exp: 补提交轮次40 的 K 成本探针产物（此前漏在未跟踪文件里，是候选档位成本-收益曲线的一手数据）

docs: 日志整体倒序——候选表之后的 75 个小节全部按轮次号降序重排
之前的"倒序"只在 ## 轮次 NN 之间成立：记分板/续篇/按 issue 号命名的条目(如 #27、#24、判别定案) 各有自己的位置，所以打开后第 4 屏才看到新轮次，且轮次 39 那一族曾压在 52 上面。现在规则统一：文件头 + 轮次统计 + 算法优化候选常驻最前，其后所有小节按轮次号降序，无轮次号的行政/教训类(分支清理、事故记录)按约定放最后。轮次号取自标题里的"轮次 NN"；标题没有的从正文找(#27 那种)，正文也没有的走显式定年表 (两条 #21 的条目按 round40 产物定为轮次 40)。同轮次内保持原有相对顺序，不乱改。脚本 ml/runs/_sort_log_reverse.py（默认 dry-run，--apply 才写盘），带三重守恒断言：字符数相同、逐行多重集相同、排序前后 diff -q 与 HEAD 完全一致 ⇒ 这次改动是纯重排 (72 增/72 删只是块边界)。LF 未被整体改写成 CRLF。
2026-10-21 02:03:49 +08:00 · 2026-10-21 01:59:29 +08:00 · 2026-10-21 01:57:18 +08:00 · 2026-10-21 01:55:57 +08:00 · 2026-10-21 01:48:46 +08:00 · 2026-10-21 01:31:11 +08:00
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    },
    {
      "i": 175,
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      "tight_group_pair": 18,
      "bound_pair_mm": 0.8185,
      "endpoint_gap_sum_mm": 10.77208,
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      "queries_used_batch32": 2
    },
    {
      "i": 180,
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    },
    {
      "i": 180,
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    {
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    {
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      "bound_pair_mm": 29.09,
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
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    {
      "i": 255,
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    {
      "i": 255,
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  ]
 }
@@ -0,0 +1,24 @@
 {
  "baseline_commit": "95da491",
  "poses": "first 64 existing parity poses",
  "n_poses": 64,
  "kin_fp": "2ebcbf17",
  "dtype": "float64",
  "degenerate_capsules": [
    686
  ],
  "pairs_with_second_degenerate": 670,
  "evaluated_pairs": 42880,
  "changed_pairs_over_1e_8": 20661,
  "old_minus_new_min_mm": 0.0,
  "old_minus_new_max_mm": 86.00309853133399,
  "new_reverse_max_abs_mm": 0.0,
  "worst": {
    "pose": 29,
    "first_capsule": 430,
    "second_capsule": 686,
    "old_distance_mm": 493.354753665718,
    "new_distance_mm": 407.35165513438403
  },
  "scope": "Raw capsule distances; does not imply these pairs win the group minimum."
 }
@@ -0,0 +1,16 @@
 {
  "baseline_commit": "95da491",
  "sample_source": "docs/experiment_round31_certificates.json",
  "segments": 48,
  "dense_points": 7586,
  "max_step_deg": 0.01,
  "cap_mm": 0.5,
  "dtype": "float64",
  "device": "cuda",
  "kin_fp": "2ebcbf17",
  "max_group_gap_difference_mm": 0.0,
  "group_gaps_changed_over_1e_8": 0,
  "minimum_gaps_changed_over_1e_8": 0,
  "collision_bool_mismatches": 0,
  "wall_s": 4.18259759999637
 }
@@ -0,0 +1,10 @@
 {
 "n": 2000,
 "fk_max_abs_mm": 4.547473508864641e-13,
 "orient_max_abs": 3.3306690738754696e-16,
 "gap_max_abs_mm": 2.5224267119483557e-13,
 "collide_bool_mismatch": 0,
 "feat_max_abs": 5.960463655974024e-08,
 "grad_rel_err_max": 1.8474076070450906e-06,
 "PASS": true
 }
@@ -0,0 +1,22 @@
 {
  "scope": "全部 48 段 × 15 组对 = 720 项检验；观察值按 cap=2.0 mm 钳制，故可检验命题取 min(预测下限, cap)。违例=实测最小间隙低于该下限。",
  "checks": 720,
  "violations": [],
  "worst_margin_mm": 0.0,
  "min_slack_mm": 1e-06,
  "dense_cap_mm": 2.0,
  "grid_points_per_segment": 121,
  "supersedes": "docs/experiment_round32_uniform_validation.json 里 bound_checks=61 的部分覆盖版本",
  "kin_fp": "2ebcbf17",
  "wall_s": 1.66,
  "nontrivial": {
    "definition": "只有预测下限 < 观察 cap 才是有信息量的检验；其余组对被钳制恒等式自动满足",
    "checks": 61,
    "min_slack_mm": 0.035347,
    "p10_slack_mm": 0.544274,
    "violations": 0,
    "pred_mm_median_all": 15.8639,
    "pred_mm_max_all": 19.9582
  },
  "note": "720 项总检验 0 违例，其中有信息量的是 61 项；预测下限的中位数 15.9 mm 远大于观察 cap 2 mm，所以大部分组对无法在本网格上证伪"
 }
@@ -0,0 +1,190 @@
 {
  "device": "cuda float64",
  "torch": "2.10.0+cu130",
  "kin_fp": "2ebcbf17",
  "roots_per_call": 2,
  "pairs": 24,
  "warm": 3,
  "reps": 7,
  "segment_sample": "docs/experiment_round31_certificates.json 的 48 段两两成对",
  "design": "每对根轮流按正序/逆序测完全部配置（3次预热+7次计时）；uniform_auto 的宽相位=最大子段界+2eps，uniform_cap20 固定 20 mm，bisect 为第31轮逐层二分口径",
  "results": [
    {
      "kind": "uniform_auto",
      "n_or_budget": 8,
      "points": 18,
      "cap_used_mm": 5.9191,
      "calls": 168,
      "p50_ms": 9.684,
      "p90_ms": 13.027,
      "p99_ms": 15.157,
      "max_ms": 17.537,
      "over_20ms": 0,
      "roots": 48,
      "safe": 26,
      "collision": 1,
      "rejected": 21
    },
    {
      "kind": "uniform_auto",
      "n_or_budget": 16,
      "points": 34,
      "cap_used_mm": 2.9597,
      "calls": 168,
      "p50_ms": 14.137,
      "p90_ms": 18.454,
      "p99_ms": 20.866,
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      "over_20ms": 5,
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      "safe": 30,
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    },
    {
      "kind": "uniform_auto",
      "n_or_budget": 32,
      "points": 66,
      "cap_used_mm": 1.4799,
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    },
    {
      "kind": "uniform_auto",
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      "cap_used_mm": 0.7401,
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    },
    {
      "kind": "uniform_auto",
      "n_or_budget": 128,
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      "cap_used_mm": 0.3701,
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    },
    {
      "kind": "uniform_cap20",
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    },
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    },
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      "kind": "uniform_cap20",
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      "points": 130,
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      "p99_ms": 88.355,
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    },
    {
      "kind": "uniform_cap20",
      "n_or_budget": 128,
      "points": 258,
      "cap_used_mm": 20.0,
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      "p99_ms": 167.042,
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    },
    {
      "kind": "bisect",
      "n_or_budget": 32,
      "points": null,
      "cap_used_mm": 20.0,
      "calls": 168,
      "p50_ms": 54.885,
      "p90_ms": 82.151,
      "p99_ms": 117.205,
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    },
    {
      "kind": "bisect",
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      "cap_used_mm": 20.0,
      "calls": 168,
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      "p99_ms": 122.533,
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      "collision": 1,
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    }
  ],
  "wall_s": 108.63
 }
@@ -0,0 +1,62 @@
 {
  "segments": 48,
  "kin_fp": "2ebcbf17",
  "dense_cap_mm": 2.0,
  "eps_mm": 0.0001,
  "bound_falsification": "每条组对：预测下限 (ga+gb-bound)/2 应 ≤ 网格上该组对的最小实测间隙",
  "per_segment": [
    {
      "n": 8,
      "safe": 26,
      "collision": 1,
      "unproven": 21,
      "outside_box": 0,
      "false_accept_vs_dense": 0,
      "cap_required_mm": 5.919107314806348,
      "known_missed_segment_collision": true
    },
    {
      "n": 16,
      "safe": 30,
      "collision": 1,
      "unproven": 17,
      "outside_box": 0,
      "false_accept_vs_dense": 0,
      "cap_required_mm": 2.959653657403174,
      "known_missed_segment_collision": true
    },
    {
      "n": 32,
      "safe": 34,
      "collision": 1,
      "unproven": 13,
      "outside_box": 0,
      "false_accept_vs_dense": 0,
      "cap_required_mm": 1.479926828701587,
      "known_missed_segment_collision": true
    },
    {
      "n": 64,
      "safe": 36,
      "collision": 1,
      "unproven": 11,
      "outside_box": 0,
      "false_accept_vs_dense": 0,
      "cap_required_mm": 0.7400634143507935,
      "known_missed_segment_collision": true
    },
    {
      "n": 128,
      "safe": 38,
      "collision": 1,
      "unproven": 9,
      "outside_box": 0,
      "false_accept_vs_dense": 0,
      "cap_required_mm": 0.37013170717539673,
      "known_missed_segment_collision": true
    }
  ],
  "bound_checks": 61,
  "bound_violations": [],
  "worst_margin_mm": 0.0
 }
@@ -0,0 +1,70 @@
 {
  "config": {
    "ckpt": "ml/runs/base_dag2_dag1/best.pt",
    "samples": "ml/data/samples",
    "tasks": null,
    "split": "val",
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    "sub_refresh": 0,
    "fb_block_gain": 0.5,
    "dump": "ml/runs/round33_base_rows.json",
    "out": "docs/experiment_round33_base.json",
    "trace": "ml/runs/round33_base.pt",
    "select": "endpoint",
    "replan": "empty",
    "safety": 1,
    "partial": 1,
    "inc_cap": 0,
    "gap_margin": 0.0,
    "max_sec": null,
    "seed": 0,
    "dls": "analytic",
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  "batch_seed_rule": "same seed per batch",
  "checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
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    "time_ok_1p3": 0.390625,
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    "travel_ratio_median": 2.4449527263641357,
    "time_mean_s": 2.3076188564300537,
    "brakes_mean": 19.25,
    "start_fail": 0.0,
    "goal_ratio_2s": 0.20997506380081177,
    "err_fail_med": 229.68746948242188,
    "err_success_med": 0.18899640440940857,
    "dls_share": 0.625,
    "time_budget": "teacher×2(≥3 s)",
    "speed_ratio_p50": 1.6600004434585571,
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 }
@@ -0,0 +1,70 @@
 {
  "config": {
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    "dump": "ml/runs/round33_both_rows.json",
    "out": "docs/experiment_round33_both.json",
    "trace": "ml/runs/round33_both.pt",
    "select": "endpoint",
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 "note": "动作块是 ±1 归一化的 16×6；l2_mean 无单位权重，越小越贴老师"
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  "checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
  "tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
  "kin_fp": "2ebcbf17",
  "act_scale": 1.0,
  "metrics": {
    "n": 256,
    "n_ref": 256,
    "success": 0.56640625,
    "collision_free_success": 0.56640625,
    "time_ok_1p3": 0.359375,
    "collision_rate": 0.0,
    "collision_steps_mean": 0.0,
    "travel_ratio_median": 2.414156198501587,
    "time_mean_s": 2.265103578567505,
    "brakes_mean": 14.1953125,
    "start_fail": 0.0,
    "goal_ratio_2s": 0.13815215229988098,
    "err_fail_med": 184.40463256835938,
    "err_success_med": 0.2881776690483093,
    "dls_share": 0.64453125,
    "time_budget": "teacher×2(≥3 s)",
    "speed_ratio_p50": 1.0000016689300537,
    "speed_ratio_p95": 1.0000101327896118,
    "speed_ratio_max": 1.0000101327896118,
    "over_vmax_rate": 0.0,
    "accel_ratio_p95": 1.0000016689300537,
    "accel_ratio_max": 1.0000101327896118,
    "reverse_p50": 0.2373737394809723,
    "reverse_mean": 0.2401755303144455,
    "reverse_moving_p50": 0.27848100662231445,
    "moving_share_p50": 0.9851852059364319,
    "brake_rate_mean": 0.07279647886753082,
    "brake_rate_p50": 0.0,
    "brake_rate_p90": 0.27435898780822754,
    "stuck_share": 0.171875,
    "n_plans_mean": 195.0,
    "success_reachable": 0.596707820892334,
    "by_cat": {
      "A": [
        128,
        100
      ],
      "B": [
        74,
        26
      ],
      "C": [
        32,
        12
      ],
