context inference？", recommendation), ] for index, (question, answer) in enumerate(answers, 1): lines.extend(["", f"**{index}. {question}**", "", answer]) lines.extend( [ "", "辅助检查：frozen carrier−主配置=" + stat(suff["primary_capacity_gap"]) + "；wrong-scene−正确 scene=" + stat(suff["primary_wrong_scene_damage"]) + "；query−context=" + stat(primary["generalization_gap"]) + "。", "", secondary_note, "", "统计单位是 scene：先平均 query，再平均 A/B，再场景等权；10,000 paired scene bootstrap，" "seed=20260927，95% percentile CI。per_scene、median、improved/tied/worse、LOSO、top1/top3 贡献保存在 bootstrap_results.json。" "两条 selection 均完整报告，CONTEXT_SELECTED 只使用预算内 full context objective，不能替换主配置。", "", "Oracle CURRENT 的 joint_query seed 与 GT-free A/B seed 不配对；同 bounds 的 RGBD 与 QUERY_ORACLE 也因 track 不同而 seed 不同。" "Oracle 跨 bounds 差异不是纯 bounds 因果效果，也不参与 BOUNDS_STATUS 主判定。", "", "固定 kernel 与 CPU regression 可以精确检查；CUDA 浮点复算不承诺 bitwise 相等，需报告实际容差与误差。" "状态 hash 完整性和数值复算容差是不同检查，不能混称。", "", "成本按真实共享轨迹计一次，不能把不同 budget/selection 的累计时间再相加。四子进程共享 GPU，" "累计 worker seconds 不等于 phase wall time，也不可直接当独占 GPU 延迟。未来 carrier training 成本未实测。", "", primary_cost_note + secondary_cost_note + "每配置 seconds/state、total seconds、allocated/reserved VRAM 和 render latency 见 cost_analysis.json；所有七组曲线见 figures/。", "", "FINAL_HOLDOUT_TOUCHED=false；NEW_CARRIER_TRAINED=false；DYNAMIC_TTT_RUN=false。" "没有新增模型训练、没有开启 holdout；direct-state 优化属于已授权归因实验。", "", "复现命令见 commands.sh；raw、配置、锁定、访问日志、测试和完整性证据均在本实验目录，" "checkpoint 文件以本地 outputs 与发布索引为准。报告生成不加载任何媒体、模型或 checkpoint。", ] ) summary = ( "16G OPTIMIZATION SUFFICIENCY + GT-FREE BOUNDS FINAL\n" + "\n".join( f"{key}={str(value).lower() if isinstance(value, bool) else value}" for key, value in terminal.items() ) + "\n" ) audit = { "status": "PASS", "request_source": "User protocol section 24: poor context-direct with good query oracle => context inference diagnostic; both poor => representation/bounds candidate diagnostic", "decision_rules_sha256": hashlib.sha256( (root / "decision_rules.json").read_bytes() ).hexdigest(), "report_source_sha256": hashlib.sha256(Path(__file__).read_bytes()).hexdigest(), "diagnostic_scope": "Report-only derived flags; not exclusive causal attribution; no frozen statistics, thresholds or primary decision changed", "primary_key": primary_key, "engineering_thresholds": gate, "context_engineering_good": decision["ENGINEERING_GOOD"], "oracle_engineering_good": oracle_good, "oracle_scene_good_count": oracle_good_count, "oracle_scene_count": len(oracle_primary["per_scene"]), "CONTEXT_INFERENCE_BOTTLENECK": context_inference, "REPRESENTATION_OR_BOUNDS_BOTTLENECK": representation_bounds, "cost_scope": "Primary119 cumulative worker seconds separate from secondary34 and combined153; wall times reported separately; no prefix double counting", "secondary_cost_included": secondary is not None, } (root / "audit").mkdir(exist_ok=True) (root / "audit/report_contract_completion.json").write_text( json.dumps(audit, indent=2, sort_keys=True) + "\n" ) (root / "README.md").write_text("\n".join(lines) + "\n") (root / "STATUS.md").write_text("```text\n" + summary + "```\n") (root / "terminal_summary.txt").write_text(summary) print(summary) return terminal if __name__ == "__main__": parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--root", type=Path, required=True) parser.add_argument("--docs", type=Path) parser.add_argument("--secondary", type=Path) args = parser.parse_args() run(args.root, args.docs, args.secondary)