      "D": [
        13,
        0
      ],
      "Dr": [
        9,
        7
      ]
    }
  },
  "simulation_wall_s": 486.8652108999959
 }
@@ -0,0 +1,110 @@
 {
  "config": {
    "ckpt": "ml/runs/base_dag2_dag1/best.pt",
    "samples": "ml/data/samples",
    "tasks": null,
    "split": "val",
    "n": 256,
    "K": 8,
    "commit": 2,
    "fb": 2.2,
    "ki": 0.0,
    "fb_scale": 0.0,
    "fb_near": 0.2,
    "fb_fallback": 0.0,
    "kdecay": 0.9,
    "kimax": 0.5,
    "fb_every": 1,
    "safety_try": 0,
    "sel_w": 0.1,
    "dls_safety": 1,
    "fb_clear": 0.0,
    "fb_cmin": 0.3,
    "sub_refresh": 0,
    "fb_block_gain": 0.5,
    "dump": "ml/runs/round52_ctl_ex_rows.json",
    "out": "docs/experiment_round52_ctl_ex.json",
    "trace": null,
    "select": "endpoint",
    "replan": "empty",
    "safety": 1,
    "partial": 1,
    "inc_cap": 1,
    "gap_margin": 0.0,
    "cert_batch": 1,
    "steps": 1,
    "z_scale": 1.0,
    "len_w": 0.0,
    "tie": 0,
    "pre_h": 0,
    "pre_near": 0.0,
    "backoff": 0,
    "max_sec": null,
    "seed": 0,
    "dls": "analytic",
    "device": "cuda"
  },
  "batch_size": 256,
  "batch_seed_rule": "same seed per batch",
  "checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
  "tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
  "kin_fp": "2ebcbf17",
  "act_scale": 1.0,
  "metrics": {
    "n": 256,
    "n_ref": 256,
    "success": 0.5234375,
    "collision_free_success": 0.5234375,
    "time_ok_1p3": 0.265625,
    "collision_rate": 0.0,
    "collision_steps_mean": 0.0,
    "travel_ratio_median": 2.615795850753784,
    "time_mean_s": 2.486567258834839,
    "brakes_mean": 19.5546875,
    "start_fail": 0.0,
    "goal_ratio_2s": 0.27920001745224,
    "err_fail_med": 204.71360778808594,
    "err_success_med": 0.21751396358013153,
    "dls_share": 0.61328125,
    "time_budget": "teacher×2(≥3 s)",
    "speed_ratio_p50": 1.0000016689300537,
    "speed_ratio_p95": 1.0000101327896118,
    "speed_ratio_max": 1.0000101327896118,
    "over_vmax_rate": 0.0,
    "accel_ratio_p95": 1.0000016689300537,
    "accel_ratio_max": 1.0000101327896118,
    "reverse_p50": 0.24741201102733612,
    "reverse_mean": 0.25991496443748474,
    "reverse_moving_p50": 0.32581019401550293,
    "moving_share_p50": 0.9587156176567078,
    "brake_rate_mean": 0.10028044879436493,
    "brake_rate_p50": 0.0051282052882015705,
    "brake_rate_p90": 0.3153846263885498,
    "stuck_share": 0.234375,
    "n_plans_mean": 195.0,
    "success_reachable": 0.5514403581619263,
    "by_cat": {
      "A": [
        128,
        96
      ],
      "B": [
        74,
        20
      ],
      "C": [
        32,
        11
      ],
      "D": [
        13,
        0
      ],
      "Dr": [
        9,
        7
      ]
    }
  },
  "simulation_wall_s": 296.3142095999938
 }
@@ -0,0 +1,110 @@
 {
  "config": {
    "ckpt": "ml/runs/base_dag2_dag1/best.pt",
    "samples": "ml/data/samples",
    "tasks": null,
    "split": "val",
    "n": 256,
    "K": 8,
    "commit": 2,
    "fb": 2.2,
    "ki": 0.0,
    "fb_scale": 0.0,
    "fb_near": 0.2,
    "fb_fallback": 0.0,
    "kdecay": 0.9,
    "kimax": 0.5,
    "fb_every": 1,
    "safety_try": 0,
    "sel_w": 0.1,
    "dls_safety": 1,
    "fb_clear": 0.0,
    "fb_cmin": 0.3,
    "sub_refresh": 0,
    "fb_block_gain": 0.5,
    "dump": "ml/runs/round53_ctl_ex_s1_rows.json",
    "out": "docs/experiment_round53_ctl_ex_s1.json",
    "trace": null,
    "select": "endpoint",
    "replan": "empty",
    "safety": 1,
    "partial": 1,
    "inc_cap": 1,
    "gap_margin": 0.0,
    "cert_batch": 1,
    "steps": 1,
    "z_scale": 1.0,
    "len_w": 0.0,
    "tie": 0,
    "pre_h": 0,
    "pre_near": 0.0,
    "backoff": 0,
    "max_sec": null,
    "seed": 1,
    "dls": "analytic",
    "device": "cuda"
  },
  "batch_size": 256,
  "batch_seed_rule": "same seed per batch",
  "checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
  "tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
  "kin_fp": "2ebcbf17",
  "act_scale": 1.0,
  "metrics": {
    "n": 256,
    "n_ref": 256,
    "success": 0.53125,
    "collision_free_success": 0.53125,
    "time_ok_1p3": 0.296875,
    "collision_rate": 0.0,
    "collision_steps_mean": 0.0,
    "travel_ratio_median": 2.636941432952881,
    "time_mean_s": 2.4600000381469727,
    "brakes_mean": 19.265625,
    "start_fail": 0.0,
    "goal_ratio_2s": 0.2750342786312103,
    "err_fail_med": 214.6193084716797,
    "err_success_med": 0.22276456654071808,
    "dls_share": 0.62890625,
    "time_budget": "teacher×2(≥3 s)",
    "speed_ratio_p50": 1.0000016689300537,
    "speed_ratio_p95": 1.0000101327896118,
    "speed_ratio_max": 1.0000101327896118,
    "over_vmax_rate": 0.0,
    "accel_ratio_p95": 1.0000016689300537,
    "accel_ratio_max": 1.0000101327896118,
    "reverse_p50": 0.25339367985725403,
    "reverse_mean": 0.2575649917125702,
    "reverse_moving_p50": 0.3117443919181824,
    "moving_share_p50": 0.9636363387107849,
    "brake_rate_mean": 0.09879808127880096,
    "brake_rate_p50": 0.0051282052882015705,
    "brake_rate_p90": 0.3384615480899811,
    "stuck_share": 0.25390625,
    "n_plans_mean": 195.0,
    "success_reachable": 0.5596708059310913,
    "by_cat": {
      "A": [
        128,
        96
      ],
      "B": [
        74,
        21
      ],
      "C": [
        32,
        12
      ],
      "D": [
        13,
        0
      ],
      "Dr": [
        9,
        7
      ]
    }
  },
  "simulation_wall_s": 290.3290144000057
 }
@@ -139,7 +139,10 @@ def main():
                stats['tgt'] += lt.mean().item()
                (seg_loss / nseg).backward()
                seg_loss = torch.zeros((), device=dev)
-                q, qp, queue, prev_d = q.detach(), qp.detach(), queue.detach(), prev_d.detach()
+                # 只 detach 状态并重置队列：段边界下一 plan 走 r=0 重新提交（新图有梯度）；
                # 沿用旧 queue 会跨过已 backward 的图（二次 backward 报错），也避免整段无梯度
                q, qp, prev_d = q.detach(), qp.detach(), prev_d.detach()
                queue = torch.zeros(B, 0, 6, device=dev)
        # BC 锚定
        X, Y, V, SG = data.batch(C['batch'] * 4)
        with torch.autocast('cuda', dtype=torch.bfloat16, enabled=amp):
@@ -52,8 +52,10 @@ def features(kin, q, q_prev, T, R, prefix, prefix_len, sub, sub_flag, gm=None, x
    out.append(sd)
    out.append(sub_flag[:, None].to(dt))
    if gm is None:
-        gm = kin.gaps(q, GAP_CAP, xf)['gm']
+        out.append(kin.gm_groups(q, GP, GAP_CAP, xf=xf) / GAP_CAP)   # 2026-10-20：只算特征用到的 8 组；xf 复用不重复算 FK
-    out.append(torch.stack([gm[:, a * 7 + b].clamp(max=GAP_CAP) / GAP_CAP for a, b in GP], -1))
+        gm = None
    if gm is not None:
        out.append(torch.stack([gm[:, a * 7 + b].clamp(max=GAP_CAP) / GAP_CAP for a, b in GP], -1))
    f = torch.cat(out, -1)
    assert f.shape[1] == F_DIM, f.shape
    return f
@@ -38,7 +38,8 @@ def seg_seg_dist(p1, q1, p2, q2, eps=1e-12):
    s = torch.where(t_lo, (-c / a.clamp_min(eps)).clamp(0, 1), s)
    s = torch.where(t_hi, ((b - c) / a.clamp_min(eps)).clamp(0, 1), s)
    t = t.clamp(0, 1)
-    # 退化（线段长度≈0）
+    # 第二条退化成点时，仍须在第一条上投影，而不是固定取其起点。
    s = torch.where(e <= eps, (-c / a.clamp_min(eps)).clamp(0, 1), s)
    s = torch.where(a <= eps, torch.zeros_like(s), s)
    t = torch.where(e <= eps, torch.zeros_like(t), t)
    c1 = p1 + d1 * s.unsqueeze(-1)
@@ -85,8 +86,36 @@ class Kin:
        gi, gj = self.cap_g[self.pi], self.cap_g[self.pj]
        self.pair_gp = torch.minimum(gi, gj) * 7 + torch.maximum(gi, gj)
        self.lever = T(K['lever'])
        table = [[0.0] * 7 for _ in range(6)]
        for group in range(1, 7):
            endpoints = [c[key] for c in caps if c['g'] == group for key in ('a', 'b')]
            if not endpoints:
                continue
            pivot, axis = K['P'][group - 1], K['D'][group - 1]
            unit = [v / math.hypot(*axis) for v in axis]
            relative = [[e[j] - pivot[j] for j in range(3)] for e in endpoints]
            radius = max(math.hypot(*v) for v in relative)
            radial = max(math.hypot(v[1] * unit[2] - v[2] * unit[1],
                                    v[2] * unit[0] - v[0] * unit[2],
                                    v[0] * unit[1] - v[1] * unit[0]) for v in relative)
            for joint in range(group):
                chain = sum(math.dist(K['P'][j], K['P'][j + 1]) for j in range(joint, group - 1))
                table[joint][group] = (chain + (radial if joint == group - 1 else radius)) * D2R
        self.cert_groups = torch.tensor([a * 7 + b for a in range(7) for b in range(a + 2, 7)], device=device)
        # 三角不等式给出全姿态界，不用随机姿态最大值冒充上界。
        self.cert_lever = T([[table[k][b] if a <= k < b else 0.0 for k in range(6)]
                             for a in range(7) for b in range(a + 2, 7)])
    # ---------------- 运动学 ----------------
    def _checked_xf(self, q, xf):
        """xf 是按行对应 q 的位姿数组，行数不匹配会静默广播出错（宽相位筛到别的行 ⇒ device assert 或错间隙）。"""
        if xf is None:
            return self.cum_xf(q)
        Rs, Ts = xf
        if Rs.shape[0] != q.shape[0] or Ts.shape[0] != q.shape[0]:
            raise ValueError('xf 的行数 %d 与 q 的行数 %d 不符' % (Rs.shape[0], q.shape[0]))
        return xf
    def cum_xf(self, q):
        """q: [B,6] 度 → Rs [B,7,3,3], Ts [B,7,3]"""
        B = q.shape[0]
@@ -110,6 +139,19 @@ class Kin:
        Rs, _ = xf if xf is not None else self.cum_xf(q)
        return Rs[:, 6]
    def jac(self, q, xf=None):
        """几何雅可比（对度）：返回 (Jv [B,3,6] mm/deg, Jw [B,3,6] 单位轴/deg)"""
        Rs, Ts = xf if xf is not None else self.cum_xf(q)
        p = self.fk(q, (Rs, Ts))
        Jv, Jw = [], []
        for k in range(6):
            d = torch.tensor(self.D[k], dtype=q.dtype, device=q.device)
            a = Rs[:, k] @ d
            o = torch.einsum('bij,j->bi', Rs[:, k], self.P[k].to(q.dtype)) + Ts[:, k]
            Jv.append(torch.linalg.cross(a, p - o, dim=-1) * D2R)
            Jw.append(a * D2R)
        return torch.stack(Jv, -1), torch.stack(Jw, -1)
    def caps_world(self, q, xf=None):
        Rs, Ts = xf if xf is not None else self.cum_xf(q)
        Rg, Tg = Rs[:, self.cap_g], Ts[:, self.cap_g]               # [B,C,3,3], [B,C,3]
@@ -131,7 +173,8 @@ class Kin:
    def _cat_gaps(parts, starts):
        out = {'gm': torch.cat([p.pop('gm') for p in parts], 0)}
        if 'b' in parts[0]:
-            for p, s in zip(parts, starts): p['b'] += s
+            # 非 in-place：b 会被 autograd 的索引反向图引用（APG 的碰撞铰链梯度路径），+= 会让 backward 报 version 错
            for p, s in zip(parts, starts): p['b'] = p['b'] + s
            for k in ('b', 'i', 'j', 'gap'): out[k] = torch.cat([p.pop(k) for p in parts])   # 逐键拼、拼完即释放
        return out
@@ -142,7 +185,7 @@ class Kin:
           2026-10-19（用户实测 4096 点 cap 40 峰值 4.46 GiB 全在拼接 8.6e7 对的 b/i/j/gap）：
             默认只返回 gm，每块算完就丢掉胶囊对，峰值只剩单块。
        """
-        xf = xf if xf is not None else self.cum_xf(q)
+        xf = self._checked_xf(q, xf)
        Rs, Ts = xf
        B = q.shape[0]
        step = max(1, self.COARSE_ENTRIES // int(self.cp_a.shape[0]))
@@ -201,6 +244,127 @@ class Kin:
        if not pairs: return {'gm': gm.view(B, 49)}
        return {'gm': gm.view(B, 49), 'b': rep_b, 'i': pi, 'j': pj, 'gap': gap}
    def gm_groups(self, q, gp, cap=40.0, xf=None):
        """只算指定组对 gp=[(a,b),...] 的最小间隙 [B,len(gp)]（特征用，避免 49 组全量）。
        数值与 gaps(q,cap)['gm'][:, a*7+b] 一致；组对在碰撞粗对里不存在时返回 cap。
        xf 可传入调用者已算好的 cum_xf(q)，避免同一姿态重复算 FK（特征路径每周期省一次）。"""
        key = tuple(gp)
        if getattr(self, '_gp_key', None) != key:
            m = torch.full((49,), -1, dtype=torch.long, device=q.device)
            for i, (a_, b_) in enumerate(gp): m[min(a_, b_) * 7 + max(a_, b_)] = i
            ga, gb = self.cl_g[self.cp_a], self.cl_g[self.cp_b]
            cg = torch.minimum(ga, gb) * 7 + torch.maximum(ga, gb)   # 簇对 → 7 连杆组对
            sel = torch.isin(cg, torch.tensor([min(a, b) * 7 + max(a, b) for a, b in gp], device=q.device))
            self._gp = (m, sel, sel.nonzero(as_tuple=True)[0]); self._gp_key = key
        self._gp49, sel, idx = self._gp
        xf = self._checked_xf(q, xf); Rs, Ts = xf; B = q.shape[0]
        with torch.no_grad():
            cc = torch.einsum('bkij,kj->bki', Rs[:, self.cl_g], self.cl_c.to(q.dtype)) + Ts[:, self.cl_g]
            d2 = (cc[:, self.cp_a[idx]] - cc[:, self.cp_b[idx]]).pow(2).sum(-1)
            keep = d2 <= (self.cp_lim[idx] + cap).pow(2)
            bb, mm = keep.nonzero(as_tuple=True)
            del keep, d2, cc
            cnt = self.cp_cnt[idx][mm]
            rep_b = torch.repeat_interleave(bb, cnt)
            off = torch.repeat_interleave(self.cp_start[idx][mm] - torch.cumsum(cnt, 0) + cnt, cnt) + torch.arange(int(cnt.sum()), device=q.device)
            pidx = off
        a, b = self.caps_world(q, xf)
        with torch.no_grad():
            pi, pj = self.pi[pidx], self.pj[pidx]
            mid = 0.5 * (a[rep_b, pi] + b[rep_b, pi] - a[rep_b, pj] - b[rep_b, pj])
            k2 = mid.pow(2).sum(-1) <= (self.pair_bl[pidx] + cap).pow(2)
            rep_b, pidx, pi, pj = rep_b[k2], pidx[k2], pi[k2], pj[k2]
        d = seg_seg_dist(a[rep_b, pi], b[rep_b, pi], a[rep_b, pj], b[rep_b, pj])
        gap = d - self.pair_need[pidx].to(q.dtype)
        gm = torch.full((B * len(gp),), float(cap), dtype=q.dtype, device=q.device)
        gm = gm.scatter_reduce(0, rep_b * len(gp) + self._gp49[self.pair_gp[pidx]], gap.clamp(max=cap), reduce='amin', include_self=True)
        return gm.view(B, len(gp))
    @torch.no_grad()
    def certify_segments(self, a, b, max_queries=32, max_depth=12, eps=1e-4, cap=20.0):
        """逐层二分认证：每根段独立查询预算，未知（超预算/超深度/非有限/越界）一律拒绝。
        判据对每条组对：子段两端间隙之和 > 该子段位移界 + 2eps（间隙取 min(gap,cap)，钳制只会更保守）。
        cap 必须 ≥ 该层子段界 + 2eps 才不会挡住本可通过的判定，故不做自适应收紧——同批其他段会改变
        钳制程度、让结果随批次而变（实测也无耗时收益，见探索日志轮次 31）。
        """
        if self.dtype != torch.float64 or a.dtype != torch.float64 or b.dtype != torch.float64:
            raise ValueError('segment certification requires float64')
        if a.shape != b.shape or a.ndim != 2 or a.shape[1] != 6:
            raise ValueError('a and b must be [count,6] with identical shape')
        # 预算给成张量时 max_depth + 1 会在 uint8 这类窄类型上回绕成 0，循环整个跳过，把碰撞段当安全返回。
        if type(max_queries) is not int or type(max_depth) is not int:
            raise ValueError('certification budgets must be plain ints')
        if max_queries < 2 or max_depth < 0 or eps <= 0 or cap <= eps:
            raise ValueError('invalid certification budget or tolerance')
        count = a.shape[0]
        status = torch.zeros(count, dtype=torch.long, device=a.device)
        inside = (((a >= self.lo) & (a <= self.hi) & (b >= self.lo) & (b <= self.hi)) | self.cont).all(-1)
        status[~inside] = 3
        root = torch.arange(count, device=a.device)
        gaps = self.gaps(torch.cat([a, b]), cap)['gm'][:, self.cert_groups]
        ga, gb = gaps[:count], gaps[count:]
        queries = torch.full((count,), 2, dtype=torch.long, device=a.device)
        bound = (b - a).abs() @ self.cert_lever.T
        for depth in range(max_depth + 1):
            finite = torch.isfinite(ga).all(-1) & torch.isfinite(gb).all(-1)
            collision = (ga <= 0).any(-1) | (gb <= 0).any(-1)
            status[root[~finite]] = 2
            status[root[collision]] = 1
            positive = (ga > eps).all(-1) & (gb > eps).all(-1)
            proven = positive & (ga + gb > bound + 2 * eps).all(-1)
            pending = (status[root] == 0) & ~proven
            if not bool(pending.any()):
                break
            a, b, ga, gb, root, bound = a[pending], b[pending], ga[pending], gb[pending], root[pending], bound[pending]
            if depth == max_depth:
                status[root] = 2
                break
            requested = torch.bincount(root, minlength=count)
            status[(status == 0) & (queries + requested > max_queries)] = 2
            keep = status[root] == 0
            a, b, ga, gb, root, bound = (t[keep] for t in (a, b, ga, gb, root, bound))
            if not root.numel():
                break
            mid = a + 0.5 * (b - a)
            gm = self.gaps(mid, cap)['gm'][:, self.cert_groups]
            queries += torch.bincount(root, minlength=count)
            a, b = torch.cat([a, mid]), torch.cat([mid, b])
            ga, gb = torch.cat([ga, gm]), torch.cat([gm, gb])
            root = torch.cat([root, root])
            bound = torch.cat([bound, bound]) / 2
        return {'safe': status == 0, 'collision': status == 1, 'unresolved': status == 2,
                'outside_box': status == 3, 'queries': queries}
    @torch.no_grad()
    def certify_uniform(self, a, b, n=64, eps=1e-4, cap=None):
        """固定层数的均匀细分认证：n 个子段只需一次 gaps 调用（n+1 个点），没有逐层依赖的往返。
        判据与 certify_segments 完全相同；界对 Δq 线性，所以子段界 = 整段界 / n。未通过即 unproven
        （拒绝），不存在“未知当安全”。cap=None 时取“最小不挡住可通过判定的宽相位”= 最大子段界 + 2eps：
        小于它钳制会把本可证明的子段压成不可证（偏保守），大于它只是多算对（偏慢），不改正确性。
        """
        if self.dtype != torch.float64 or a.dtype != torch.float64 or b.dtype != torch.float64:
            raise ValueError('segment certification requires float64')
        if type(n) is not int or n < 1 or eps <= 0 or (cap is not None and cap <= eps):
            raise ValueError('invalid subdivision count or tolerance')
        if a.shape != b.shape or a.ndim != 2 or a.shape[1] != 6:
            raise ValueError('a and b must be [count,6] with identical shape')
        count = a.shape[0]
        inside = (((a >= self.lo) & (a <= self.hi) & (b >= self.lo) & (b <= self.hi)) | self.cont).all(-1)
        bound = (b - a).abs() @ self.cert_lever.T / n                       # [count,15] 子段界
        required = float(bound.max()) + 2 * eps if count else 2 * eps
        qc = required if cap is None else cap
        alpha = torch.arange(n + 1, dtype=self.dtype, device=a.device) / n
        points = (a[:, None] + (b - a)[:, None] * alpha[None, :, None]).reshape(-1, 6)
        gm = self.gaps(points, qc)['gm'][:, self.cert_groups].view(count, n + 1, -1)
        adjacent = gm[:, :-1] + gm[:, 1:] > bound[:, None, :] + 2 * eps
        collision = (gm <= 0).any((1, 2))
        safe = inside & (gm > eps).all((1, 2)) & adjacent.all((1, 2)) & ~collision
        return {'safe': safe, 'collision': collision, 'unproven': (~safe & ~collision & inside),
                'outside_box': ~inside, 'points': n + 1, 'queries': n + 1,
                'cap_required_mm': required, 'cap_used_mm': qc}
    def min_gap(self, q, cap=40.0):
        return self.gaps(q, cap)['gm'].min(-1).values
@@ -0,0 +1,11 @@
 {
  "model": {"d": 192, "heads": 8, "ff": 768, "enc": 4, "dec": 4},
  "train": {
    "batch": 1024, "lr": 1e-3, "warmup": 1000, "steps": 24000, "wd": 0.01, "betas": [0.9, 0.95],
    "amp": "bf16", "grad_clip": 1.0,
    "w_mf": 1.0, "w_fk": 0.5, "w_smooth": 0.05, "w_value": 0.1, "w_sub": 0.1,
    "p_neq": 0.5, "p_edge": 0.1, "p_neq_warm": 5000, "fk_tmax": 0.3, "fk_caps": 96,
    "ema": 0.999, "aug_pre_drop": 0.15, "aug_pre_sigma": 0.1, "aug_dq_sigma": 0.3,
    "ckpt_min": 10, "eval_every": 2000, "eval_tasks": 512, "log_every": 50, "patience_evals": 5
  }
 }
@@ -0,0 +1,11 @@
 {
  "model": {"d": 192, "heads": 8, "ff": 768, "enc": 4, "dec": 4},
  "train": {
    "batch": 1024, "lr": 1e-3, "warmup": 1000, "steps": 24000, "wd": 0.01, "betas": [0.9, 0.95],
    "amp": "bf16", "grad_clip": 1.0,
    "w_mf": 1.0, "w_fk": 0.5, "w_smooth": 0.05, "w_value": 0.1, "w_sub": 0.1,
    "p_neq": 0.5, "p_edge": 0.1, "p_neq_warm": 5000, "fk_tmax": 0.3, "fk_caps": 96,
    "ema": 0.999, "aug_pre_drop": 0.15, "aug_pre_sigma": 0.1, "aug_dq_sigma": 0.3,
    "ckpt_min": 10, "eval_every": 2000, "eval_tasks": 512, "log_every": 50, "patience_evals": 5
  }
 }
@@ -0,0 +1,41 @@
 {
  "model": {
    "d": 192,
    "heads": 8,
    "ff": 768,
    "enc": 4,
    "dec": 4
  },
  "train": {
    "batch": 1024,
    "lr": 0.0001,
    "warmup": 1000,
    "steps": 3000,
    "wd": 0.01,
    "betas": [
      0.9,
      0.95
    ],
    "amp": "bf16",
    "grad_clip": 1.0,
    "w_mf": 1.0,
    "w_fk": 0.1,
    "w_smooth": 0.01,
    "w_value": 0.0,
    "w_sub": 0.0,
    "p_neq": 0.5,
    "p_edge": 0.1,
    "p_neq_warm": 0,
    "fk_tmax": 0.3,
    "fk_caps": 96,
    "ema": 0.999,
    "aug_pre_drop": 0.15,
    "aug_pre_sigma": 0.1,
    "aug_dq_sigma": 0.3,
    "ckpt_min": 10,
    "eval_every": 1000,
    "eval_tasks": 256,
    "log_every": 50,
    "patience_evals": 3
  }
 }
@@ -28,6 +28,10 @@ def main():
    ap.add_argument('--parts', type=int, default=8); ap.add_argument('--out', required=True)
    ap.add_argument('--bs', type=int, default=64)
    ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu')
    ap.add_argument('--fb_gain', type=float, default=0.0, help='部署分布采集：目标反馈增益（与 rollout 一致）')
    ap.add_argument('--safety', type=int, default=0, help='部署分布采集：是否开安全层')
    ap.add_argument('--replan', default='r3'); ap.add_argument('--commit_n', type=int, default=3)
    ap.add_argument('--safety_partial', type=int, default=0)
    a = ap.parse_args()
    gap_min = float(os.environ.get('MIN_GAP_MM') or 3)
    os.makedirs(a.out, exist_ok=True)
@@ -41,7 +45,8 @@ def main():
    for i in range(0, len(tasks), a.bs):
        tb = tasks[i:i + a.bs]
        with torch.no_grad():
-            _, per = rollout(model, kin, tb, a.device, K=8, safety=False, seed=a.seed * 100003 + i, return_traj=True)
+            _, per = rollout(model, kin, tb, a.device, K=8, safety=bool(a.safety), seed=a.seed * 100003 + i, return_traj=True,
                            fb_gain=a.fb_gain, replan=a.replan, commit_n=a.commit_n, safety_partial=a.safety_partial)
        tr = per['traj'].to(a.device)                                   # [B,T,6]
        B, Tn, _ = tr.shape
        _, T, R, _, _ = tasks_to_tensors(tb, a.device)
@@ -0,0 +1,57 @@
 #!/usr/bin/env python
 """反馈蒸馏：用最终部署配置（目标反馈 + 安全层 + 部分前缀 + 每步规划）跑闭环，
 收集 (特征, 复合动作 = 模型输出 + 目标反馈) 样本，供 train.py 微调把反馈内化进策略。
  PYTHONUTF8=1 python -m ml.dagger.distill_fb --ckpt ml/runs/base_dag2_dag1/best.pt --n 500 \
      --fb_gain 1.8 --out ml/data/samples_fbdistill
 输出可直接被 ml.data.Samples 读取（collect.X/Y/V/SG/TID.npy；无 meta = act_scale 1.0，与主数据一致）。
 动作标签 = 候选动作（含反馈）的归一化 act（/STEP，clamp ±1），与主数据同口径。
 """
 import argparse, os, random
 import numpy as np
 import torch
 from ml.common.ckpt import load_policy
 from ml.common.features import STEP
 from ml.common.kin_torch import Kin
 from ml.data import load_tasks
 from ml.sim.closed_loop import rollout
 def main():
    ap = argparse.ArgumentParser()
    ap.add_argument('--ckpt', required=True); ap.add_argument('--n', type=int, default=500)
    ap.add_argument('--bs', type=int, default=64); ap.add_argument('--fb_gain', type=float, default=1.8)
    ap.add_argument('--samples', default='ml/data/samples'); ap.add_argument('--out', required=True)
    ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu'); ap.add_argument('--seed', type=int, default=1)
    a = ap.parse_args()
    dev = a.device
    model, _ = load_policy(a.ckpt, dev)
    kin = Kin(device=dev)
    pool = [t for t in load_tasks([a.samples]) if t['split'] == 'train' and t.get('cat') not in ('D',)]
    random.Random(a.seed).shuffle(pool); tasks = pool[:a.n]
    stp = torch.tensor(STEP)   # collect 返回 CPU 张量，标签换算也在 CPU
    X, Y, TID = [], [], []
    for i in range(0, len(tasks), a.bs):
        ch = tasks[i:i + a.bs]
        coll = []
        with torch.no_grad():
            rollout(model, kin, ch, dev, K=8, safety=True, replan='empty', fb_gain=a.fb_gain,
                    safety_partial=1, commit_n=1, seed=a.seed * 100003 + i, collect=coll)
        for f, ac in coll:
            X.append(f.numpy()); Y.append((ac / stp).clamp(-1, 1).numpy())
        ncol = sum(f.shape[0] for f, _ in coll)
        TID.append(np.arange(ncol, dtype=np.int32) + i * 100000)
        print(f'[distill] {i + len(ch)}/{len(tasks)} tasks, samples {sum(x.shape[0] for x in X)}', flush=True)
    os.makedirs(a.out, exist_ok=True)
    X = np.concatenate(X); Y = np.concatenate(Y); TID = np.concatenate(TID)
    n = X.shape[0]
    np.save(os.path.join(a.out, 'collect.X.npy'), X.astype(np.float32))
    np.save(os.path.join(a.out, 'collect.Y.npy'), Y.astype(np.float32))
    np.save(os.path.join(a.out, 'collect.V.npy'), np.full(n, 0.5, np.float32))
    np.save(os.path.join(a.out, 'collect.SG.npy'), np.zeros((n, 13), np.float32))
    np.save(os.path.join(a.out, 'collect.TID.npy'), TID)
    print(f'done: {n} samples, X {X.shape}, Y {Y.shape}', flush=True)
 if __name__ == '__main__':
    main()
@@ -24,7 +24,7 @@ for ((r=1; r<=R; r++)); do
  SAMPLES="$SAMPLES $SD"
  if [ -f $OUT/done ]; then echo "[dagger] 第 $r 轮已完成，跳过"; CK=$($PY -m ml.common.ckpt pick $OUT); continue; fi
  if [ -f $D/collect.json ]; then echo "[dagger] 第 $r 轮起点已采集，跳过"
-  else echo "[dagger] 第 $r 轮：用 $CK 闭环采起点（$N 题）"; $PY -m ml.dagger.collect --ckpt $CK --samples ml/data/samples --n $N --seed $r --parts $P --out $D; fi
+  else echo "[dagger] 第 $r 轮：用 $CK 闭环采起点（$N 题）"; $PY -m ml.dagger.collect --ckpt $CK --samples ml/data/samples --n $N --seed $r --parts $P --out $D --fb_gain ${FB_GAIN:-0} --safety ${DAG_SAFETY:-0} --replan ${DAG_REPLAN:-r3} --commit_n ${DAG_COMMIT_N:-3} --safety_partial ${DAG_PARTIAL:-0}; fi
  cat $D/collect.json
  pids=()
  for f in $D/tasks_*.jsonl; do
@@ -0,0 +1,54 @@
 """DLS 解析雅可比对拍 + 延迟（python -m ml.eval.check_dls）"""
 import time
 import torch
 from ml.common.kin_torch import Kin
 from ml.sim.closed_loop import dls_steps
 def main():
    dev = 'cuda' if torch.cuda.is_available() else 'cpu'
    kin = Kin(device=dev)
    g = torch.Generator(device=dev); g.manual_seed(0)
    B = 64
    lo, hi = kin.lo + 20, kin.hi - 20
    q = lo + (hi - lo) * torch.rand(B, 6, device=dev, generator=g)
    xf = kin.cum_xf(q)
    p = kin.fk(q, xf)
    T = p + 6 * torch.randn(B, 3, device=dev, generator=g)
    R = torch.zeros(B, 9, device=dev)
    a = dls_steps(kin, q, T, R, n=3, analytic=True)
    b = dls_steps(kin, q, T, R, n=3, analytic=False)
    d = (a - b).abs()
    print('pos-only  max|Δdq| %.6f°  mean %.6f°  rel %.4f' % (d.max().item(), d.mean().item(), (d.norm() / b.norm()).item()))
    # 带姿态目标：绕随机轴 ±8°
    axis = torch.randn(B, 3, device=dev, generator=g); axis = axis / axis.norm(dim=-1, keepdim=True)
    ang = (torch.rand(B, 1, device=dev, generator=g) * 16 - 8) * torch.pi / 180
    Kx = torch.zeros(B, 3, 3, device=dev)
    Kx[:, 0, 1] = -axis[:, 2]; Kx[:, 0, 2] = axis[:, 1]; Kx[:, 1, 0] = axis[:, 2]
    Kx[:, 1, 2] = -axis[:, 0]; Kx[:, 2, 0] = -axis[:, 1]; Kx[:, 2, 1] = axis[:, 0]
    I = torch.eye(3, device=dev)
    Rd = I + torch.sin(ang)[..., None] * Kx + (1 - torch.cos(ang))[..., None] * (Kx @ Kx)
    Rt = kin.orient(q, xf) @ Rd
    R9 = Rt.reshape(B, 9)
    a = dls_steps(kin, q, T, R9, n=3, analytic=True)
    b = dls_steps(kin, q, T, R9, n=3, analytic=False)
    d = (a - b).abs()
    print('pos+rot   max|Δdq| %.6f°  mean %.6f°  rel %.4f' % (d.max().item(), d.mean().item(), (d.norm() / b.norm()).item()))
    for n_ in (1, 3):
        for Bn in (1, 256):
            qq = q[:Bn]; TT = T[:Bn]; RR = R9[:Bn]
            for an in (False, True):
                for _ in range(3):
                    dls_steps(kin, qq, TT, RR, n=n_, analytic=an)
                torch.cuda.synchronize() if dev == 'cuda' else None
                t0 = time.perf_counter()
                for _ in range(20):
                    dls_steps(kin, qq, TT, RR, n=n_, analytic=an)
                torch.cuda.synchronize() if dev == 'cuda' else None
                print('n=%d B=%3d analytic=%d  %.2f ms' % (n_, Bn, an, (time.perf_counter() - t0) / 20 * 1e3))
 if __name__ == '__main__':
    main()
@@ -7,7 +7,7 @@
 口径与 closed_loop.rollout 一致：每步同时检查当前状态 q 与相邻状态中点 mid 的间隙（cap=0.5 mm）；
 间隙组对索引 = gA*7+gB（gA<gB，g 为连杆组 0..5 → J1..J6）。
 """
-import argparse, json, math, os, sys
+import argparse, json, math, os, sys, time
 from collections import Counter
 import torch
@@ -25,9 +25,253 @@ def pair_names():
    return {a * 7 + b: f'{nm(a)}-{nm(b)}' for a in range(7) for b in range(a + 1, 7)}
@torch.no_grad()
 def replay_first_hits(path, out=None):
    saved = torch.load(path, map_location='cpu', weights_only=True)
    k32, k64 = Kin(), Kin(dtype=torch.float64)
    rows, offset = [], 0
    for _, per, count in saved['batches']:
        for b in (per['col'] > 0).nonzero().flatten().tolist():
            it = int(round(float(per['first_hit'][b]) / DT))
            prev, q = per['traj'][b, it - 1:it + 1]
            mid = prev + 0.5 * (q - prev)
            points = torch.stack([per['traj'][b, 0], prev, mid, q])
            gm32 = k32.gaps(points, 0.5)['gm']
            gm64 = k64.gaps(points.double(), 0.5)['gm']
            gap32, gp32 = gm32.min(-1)
            gap64, gp64 = gm64.min(-1)
            nseg = max(1, math.ceil(float((q - prev).abs().max()) / 0.01))
            dense = prev.double() + torch.linspace(0, 1, nseg + 1, dtype=torch.float64)[:, None] * (q.double() - prev.double())
            dense_gap = torch.cat([k64.gaps(chunk, 0.5)['gm'].min(-1).values for chunk in dense.split(32)])
            boundary = (~k32.cont) & ((q - k32.lo).abs().lt(1e-5) | (q - k32.hi).abs().lt(1e-5))
            row = {
                'i': offset + b, 'task': saved['tasks'][offset + b],
                'first_hit_s': float(per['first_hit'][b]), 'dls_it': int(per['dls_it'][b]),
                'point_order': ['q0', 'previous', 'midpoint', 'endpoint'],
                'gap32_mm': gap32.tolist(), 'gap64_mm': gap64.tolist(),
                'pair32': gp32.tolist(), 'pair64': gp64.tolist(),
                'boundary_joints': (boundary.nonzero().flatten() + 1).tolist(),
                'previous': prev.tolist(), 'endpoint': q.tolist(),
                'dense_nsegments': nseg, 'dense_gap64_min_mm': float(dense_gap.min()),
            }
            if 'plans' in per:
                plans = per['plans']
                pi = int(torch.searchsorted(plans['it'], it - 1, right=True)) - 1
                step = it - 1 - int(plans['it'][pi])
                ncommit = plans['commit'].shape[2]
                command = plans['commit'][b, pi, step]
                cp, cm = plans['points'][b, pi, step], plans['midpoints'][b, pi, step]
                checked = bool(plans['certified'][b, pi, step])
                predicted = k32.clamp_box((prev + command)[None])[0]
                row['certificate'] = {
                    'plan_start_it': int(plans['it'][pi]), 'step': step, 'checked': checked,
                    'endpoint': cp.tolist() if checked else None,
                    'midpoint': cm.tolist() if checked else None,
                    'recorded_gap32_mm': plans['gaps'][b, pi, [step, ncommit + step]].tolist() if checked else None,
                    'replayed_gap64_mm': k64.gaps(torch.stack([cp, cm]).double(), 0.5)['gm'].min(-1).values.tolist() if checked else None,
                    'command_delta': command.tolist(),
                    'clamped_reconstruction_max_abs_deg': float((predicted - q).abs().max()),
                    'certified_vs_executed_max_abs_deg': float((cp - q).abs().max()) if checked else None,
                }
            rows.append(row)
            print('task %d first_hit %.2fs gap32 %s gap64 %s boundary %s' %
                  (row['i'], row['first_hit_s'], row['gap32_mm'], row['gap64_mm'], row['boundary_joints']), flush=True)
        offset += count
    result = {
        'trace': path, 'n': offset, 'reported_collision_tasks': len(rows),
        'replayed_first_hits32': sum(min(r['gap32_mm'][2:]) <= 0 for r in rows),
        'replayed_first_hits64': sum(min(r['gap64_mm'][2:]) <= 0 for r in rows),
        'scope': 'Replay reported first hits only; same geometry kernel at float32/float64, not a full safety audit.',
        'rows': rows,
    }
    if out:
        with open(out, 'w', encoding='utf-8') as f:
            json.dump(result, f, indent=2, allow_nan=False)
    print('replayed first hits: %d/%d float32, %d/%d float64' %
          (result['replayed_first_hits32'], len(rows), result['replayed_first_hits64'], len(rows)), flush=True)
    return result
@torch.no_grad()
 def replay_dense(path, indices, max_step_deg=0.05, device='cpu', out=None):
    saved = torch.load(path, map_location='cpu', weights_only=True)
    kin = Kin(device=device, dtype=torch.float64)
    if saved.get('kin_fp') != kin.fp:
        raise ValueError('trace/kin fingerprint mismatch')
    if not indices or min(indices) < 0 or max(indices) >= len(saved['tasks']) or max_step_deg <= 0:
        raise ValueError('valid task indices and positive max_step_deg required')
    started = time.perf_counter()
    rows, offset = [], 0
    for _, per, count in saved['batches']:
        for index in indices:
            if not offset <= index < offset + count:
                continue
            b = index - offset
            nstep = min(per['traj'].shape[1] - 1, math.floor((float(per['limit'][b]) + 1e-6) / DT))
            if torch.isfinite(per['t_done'][b]):
                nstep = min(nstep, round(float(per['t_done'][b]) / DT))
            trajectory = per['traj'][b, :nstep + 1].double()
            points, segment_ids, fractions = [trajectory[:1]], [torch.tensor([-1])], [torch.zeros(1)]
            for it in range(nstep):
                prev, end = trajectory[it:it + 2]
                nseg = max(2, 2 * math.ceil(float((end - prev).abs().max()) / (2 * max_step_deg)))
                alpha = torch.arange(1, nseg + 1, dtype=torch.float64) / nseg
                points.append(prev + alpha[:, None] * (end - prev))
                segment_ids.append(torch.full((nseg,), it))
                fractions.append(alpha)
            points = torch.cat(points)
            segments, alpha = torch.cat(segment_ids), torch.cat(fractions)
            print('dense task %d steps %d points %d start' % (index, nstep, len(points)), flush=True)
            gaps = []
            for start in range(0, len(points), 64):
                gaps.append(kin.gaps(points[start:start + 64].to(device), 0.5)['gm'].min(-1).values.cpu())
                if (start // 64 + 1) % 64 == 0:
                    print('dense task %d points %d/%d wall %.1fs' %
                          (index, min(start + 64, len(points)), len(points), time.perf_counter() - started), flush=True)
            gaps = torch.cat(gaps)
            hit = gaps <= 0
            first = int(hit.nonzero()[0]) if hit.any() else None
            rows.append({
                'i': index, 'task': saved['tasks'][index], 'steps': nstep, 'sampled_points': len(points),
                'initial_gap64_mm': float(gaps[0]), 'min_gap64_mm': float(gaps.min()),
                'collision_segments': int(segments[hit & (segments >= 0)].unique().numel()),
                'first_collision_s': max(0.0, float(segments[first] + alpha[first]) * DT) if first is not None else None,
            })
            print('dense task %d min_gap %.9f mm collision_segments %d' %
                  (index, rows[-1]['min_gap64_mm'], rows[-1]['collision_segments']), flush=True)
        offset += count
    result = {
        'trace': str(path), 'tasks_sha256': saved.get('tasks_sha256'), 'kin_fp': kin.fp,
        'indices': indices, 'max_step_deg': max_step_deg, 'dtype': 'float64', 'device': device,
        'scope': 'Executed trajectory through each task deadline or arrival, including initial state; finite sampling, not continuous certification.',
        'collision_tasks': sum(r['min_gap64_mm'] <= 0 for r in rows), 'rows': rows,
        'audit_wall_s': time.perf_counter() - started,
    }
    if out:
        with open(out, 'w', encoding='utf-8') as f:
            json.dump(result, f, indent=2, allow_nan=False)
    return result
@torch.no_grad()
 def replay_certificates(path, indices, device='cpu', out=None, repeats=5, extra_segments=()):
    saved = torch.load(path, map_location='cpu', weights_only=True)
    kin = Kin(device=device, dtype=torch.float64)
    if saved.get('kin_fp') != kin.fp:
        raise ValueError('trace/kin fingerprint mismatch')
    if not indices or min(indices) < 0 or max(indices) >= len(saved['tasks']) or repeats < 1:
        raise ValueError('valid task indices and positive repeats required')
    rows, offset = [], 0
    for _, per, count in saved['batches']:
        for index in indices:
            if not offset <= index < offset + count:
                continue
            b = index - offset
            nstep = min(per['traj'].shape[1] - 1, math.floor((float(per['limit'][b]) + 1e-6) / DT))
            if torch.isfinite(per['t_done'][b]):
                nstep = min(nstep, round(float(per['t_done'][b]) / DT))
            traj = per['traj'][b, :nstep + 1]
            moving = ((traj[1:] - traj[:-1]).abs().max(-1).values > 1e-6).nonzero().flatten().tolist()
            if not moving:
                continue
            plans = per['plans']
            nearest, nearest_gap = None, float('inf')
            for it in moving:
                pi = int(torch.searchsorted(plans['it'], it, right=True)) - 1
                step = it - int(plans['it'][pi])
                if 0 <= step < plans['commit'].shape[2] and plans['certified'][b, pi, step]:
                    gap = float(plans['gaps'][b, pi, [step, plans['commit'].shape[2] + step]].min())
                    if gap < nearest_gap:
                        nearest, nearest_gap = it, gap
            selected = {moving[0], moving[len(moving) // 2], moving[-1]}
            if nearest is not None:
                selected.add(nearest)
            for task_index, it in extra_segments:
                if task_index == index:
                    if it not in moving:
                        raise ValueError('extra segment must be a moving segment before deadline or arrival')
                    selected.add(it)
            for it in sorted(selected):
                rows.append({'i': index, 'start_it': it, 'a': traj[it].tolist(), 'b': traj[it + 1].tolist()})
        offset += count
    if not rows:
        raise ValueError('no moving segments in selected tasks')
    a = torch.tensor([r['a'] for r in rows], dtype=torch.float64, device=device)
    b = torch.tensor([r['b'] for r in rows], dtype=torch.float64, device=device)
    started = time.perf_counter()
    print('certificate replay: %d tasks, %d fixed moving segments' % (len(indices), len(rows)), flush=True)
    summaries = {}
    for budget in (2, 8, 32):
        result = kin.certify_segments(a, b, max_queries=budget)
        summaries[str(budget)] = {key: int(result[key].sum()) for key in ('safe', 'collision', 'unresolved', 'outside_box')}
        summaries[str(budget)]['queries_p50'] = float(result['queries'].double().quantile(0.5))
        summaries[str(budget)]['queries_max'] = int(result['queries'].max())
        for j, row in enumerate(rows):
            row.setdefault('budgets', {})[str(budget)] = {key: value[j].item() for key, value in result.items()}
        print('budget %d: %s' % (budget, summaries[str(budget)]), flush=True)
    latencies = []
    for j, row in enumerate(rows):
        aa, bb = a[j:j + 1], b[j:j + 1]
        discrete = kin.gaps(torch.cat([aa, aa + 0.5 * (bb - aa), bb]), 0.5)['gm'].min(-1).values
        row['previous_midpoint_endpoint_gap_mm'] = discrete.tolist()
        nseg = max(2, 2 * math.ceil(float((bb - aa).abs().max()) / 0.02))
        alpha = torch.linspace(0, 1, nseg + 1, dtype=torch.float64, device=device)
        points = aa + alpha[:, None] * (bb - aa)
        dense = torch.cat([kin.gaps(chunk, 0.5)['gm'].min(-1).values for chunk in points.split(64)])
        row['dense_points'] = len(points)
        row['dense_min_gap_mm'] = float(dense.min())
        row['dense_min_fraction'] = float(alpha[dense.argmin()])
        for _ in range(3):
            kin.certify_segments(aa, bb)
        elapsed = []
        for _ in range(repeats):
            if a.is_cuda:
                torch.cuda.synchronize(a.device)
            t0 = time.perf_counter()
            result = kin.certify_segments(aa, bb)
            if a.is_cuda:
                torch.cuda.synchronize(a.device)
            elapsed.append((time.perf_counter() - t0) * 1000)
        row['batch_single_match'] = all(value.item() == row['budgets']['32'][key] for key, value in result.items())
        row['latency_ms'] = elapsed
        latencies.extend(elapsed)
        print('segment %d/%d task %d it %d dense %.6f mm queries %d latency %.2f ms' %
              (j + 1, len(rows), row['i'], row['start_it'], row['dense_min_gap_mm'],
               row['budgets']['32']['queries'], sum(elapsed) / len(elapsed)), flush=True)
    elapsed = torch.tensor(latencies, dtype=torch.float64)
    result = {
        'trace': str(path), 'tasks_sha256': saved.get('tasks_sha256'), 'kin_fp': kin.fp,
        'device': str(device), 'gpu': torch.cuda.get_device_name(a.device) if a.is_cuda else None,
        'torch_version': str(torch.__version__), 'cpu_threads': torch.get_num_threads(), 'dtype': 'float64',
        'indices': indices, 'extra_segments': list(extra_segments),
        'selection': 'First/middle/last moving segment and minimum original certified gap per task; plus explicit extra_segments.',
        'cap_mm': 20.0, 'eps_mm': 1e-4, 'max_depth': 12, 'dense_max_step_deg': 0.01,
        'timing': {'scope': 'B=1 certificate only, resident inputs, 3 warmups per segment; synchronized wall time, not policy or scheduling latency.',
                   'repeats_per_segment': repeats, 'calls': len(latencies),
                   'p50_ms': float(elapsed.quantile(0.5)), 'p99_ms': float(elapsed.quantile(0.99)),
                   'max_ms': float(elapsed.max()), 'over_20ms': int((elapsed > 20).sum())},
        'budgets': summaries,
        'dense_collision_segments': sum(r['dense_min_gap_mm'] <= 0 for r in rows),
        'accepted_dense_collision_segments': sum(r['dense_min_gap_mm'] <= 0 and r['budgets']['32']['safe'] for r in rows),
        'batch_single_match': all(r['batch_single_match'] for r in rows),
        'scope': 'Fixed recorded segments, not a new rollout. Dense replay is finite sampling; floating-point certification error is not formally bounded.',
        'wall_s': time.perf_counter() - started, 'rows': rows,
    }
    if out:
        with open(out, 'w', encoding='utf-8') as f:
            json.dump(result, f, indent=2, allow_nan=False)
    return result
 def main():
    ap = argparse.ArgumentParser()
-    ap.add_argument('--ckpt', required=True)
+    ap.add_argument('--ckpt')
    ap.add_argument('--trace')
    ap.add_argument('--dense_indices', help='逗号分隔的任务下标；回放实际轨迹，不依赖原 col 标记')
    ap.add_argument('--cert_indices', help='固定实际段的连续认证、加密对照和单段计时')
    ap.add_argument('--cert_segments', default='', help='额外指定任务号:零基段号，逗号分隔')
    ap.add_argument('--repeats', type=int, default=5)
    ap.add_argument('--max_step_deg', type=float, default=0.05)
    ap.add_argument('--samples', default='ml/data/samples')
    ap.add_argument('--split', default='val')
    ap.add_argument('--n', type=int, default=512)
@@ -38,6 +282,26 @@ def main():
    ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu')
    ap.add_argument('--out', default=None)
    a = ap.parse_args()
    if a.dense_indices and a.cert_indices:
        ap.error('--dense_indices and --cert_indices are mutually exclusive')
    if a.cert_segments and not a.cert_indices:
        ap.error('--cert_segments requires --cert_indices')
    if a.trace:
        if a.cert_indices:
            extra = [tuple(map(int, segment.split(':'))) for segment in a.cert_segments.split(',') if segment]
            indices = [int(i) for i in a.cert_indices.split(',')]
            if any(len(segment) != 2 or segment[0] not in indices for segment in extra):
                ap.error('--cert_segments requires task:segment pairs from --cert_indices')
            replay_certificates(a.trace, indices, a.device, a.out, a.repeats, extra)
        elif a.dense_indices:
            replay_dense(a.trace, [int(i) for i in a.dense_indices.split(',')], a.max_step_deg, a.device, a.out)
        else:
            replay_first_hits(a.trace, a.out)
        return
    if a.dense_indices or a.cert_indices:
        ap.error('--dense_indices and --cert_indices require --trace')
    if not a.ckpt:
        ap.error('--ckpt or --trace is required')
    ck = torch.load(a.ckpt, map_location=a.device)
    model = build(ck['cfg']).to(a.device)
@@ -0,0 +1,178 @@
 """小规模同步闭环评估；仿真墙钟不等于控制延迟。"""
 import argparse, functools, hashlib, json, math, time
 from pathlib import Path
 import torch
 from ml.common.ckpt import load_policy
 from ml.common.kin_torch import Kin
 from ml.data import load_tasks
 from ml.sim.closed_loop import rollout, tasks_to_tensors
 import ml.sim.closed_loop as clmod
 def run(model, kin, tasks, device, bs=256, **kw):
    per = []
    for i in range(0, len(tasks), bs):
        t0 = time.perf_counter()
        print('batch %d-%d/%d start' % (i + 1, min(i + bs, len(tasks)), len(tasks)), flush=True)
        def progress(step, total):
            print('batch %d step %d/%d wall %.1fs' % (i // bs + 1, step, total, time.perf_counter() - t0), flush=True)
        r, p = rollout(model, kin, tasks[i:i + bs], device, progress=progress, **kw)
        per.append((r, p, len(tasks[i:i + bs])))
        print('batch %d done wall %.1fs' % (i // bs + 1, time.perf_counter() - t0), flush=True)
    flat = {k: torch.cat([p[k] for _, p, _ in per]) for k in per[0][1] if k not in ('traj', 'plans')}
    _, _, _, ex, tr = tasks_to_tensors(tasks, 'cpu')
    ok, col = flat['ok'], flat['col'] > 0
    has_ref = torch.isfinite(ex)
    okr = ok & has_ref & torch.isfinite(tr)
    median = lambda x: x.median().item() if x.numel() else None
    out = {
        'n': len(tasks), 'n_ref': int(has_ref.sum()),
        'success': ok.float().mean().item(),
        'collision_free_success': (ok & ~col).float().mean().item(),
        'time_ok_1p3': (ok & has_ref & (flat['t_done'] <= 1.3 * ex)).sum().item() / int(has_ref.sum()) if has_ref.any() else None,
        'collision_rate': col.float().mean().item(),
        'collision_steps_mean': flat['col'].mean().item(),
        'travel_ratio_median': median(flat['travel'][okr] / tr[okr].clamp(min=1)),
        'time_mean_s': flat['t_done'][ok].mean().item() if ok.any() else None,
        'brakes_mean': flat['brakes'].mean().item(),
        'start_fail': flat['start_fail'].float().mean().item(),
        'goal_ratio_2s': median(flat['goal_ratio_2s']),
        'err_fail_med': median(flat['err'][~ok]),
        'err_success_med': median(flat['err'][ok]),
        'dls_share': flat['dls_it'].ge(0).float().mean().item(),
        'time_budget': ('fixed %.3f s' % kw['max_sec']) if kw.get('max_sec') is not None else
                       (per[0][0]['time_budget'] if len({r['time_budget'] for r, _, _ in per}) == 1 else 'mixed'),
    }
    if 'speed_ratio' in flat:   # 实际执行速度：每题时限内 max|Δq|/STEP（1.0 = VMAX），加速度同口径按每控制步的速度变化
        v, acc = flat['speed_ratio'], flat['accel_ratio']
        out.update({'speed_ratio_p50': median(v), 'speed_ratio_p95': v.quantile(0.95).item(),
                    'speed_ratio_max': v.max().item(), 'over_vmax_rate': (v > 1.001).float().mean().item(),
                    'accel_ratio_p95': acc.quantile(0.95).item(), 'accel_ratio_max': acc.max().item()})
    if 'reverse_rate' in flat:   # 换向步占比：行程超出主要是同一轴来回抖（轮次 39 分解），所以每个臂都要直接报它
        rr = flat['reverse_rate']
        out.update({'reverse_p50': median(rr), 'reverse_mean': rr.mean().item(),
                    'reverse_moving_p50': median(flat['reverse_rate_moving']),   # 只在动了的步上算，不被刹停稀释
                    'moving_share_p50': median(flat['moving_share'])})          # 活动窗内动了的步占比（=1−站住比例）
    if 'brake_rate' in flat:   # §9.2 要"刹停 <2% 推理周期"。均值会被长尾拖着（实测 p50=0.5% 而 mean=10%），
        rr = flat['brake_rate']  # 所以必须分层报：p90 与"卡住题占比"才是能区分杠杆的读数。
        out.update({'brake_rate_mean': rr.mean().item(), 'brake_rate_p50': median(rr), 'brake_rate_p90': rr.quantile(0.9).item(),
                    'stuck_share': (rr > 0.2).float().mean().item(),
                    'n_plans_mean': flat['n_plans'].mean().item()})
    cats = [t.get('cat') for t in tasks]           # 分层读数：D 类按定义不可达，混在分母里会让"可达任务到达率"失真
    reach = torch.tensor([c != 'D' for c in cats])
    if reach.any():
        out['success_reachable'] = (ok[reach].float().mean().item())
    out['by_cat'] = {c: [int(sum(1 for x in cats if x == c)), int(sum(1 for i, x in enumerate(cats) if x == c and ok[i]))]
                     for c in sorted({c for c in cats if c})}
    if 'backoff' in flat and flat['backoff'].any():   # backoff=缩步顶上的次数；brakes 现在只记连最小档都不安全的
        out['backoff_mean'] = flat['backoff'].mean().item()      # ⇒ 原来的"全拒"事件数 = 两者之和
        out['reject_all_mean'] = (flat['backoff'] + flat['brakes']).mean().item()
    if 'tie_opp' in flat and flat['tie_opp'].any():   # tie=1 的自检：钩子可用率/真的改选率，分母与 brake_rate 同为规划次数
        out.update({'tie_opp_rate_mean': flat['tie_opp_rate'].mean().item(),
                    'tie_changed_rate_mean': flat['tie_changed_rate'].mean().item(),
                    'tie_opp_task_share': (flat['tie_opp'] > 0).float().mean().item()})
    return out, per
 def json_finite(value):
    if isinstance(value, dict): return {k: json_finite(v) for k, v in value.items()}
    if isinstance(value, list): return [json_finite(v) for v in value]
    if isinstance(value, float) and not math.isfinite(value): return None
    return value
 def main():
    ap = argparse.ArgumentParser()
    ap.add_argument('--ckpt', default='ml/runs/base_dag2_dag1/best.pt')
    ap.add_argument('--samples', default='ml/data/samples')
    ap.add_argument('--tasks', default=None)
    ap.add_argument('--split', default='val')
    ap.add_argument('--n', type=int, default=256)
    ap.add_argument('--K', type=int, default=8)
    ap.add_argument('--commit', type=int, default=3)
    ap.add_argument('--fb', type=float, default=1.8)
    ap.add_argument('--ki', type=float, default=0.0)
    ap.add_argument('--fb_scale', type=float, default=0.0)
    ap.add_argument('--fb_near', type=float, default=0.2)
    ap.add_argument('--fb_fallback', type=float, default=0.0)
    ap.add_argument('--kdecay', type=float, default=0.9)
    ap.add_argument('--kimax', type=float, default=0.5)
    ap.add_argument('--fb_every', type=int, default=1)
    ap.add_argument('--safety_try', type=int, default=0)
    ap.add_argument('--sel_w', type=float, default=0.1)
    ap.add_argument('--dls_safety', type=int, default=0)
    ap.add_argument('--fb_clear', type=float, default=0.0)
    ap.add_argument('--fb_cmin', type=float, default=0.3)
    ap.add_argument('--sub_refresh', type=int, default=0)
    ap.add_argument('--fb_block_gain', type=float, default=1.0)
    ap.add_argument('--dump', default=None)
    ap.add_argument('--out', default=None)
    ap.add_argument('--trace', default=None)
    ap.add_argument('--select', default='consist')
    ap.add_argument('--replan', default='r3')
    ap.add_argument('--safety', type=int, default=1, help='1=安全层+部分前缀；0=关')
    ap.add_argument('--partial', type=int, default=1)
    ap.add_argument('--inc_cap', type=int, default=0, help='1=反馈叠加后再按每轴 VMAX·DT 限幅')
    ap.add_argument('--gap_margin', type=float, default=0.0, help='安全层要求的间隙余量 mm（>0 时宽相位 cap 自动抬到 margin+0.5）')
    ap.add_argument('--cert_batch', type=int, default=0, help='1=一次 gaps 调用覆盖全部候选（压最坏情况，典型路径略贵）')
    ap.add_argument('--steps', type=int, default=1, help='MeanFlow 采样段数；1=线上一口径（历史发布数字全是 1）')
    ap.add_argument('--z_scale', type=float, default=1.0, help='候选噪声缩放：>1 让 K 条候选更分散（测"全拒是没有备选"还是"真的没路"）')
    ap.add_argument('--len_w', type=float, default=0.0, help="ep_len=罚累计归一行程；ep_waste=罚(1−净/总)×当前误差；0 与 endpoint 逐位相同")
    ap.add_argument('--tie', type=int, default=0, help='1=等长安全前缀时按沿路最小间隙定夺（需 --cert_batch 1）')
    ap.add_argument('--pre_h', type=int, default=0, help="select=pre 的打分步长：0=commit_n（被执行前缀），>0=按绝对步数（#30 曲线）")
    ap.add_argument('--pre_near', type=float, default=0.0, help="select=pre 的门控阈值(mm)：当前误差≤它时改用整条地平线打分（#31，0=关）")
    ap.add_argument('--backoff', type=int, default=0, help='全拒时把首选候选按 1/2、1/4…缩步重试的档数（需 --cert_batch 1）')
    ap.add_argument('--max_sec', type=float, default=None)
    ap.add_argument('--seed', type=int, default=0)
    ap.add_argument('--dls', default='analytic', choices=['analytic', 'fd'])
    ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu')
    a = ap.parse_args()
    print('config ' + json.dumps(vars(a), ensure_ascii=False), flush=True)
    if a.dls == 'fd':
        clmod.dls_steps = functools.partial(clmod.dls_steps, analytic=False)
    model, ck = load_policy(a.ckpt, a.device)
    kin = Kin(device=a.device)
    assert ck.get('fp') in (None, kin.fp)
    tasks = ([json.loads(l) for l in open(a.tasks, encoding='utf-8') if l.strip()] if a.tasks
             else [t for t in load_tasks([a.samples]) if t['split'] == a.split])
    tasks = tasks[:a.n]
    if not tasks: ap.error('no tasks selected')
    meta = {
        'config': vars(a), 'batch_size': 256, 'batch_seed_rule': 'same seed per batch',
        'checkpoint_sha256': hashlib.sha256(Path(a.ckpt).read_bytes()).hexdigest(),
        'tasks_sha256': hashlib.sha256(json.dumps(tasks, sort_keys=True).encode()).hexdigest(),
        'kin_fp': kin.fp, 'act_scale': float(model.act_scale),
    }
    t0 = time.perf_counter()
    r, pers = run(model, kin, tasks, a.device, K=a.K, safety=bool(a.safety), seed=a.seed, max_sec=a.max_sec, steps=a.steps, z_scale=a.z_scale,
            safety_partial=(a.commit if (a.safety and a.partial) else 0), commit_n=a.commit,
            select=a.select, replan=a.replan, fb_gain=a.fb, fb_scale=a.fb_scale, fb_near=a.fb_near, fb_fallback=a.fb_fallback, fb_ki=a.ki, fb_decay=a.kdecay, fb_imax=a.kimax,
            fb_every=a.fb_every, safety_try=a.safety_try, sel_w=a.sel_w, dls_safety=a.dls_safety, fb_clear=a.fb_clear, fb_cmin=a.fb_cmin, sub_refresh=a.sub_refresh, fb_block_gain=a.fb_block_gain,
            inc_cap=a.inc_cap, gap_margin=a.gap_margin, cert_batch=a.cert_batch, backoff=a.backoff, len_w=a.len_w, tie=a.tie, pre_h=a.pre_h, pre_near=a.pre_near, return_traj=bool(a.trace))
    wall = time.perf_counter() - t0
    result = {**meta, 'metrics': r, 'simulation_wall_s': wall}
    print(json.dumps(json_finite(result), ensure_ascii=False, allow_nan=False), flush=True)
    if a.out:
        Path(a.out).write_text(json.dumps(json_finite(result), indent=2, ensure_ascii=False, allow_nan=False) + '\n', encoding='utf-8')
    if a.dump:
        rows = []
        keys = ['done', 'ok', 't_done', 'limit', 'col', 'brakes', 'err', 'first_hit', 'dls_it', 'goal_ratio_2s', 'brake_rate', 'n_plans',
                'tie_opp', 'tie_changed', 'tie_opp_rate', 'tie_changed_rate']
        flat = {k: torch.cat([p[k] for _, p, _ in pers]).tolist() for k in keys}
        _, _, _, ex_t, tr_t = tasks_to_tensors(tasks, 'cpu')     # run() 里的同名量是局部变量，这里必须自取
        for i, t in enumerate(tasks):
            row = {**t, 'i': i, **{k: flat[k][i] for k in keys}}
            # 老师用时/行程是题目属性（与臂无关），落盘后任何一臂都能给历史臂补上"1.3×老师"的逐题配对
            row['t_exp'] = float(ex_t[i]) if torch.isfinite(ex_t[i]) else None
            row['tr_ref'] = float(tr_t[i]) if torch.isfinite(tr_t[i]) else None
            rows.append(row)
        Path(a.dump).write_text(json.dumps(json_finite(rows), allow_nan=False), encoding='utf-8')
        print('dump', a.dump, flush=True)
    if a.trace:
        torch.save({**meta, 'tasks': tasks, 'batches': pers}, a.trace)
        print('trace', a.trace, flush=True)
 if __name__ == '__main__':
    main()
@@ -26,17 +26,46 @@ def sample_rt(B, device, dtype, p_neq=0.25, mu=-0.4, sigma=1.0, p_edge=0.0):
    return r, t
-def meanflow_loss(model, mem, x, p_neq=0.25, ada_p=1.0, ada_c=1e-3, return_aux=False, p_edge=0.0):
+def avg_velocity_target(model, mem, x, e, r, t, target='mf', lam=0.5):
    """平均速度目标的三种写法（目标都 detach；u 带梯度）。target='mf' 与原实现逐位相同。
      mf  ：Eq.4 单段自举 v − (t−r)·du/dt，收敛解 = 平均速度（解析高斯场已验证偏差 <1e−12）
      lin ：v − (t−r)·[(1−λ)·du(z_t,c,t)/dt + λ·du(z_c,r,c)/dc]，系数和=1 ⇒ 收敛解仍是平均速度
      sq  ：同上的 (1−λ)²/λ² 版（K-MF 论文摘要里的写法）⇒ 系数和 0.5，实测只线性收敛（结构性少修一半）
      mid ：v − (t−r)·du(z_c,r,c)/dc，整条修正移到中点
    """
    fn = lambda z_, r_, t_: model.decode(mem, z_, r_, t_)
    z = (1 - t)[:, None, None] * x + t[:, None, None] * e
    v = e - x
    if target == 'mf':
        u, dudt = jvp(fn, (z, r, t), (v, torch.zeros_like(r), torch.ones_like(t)))
        return u, (v - (t - r)[:, None, None] * dudt).detach(), z
    with torch.no_grad():
        u = fn(z, r, t)                       # 这三种写法只要前向拿 u，导数全部进目标（sg）
    c = (1 - lam) * t + lam * r
    zc = (1 - c)[:, None, None] * x + c[:, None, None] * e
    tv = (v, torch.zeros_like(r), torch.ones_like(t))
    def jv(zz, rr, tt):
        with torch.enable_grad():
            return jvp(fn, (zz, rr, tt), tv)[1].detach()
    if target == 'mid':
        corr = jv(zc, r, c)
    else:
        d1 = jv(z, c, t)
        d2 = jv(zc, r, c)
        w = ((1 - lam) ** 2, lam ** 2) if target == 'sq' else ((1 - lam), lam)
        corr = w[0] * d1 + w[1] * d2
    return u, (v - (t - r)[:, None, None] * corr).detach(), z
 def meanflow_loss(model, mem, x, p_neq=0.25, ada_p=1.0, ada_c=1e-3, return_aux=False, p_edge=0.0, target='mf', lam=0.5):
    mem, x = mem.float(), x.float()
    with torch.autocast('cuda', enabled=False):
        B = x.shape[0]
        r, t = sample_rt(B, x.device, x.dtype, p_neq, p_edge=p_edge)
        e = torch.randn_like(x)
-        z = (1 - t)[:, None, None] * x + t[:, None, None] * e
+        u, u_tgt, z = avg_velocity_target(model, mem, x, e, r, t, target, lam)
        v = e - x
        fn = lambda z_, r_, t_: model.decode(mem, z_, r_, t_)
        u, dudt = jvp(fn, (z, r, t), (v, torch.zeros_like(r), torch.ones_like(t)))
        u_tgt = (v - (t - r)[:, None, None] * dudt).detach()
        err = (u - u_tgt).pow(2).mean(dim=(1, 2))
        w = 1.0 / (err.detach() + ada_c).pow(ada_p)
        loss = (w * err).mean()
@@ -144,6 +144,7 @@
  .ctlrow { display: flex; gap: 10px; margin-top: 14px; align-items: center; flex-wrap: wrap; }
  .ctlrow .hint { font-size: 12px; color: var(--dim2); }
  .term { background: #070a10; border: 1px solid var(--line); border-radius: 10px; padding: 10px 12px; height: 260px; overflow: auto; font: 12px/1.55 var(--mono); color: #c9d4e3; white-space: pre-wrap; word-break: break-all; }
  .explore { background: #0b0f16; border: 1px solid var(--line); border-radius: 10px; padding: 10px 12px; max-height: 560px; overflow: auto; font: 12px/1.7 var(--mono); color: #c9d4e3; white-space: pre-wrap; }
  .term .e { color: var(--bad); } .term .c { color: var(--accent); } .term .g { color: var(--ok); }
  .modal { position: fixed; inset: 0; background: #05080ed9; backdrop-filter: blur(7px); display: none; align-items: flex-start; justify-content: center; z-index: 40; padding: 5vh 16px 24px; overflow: auto; }
  .modal.show { display: flex; }
@@ -213,6 +214,7 @@
    <div class="card"><h2><span>部署形态评测</span><span class="r">策略 + 安全层</span></h2><div id="bench"></div></div>
    <div class="card"><h2><span>验收汇总</span><span class="r">方案 §9.2</span></h2><div id="accept"></div></div>
  </section>
  <section class="card"><h2><span>闭环探索日志</span><span class="r">docs/探索日志.md</span></h2><div class="explore" id="explore">加载中…</div></section>
  <section class="card"><h2><span>终端输出</span><span class="r" id="termnote">ml/runs/console.log</span></h2><div class="term" id="term"></div></section>
  <footer id="foot"></footer>
 </main>
@@ -834,6 +836,7 @@ $('logy').onclick = () => { logyOn = !logyOn; $('logy').classList.toggle('on', l
 for (const id of PREF_IDS) { $(id).onchange = prefSave; $(id).oninput = prefSave; }
 $('run').onchange = () => { userPicked = true; cur = null; poll(); };
 $('scenario').onchange = () => { api.scenario($('scenario').value); cur = null; userPicked = false; conOff = 0; $('run').innerHTML = ''; poll(); };
 api('/api/explore').then(d => { const el = $('explore'); el.textContent = (d && d.md) ? d.md : '（demo 模式：无探索日志）'; }).catch(() => { const el = $('explore'); el.textContent = '（无法读取 docs/探索日志.md）'; });
 $('auto').onclick = () => { const on = !$('auto').classList.contains('on'); $('auto').classList.toggle('on', on); clearInterval(timer); if (on) timer = setInterval(poll, 1000); };
 timer = setInterval(poll, 1000); poll();
@@ -608,6 +608,11 @@ class H(BaseHTTPRequestHandler):
            if u.path == '/api/status': return self.send(200, json.dumps(status()), 'application/json')
            if u.path == '/api/job': return self.send(200, json.dumps(JOB.state()), 'application/json')
            if u.path == '/api/console': return self.send(200, json.dumps(console(int(q.get('offset', ['0'])[0]))), 'application/json')
            if u.path == '/api/explore':   # 2026-10-20：闭环探索日志（docs/探索日志.md）回写控制台
                try:
                    with open(os.path.join(ROOT, 'docs', '探索日志.md'), encoding='utf-8') as f: md = f.read()
                except Exception: md = ''
                return self.send(200, json.dumps({'md': md}), 'application/json')
            self.send(404, 'not found', 'text/plain')
        except Exception as e:
            self.send(500, json.dumps({'error': str(e)}), 'application/json')
@@ -48,23 +48,42 @@ def goal_err(kin, q, T, R):
    return e, oe
-def dls_steps(kin, q, T, R, n=3, scale=0.3, lam=0.5):
+def execution_points(kin, q, increments):
-    """局部 DLS 精修：n 步增量 [B,n,6]"""
+    points, midpoints = [], []
    for delta in increments.unbind(1):
        nxt = kin.clamp_box(q + delta)
        midpoints.append(q + 0.5 * (nxt - q))
        points.append(nxt)
        q = nxt
    return torch.stack(points, 1), torch.stack(midpoints, 1)
 def dls_steps(kin, q, T, R, n=3, scale=0.3, lam=0.5, analytic=True):
    """局部 DLS 精修：n 步增量 [B,n,6]（analytic：几何雅可比，1 次 FK；否则 7 次批量有限差分）"""
    step = torch.tensor(STEP, device=q.device)
    B = q.shape[0]
    pose = (R.abs().sum(-1) > 0).float()
    Rt = R.view(-1, 3, 3)
-    def err(x):
+    def err(x, Tb, Rtb, poseb, xf=None):
-        xf = kin.cum_xf(x); p = kin.fk(x, xf); Rq = kin.orient(x, xf)
+        xf = xf if xf is not None else kin.cum_xf(x)
-        M = Rt @ Rq.transpose(1, 2)
+        p = kin.fk(x, xf); Rq = kin.orient(x, xf)
-        rv = 0.5 * torch.stack([M[:, 2, 1] - M[:, 1, 2], M[:, 0, 2] - M[:, 2, 0], M[:, 1, 0] - M[:, 0, 1]], -1) * 100 * pose[:, None]
+        M = Rtb @ Rq.transpose(1, 2)
-        return torch.cat([T - p, rv], -1)
+        rv = 0.5 * torch.stack([M[:, 2, 1] - M[:, 1, 2], M[:, 0, 2] - M[:, 2, 0], M[:, 1, 0] - M[:, 0, 1]], -1) * 100 * poseb[:, None]
        return torch.cat([Tb - p, rv], -1)
    out = []; x = q.clone()
    ar = torch.arange(6, device=q.device)
    for _ in range(n):
-        e0 = err(x); J = []
+        xf = kin.cum_xf(x)
-        for j in range(6):
+        e0 = err(x, T, Rt, pose, xf)
-            dx = torch.zeros_like(x); dx[:, j] = 1e-3
+        if analytic:
-            J.append(-(err(x + dx) - e0) / 1e-3)
+            # 几何角速度 Jw 仅在姿态误差接近零时等于该 skew 残差的负导数。
-        J = torch.stack(J, -1)                                    # [B,6(err),6(q)]
+            Jv, Jw = kin.jac(x, xf)
            J = torch.cat([Jv, 100 * Jw * pose[:, None, None]], 1)
        else:
            dx = torch.zeros(B, 6, 6, device=q.device); dx[:, ar, ar] = 1e-3
            xp = (x[:, None, :] + dx).reshape(B * 6, 6)
            ep = err(xp, T.repeat_interleave(6, 0), Rt.repeat_interleave(6, 0), pose.repeat_interleave(6, 0))
            J = -((ep - e0.repeat_interleave(6, 0)) / 1e-3).view(B, 6, 6).transpose(1, 2)   # [B,6(err),6(q)]
        A = J @ J.transpose(1, 2) + lam ** 2 * torch.eye(6, device=q.device)
        y = torch.linalg.solve(A, e0.unsqueeze(-1))
        dq = (J.transpose(1, 2) @ y).squeeze(-1)
@@ -141,17 +160,31 @@ class TeacherOracle:
@torch.no_grad()
 def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None, return_traj=False, use_sub=True, sub_override=None,
-            qp0=None, steps=1, z_scale=1.0, select='consist', oracle=None, replan='r3', sub_rule='cross'):
+            qp0=None, steps=1, z_scale=1.0, select='consist', oracle=None, replan='r3', sub_rule='cross',
            fb_gain=0.0, fb_scale=0.0, fb_near=0.2, fb_fallback=0.0, safety_partial=0, commit_n=3, collect=None,
            fb_exec=0.0, exec_safety=0, fb_ki=0.0, fb_decay=0.9, fb_imax=0.5, fb_every=1, safety_try=0, sel_w=0.1, dls_safety=0, fb_clear=0.0, fb_cmin=0.3, sub_refresh=0, fb_block_gain=1.0,
            inc_cap=0, gap_margin=0.0, cert_batch=0, backoff=0, len_w=0.0, tie=0, pre_h=0, pre_near=0, progress=None):
    """use_sub=False：不用子目标（sf=0）；sub_override=(subs [B,M,6] 绝对关节角, n [B] 个数 或 need [B] bool)：冷启动时用给定途经点代替子目标头；
    qp0 [B,6]：起点的上一帧（默认 = q0，即静止起步）。steps：MeanFlow 采样段数；z_scale：噪声缩放；
    select：consist（部署口径：冷启动价值前瞻、之后与上一条已提交计划的一致性）/ value（每次都价值前瞻）/ first（不选，取第 0 条）。
    select='pre'：把打分地平线换成「这一轮真会执行到的位置」（fut 的第 commit_n 步）；'endpoint' 用的是整条地平线末端。
    oracle：TeacherOracle，代替 model 出动作（model 可为 None，须配 select='first'、use_sub=False）。
    replan：'r3'（部署：队列 ≤ 3 步就规划，稳态 r=3）/ 'empty'（队列空了才规划，每次 r=0，前缀消融用）。
-    sub_rule：子目标推进规则，见 sub_advance。除默认值外均为诊断用。"""
+    sub_rule：子目标推进规则，见 sub_advance。除默认值外均为诊断用。
    inc_cap=1：把「策略动作 + 目标反馈」的合计增量再按每轴 VMAX·DT 限一次幅（默认关；关时反馈是在 ±1 限幅之后叠加的，第30轮实测最大 1.66×VMAX）。
    gap_margin>0：安全层要求间隙大于该毫米余量而不是 >0；宽相位 cap 自动抬到 margin+0.5，否则 0.5 的截顶值会让余量门限恒假。
    backoff>0：全拒（连第 1 步都不安全）时不再站住，把首选候选按 1/2、1/4…（共 backoff 档）缩步一起送进同一次认证，
               取能通过的第 1 档执行；缩步候选不参与"前缀更长者优先"的比较，只在原候选全被拒时顶上。需 cert_batch=1。
    tie=1：等长安全前缀之间改用「沿路最小间隙更大」定夺（默认 0=沿用选择器分数顺序）。需 cert_batch=1。
    select='ep_len' + len_w>0：在末态误差打分上再减 len_w·Σ|Δ|/STEP（每轴归一的候选累计行程）。§9.2 要行程≤规划器×1.10，
               而实测行程比 2.4~2.6 没有任何机制在管；len_w=0 与 select='endpoint' 逐位相同（有回归断言）。
    两者默认值下行为与第32轮逐位相同。"""
    if (backoff or tie) and not cert_batch: raise ValueError('backoff/tie 目前只实现在 cert_batch=1 口径')
    g = torch.Generator(device=device); g.manual_seed(seed)
    q0, T, R, ex, tr = tasks_to_tensors(tasks, device)
    B = q0.shape[0]
    step = torch.tensor(STEP, device=device)
    gcap = max(0.5, gap_margin + 0.5)               # 余量门限必须落在截顶值之内才有意义
    act_scale = oracle.act_scale if oracle is not None else float(getattr(model, 'act_scale', 1.0))
    astep = step * act_scale                         # 动作反归一化：每单位 act 对应的度数
    has_ref = torch.isfinite(ex)
@@ -160,12 +193,24 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
    q, qp = q0.clone(), (qp0.to(device) if qp0 is not None else q0.clone())
    queue = torch.zeros(B, 0, 6, device=device)
    last = None; last_ok = torch.zeros(B, dtype=torch.bool, device=device)
    fb_i = torch.zeros(B, 6, device=device)   # 积分项：累积持续同向的目标反馈（慢变偏差）
    fb_cache = None; pit = 0                  # fb_every>1：复用上一次规划的目标反馈（误差方向慢变）
    last_brake = torch.zeros(B, dtype=torch.bool, device=device)
    subs = torch.zeros(B, 2, 6, device=device); nsub = torch.zeros(B, dtype=torch.long, device=device)
    sub_t = torch.zeros(B, device=device); sub_prev = q0.clone()
    done = torch.zeros(B, dtype=torch.bool, device=device); t_done = torch.full((B,), float('nan'), device=device)
    col = torch.zeros(B, device=device); travel = torch.zeros(B, device=device); brakes = torch.zeros(B, device=device)
    bo = torch.zeros(B, device=device)   # 缩步顶上的次数（backoff>0 才会计）
    to = torch.zeros(B, device=device)   # tie=1 才计：出现「等长安全前缀并列」的规划次数（钩子可用）
    tx = torch.zeros(B, device=device)   # tie=1 才计：并列里真的由余量改选了那条的次数（钩子有效）
    first_hit = torch.full((B,), float('nan'), device=device); dls_it = torch.full((B,), -1, dtype=torch.long, device=device)
    # 2026-10-20（轮次33）：速度/加速度按「实际执行位移」算，不看指令——第30轮证明反馈是在限幅之后叠加的
    vratio = torch.zeros(B, device=device); aratio = torch.zeros(B, device=device); prev_exec = torch.zeros(B, 6, device=device)
    rev = torch.zeros(B, device=device); nact = torch.zeros(B, device=device); prev_sgn = torch.zeros(B, 6, device=device)
    nmov = torch.zeros(B, device=device)
    nplan = torch.zeros(B, device=device)   # 每题规划次数 = 推理周期数（§9.2 的"刹停 <2% 推理周期"要这个分母）
    traj = [q.clone()] if return_traj else None
    plans = {k: [] for k in ('it', 'r', 'qe', 'commit', 'points', 'midpoints', 'gaps', 'certified')} if return_traj else None
    n_start = int(round(0.5 / DT)); moved_start = None          # R2 起步失败：0.5 s 内关节总行程 < 1°
    k2s = int(round(2.0 / DT)); e2 = None
    e0, _ = goal_err(kin, q0, T, R)
@@ -176,9 +221,14 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
    for it in range(nmax):
        tnow = it * DT
        if (queue.shape[1] <= 3) if replan == 'r3' else (queue.shape[1] == 0):
            nplan += 1.0        # 验收判据要"刹停 / 推理周期"的比值，分母就是这个（批内所有行同步规划）
            r = queue.shape[1]
-            pre = torch.zeros(B, 3, 6, device=device); pre[:, :r] = queue[:, :r]
+            pre = torch.zeros(B, 3, 6, device=device)
-            qe = q + queue.sum(1)
+            qe = q
            if r:
                prefix_points, _ = execution_points(kin, q, queue)
                pre[:, :r] = prefix_points - torch.cat([q[:, None], prefix_points[:, :-1]], 1)
                qe = prefix_points[:, -1]
            e, oe = goal_err(kin, qe, T, R)
            near = (e < 5) & ((R.abs().sum(-1) == 0) | (oe < 5))
            subs, nsub, sub_prev, sub_t = sub_advance(kin, subs, nsub, sub_prev, sub_t, qe, tnow, sub_rule)
@@ -189,7 +239,43 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
            else:
                z = torch.randn(B * K, H, 6, device=device, generator=g) * z_scale
                act, val, sub = model.sample_k(feat, K, z, steps=steps)
            if sub_refresh and not cold:   # 子目标头作为反应式规划：没有待走途经点时，用当前状态的预测补一个（训练标签在每个状态都有 sub）
                need = sub[:, 12] > 0.5
                newsubs = q[:, None] + sub[:, :12].float().view(B, 2, 6) * 180
                rf = need & (nsub <= 0)
                subs = torch.where(rf[:, None, None], newsubs, subs)
                nsub = torch.where(rf, torch.full_like(nsub, 2), nsub)
                sub_prev = torch.where(rf[:, None], q, sub_prev)
                sub_t = torch.where(rf, torch.full_like(sub_t, tnow), sub_t)
            inc = act.float().clamp(-1, 1) * astep
            if fb_gain > 0:   # 实验（2026-10-20）：目标反馈加在所有候选上（安全层也评估带反馈的候选）
                if fb_cache is None or fb_every <= 1 or pit % fb_every == 0:
                    fb = dls_steps(kin, qe, T, R, n=1); fb_cache = fb
                else:
                    fb = fb_cache
                pit += 1
                gm_ = None
                if fb_clear > 0:   # 近障碍衰减：反馈方向可能把候选推进障碍，贴近时让模型自己的绕行占主导
                    dgc = kin.gaps(qe, fb_clear)['gm'].min(-1).values
                    gm_ = torch.clamp(dgc / fb_clear, fb_cmin, 1.0)
                if fb_ki > 0:
                    fb_i = fb_decay * fb_i + fb[:, 0] * (~done)[:, None]
                    lim = step * fb_imax
                    fb_i = fb_i.clamp(-lim, lim)
                    inc = inc + fb_gain * fb[:, :, None, :] + fb_ki * fb_i[:, None, None, :]
                elif fb_scale > 0:   # 距离衰减：远离目标全增益，贴近目标减弱（减少过冲/刹停）
                    dg, _ = goal_err(kin, qe, T, R)
                    gdec = fb_gain * torch.clamp(dg / fb_scale, fb_near, 1.0)
                    if gm_ is not None: gdec = gdec * gm_
                    inc = inc + gdec[:, None, None, None] * fb[:, :, None, :]
                else:
                    gv = torch.full((B,), fb_gain, device=device, dtype=fb.dtype)
                    if gm_ is not None: gv = gv * gm_
                    if fb_block_gain != 1.0:   # 上一次被安全层全拒：减弱目标反馈，让模型自己的绕行候选有机会通过认证
                        gv = torch.where(last_brake, gv * fb_block_gain, gv)
                    inc = inc + gv[:, None, None, None] * fb[:, :, None, :]
            if inc_cap:
                inc = torch.maximum(torch.minimum(inc, step), -step)   # 反馈之后才限幅：限的是真正要执行的每轴增量
            if cold:
                need = sub[:, 12] > 0.5
                subs = q[:, None] + sub[:, :12].float().view(B, 2, 6) * 180
@@ -206,6 +292,35 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
            fut = qe[:, None, None] + torch.cumsum(inc[:, :, r:], 2)          # [B,K,H-r,6]
            if select == 'first':
                score = torch.zeros(B, K, device=device); score[:, 0] = 1
            elif select == 'pre':   # #29：按「这一轮真会执行到的位置」打分，而不是 16 步开环外推的末端。
                # 诊断来自 ep_len/ep_waste 的失败：打分地平线远长于提交步数，被安全层截断的候选那个终点根本到不了。
                qa_pre = fut[:, :, min(pre_h if pre_h > 0 else commit_n, fut.shape[2]) - 1].reshape(B * K, 6)
                e_pre, _ = goal_err(kin, qa_pre, T.repeat_interleave(K, 0), R.repeat_interleave(K, 0))
                score = -e_pre.view(B, K)
                if pre_near > 0:   # #31：近终段换回整条地平线（终端代价）。轮次 48 的曲线说：时限收益来自短前瞻、
                    # 精度代价却集中在 h≤4 ⇒ 两半可以分开要；代价是多一次 goal_err(B×K 点)，只在门控口径下发生。
                    qa_full = fut[:, :, -1].reshape(B * K, 6)
                    e_full, _ = goal_err(kin, qa_full, T.repeat_interleave(K, 0), R.repeat_interleave(K, 0))
                    score = torch.where((e <= pre_near)[:, None], -e_full.view(B, K), score)
            elif select in ('endpoint', 'adapt', 'ep_len', 'ep_waste'):
                qa = fut[:, :, -1].reshape(B * K, 6)
                e_end, _ = goal_err(kin, qa, T.repeat_interleave(K, 0), R.repeat_interleave(K, 0))
                score = -e_end.view(B, K)
                if select == 'ep_len' and len_w > 0:   # 行程惩罚：每轴按 STEP 归一的累计路程。§9.2 要“行程≤规划器×1.10”，
                    ln = ((fut[:, :, 1:] - fut[:, :, :-1]).abs() / step).sum(-1).sum(-1)   # 而现在 2.4 倍没人管
                    score = score - len_w * ln
                if select == 'ep_waste' and len_w > 0:   # 轮次 39 的分解证明行程超出主要是同一轴来回抖（换向步占 23.5%、
                    dv = fut[:, :, 1:] - fut[:, :, :-1]   # 净位移只占总行程 44.5%），绕远只占一小部分 ⇒ 罚"白费的比例"：
                    tot = dv.abs().sum(-2).sum(-1)        # 同轴来回会在净位移里相互抵消，所以 waste = 1 − 净/总 ∈ [0,1]。
                    net = dv.sum(-2).abs().sum(-1)       # dv 是 [B,K,H-1,6]：时间在第 -2 轴（写错成 -1 就变成"对轴求和"，等于总行程）
                    frac = (1.0 - net / tot.clamp_min(1e-6)).clamp(0, 1)
                    score = score - len_w * frac * e.view(B, 1)   # 罚度按"当前离目标多远"缩放：近端不会把进度项压死
                if select == 'adapt' and last is not None:
                    n = min(fut.shape[2], last.shape[1] - (commit_n + 1))
                    w = 0.85 ** torch.arange(n, device=device, dtype=torch.float32)
                    d2 = (((fut[:, :, :n] - last[:, None, commit_n + 1:commit_n + 1 + n]) / step) ** 2).sum(-1)
                    sc = torch.where(last_ok[:, None], -(d2 * w).sum(-1), torch.zeros_like(d2[..., 0]))
                    score = torch.where(last_brake[:, None], sc, score)
            elif cold or select == 'value':
                # 短前瞻：候选末态的价值
                qa = fut[:, :, -1].reshape(B * K, 6); qb = fut[:, :, -2].reshape(B * K, 6)
@@ -216,37 +331,158 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
            else:
                # 2026-10-19 修错位：last[i] = 上一计划从 qe_old 走 i 步后的位置；提交 3 步后 qe_new = last[3]，
                # 新候选 fut[0] 是 qe_new 再走 1 步 = 对应 last[4]。JS 部署（ik-policy.js）同一处同样修正。
-                n = min(fut.shape[2], last.shape[1] - 4)
+                n = min(fut.shape[2], last.shape[1] - (commit_n + 1))
                w = 0.85 ** torch.arange(n, device=device, dtype=torch.float32)
-                d2 = (((fut[:, :, :n] - last[:, None, 4:4 + n]) / step) ** 2).sum(-1)
+                d2 = (((fut[:, :, :n] - last[:, None, commit_n + 1:commit_n + 1 + n]) / step) ** 2).sum(-1)
                # 2026-10-19（审查 review_closedloop F5）：上一次是刹停或 DLS 接管的行没有「已提交计划」，
                # 与 JS（lastPlan = null → 一致性项缺席、各候选同分 → 取第 0 条）一致：同分，稳定排序取第 0 条
                score = torch.where(last_ok[:, None], -(d2 * w).sum(-1), torch.zeros_like(d2[..., 0]))
                if select == 'mix':   # 一致性 + 端点进度（w=sel_w/mm）
                    qa = fut[:, :, -1].reshape(B * K, 6)
                    e_end, _ = goal_err(kin, qa, T.repeat_interleave(K, 0), R.repeat_interleave(K, 0))
                    score = score - sel_w * e_end.view(B, K)
            order = torch.sort(score, dim=-1, descending=True, stable=True).indices
            pick = order[:, 0]
-            commit = inc[ar, pick, r:r + 3]
+            if select == 'average':   # 2026-10-20：候选平均（平滑误差）——所有候选置为 K 条动作的均值
                inc = inc.mean(1, keepdim=True).expand_as(inc).contiguous()
                K_eff = 1
            else:
                K_eff = K
            commit = inc[ar, pick, r:r + commit_n]
            if collect is not None:   # 反馈蒸馏：收集与部署完全一致的 (特征, 复合动作=模型输出+目标反馈)
                collect.append((feat.detach().float().cpu(), inc[ar, pick].detach().float().cpu()))
            committed = torch.ones(B, dtype=torch.bool, device=device)
-            if safety:
+            braked = torch.zeros(B, dtype=torch.bool, device=device)
            if return_traj:
                cert_pts = torch.full((B, commit_n, 6), float('nan'), device=device)
                cert_mids = cert_pts.clone()
                cert_gaps = torch.full((B, 2 * commit_n), float('nan'), device=device)
                certified = torch.zeros(B, commit_n, dtype=torch.bool, device=device)
            if safety and safety_partial:   # 2026-10-20：允许「部分安全前缀」——执行到第一个不安全步为止，而不是全拒刹停
                maxlen = torch.zeros(B, dtype=torch.long, device=device)
                chosen = torch.zeros(B, commit_n, 6, device=device); chosen_k = pick.clone()
                ntry = min(K_eff, safety_try) if safety_try > 0 else K_eff
                if cert_batch:   # 一次 gaps 覆盖全部候选：典型路径略贵，但把「前面候选被拒→串行调用 K 次」的最坏情况封成 1 次
                    cks = inc[ar[:, None], order[:, :ntry], r:r + commit_n]        # [B,ntry,cn,6]
                    if backoff:   # 缩步档与正常候选并进同一次认证，不额外调用几何
                        sc = (0.5 ** torch.arange(1, backoff + 1, device=device, dtype=inc.dtype))[None, :, None, None]
                        cands = torch.cat([cks, inc[ar, order[:, 0], r:r + commit_n][:, None] * sc], 1)
                    else:
                        cands = cks
                    nc = cands.shape[1]
                    pts, mids = execution_points(kin, qe.repeat_interleave(nc, 0), cands.reshape(B * nc, commit_n, 6))
                    gaps6 = kin.gaps(torch.cat([pts, mids], 1).reshape(-1, 6), gcap)['gm'].min(-1).values.view(B, nc, 2 * commit_n)
                    stepok = (gaps6 > gap_margin)[:, :, :commit_n] & (gaps6 > gap_margin)[:, :, commit_n:]
                    pl_all = torch.cumprod(stepok.long(), -1).sum(-1)       # [B,nc] 每条候选的安全前缀长度
                    plen = pl_all[:, :ntry]
                    maxlen = plen.max(-1).values
                    best = plen.argmax(-1)                # 与串行同口径：严格更大者胜 ⇒ 首个最大值
                    if tie:   # 等长前缀时不再只看选择器分数：取"沿路最小间隙更大"的那条（零额外几何调用，判据仍是 gap>0）
                        ci = torch.arange(2 * commit_n, device=device)
                        keep = (ci < maxlen[:, None]) | ((ci >= commit_n) & (ci < commit_n + maxlen[:, None]))   # [B,2cn] 只看会被执行的那些点
                        clr = gaps6[:, :ntry].where(keep[:, None, :], torch.full((), float('inf'), device=device)).amin(-1)
                        cand = torch.where(plen == maxlen[:, None], clr, torch.full_like(clr, -float('inf'))).argmax(-1)
                        ntile = (plen == maxlen[:, None]).sum(-1)   # 并列最长的候选条数（勿用 nmax：那是主循环的总步数）
                        to += (ntile > 1) & (maxlen > 0)
                        tx += (cand != best)
                        best = cand
                    if backoff:   # 缩步候选只在全拒时顶上：不参与「前缀更长者优先」的比较，所以正常路径逐位不变
                        full0 = maxlen == 0
                        bb = pl_all[:, ntry:].argmax(-1)   # 平手取缩得最少的（档 0 = 1/2）
                        mv = pl_all[ar, ntry + bb]
                        bo += (full0 & (mv > 0))
                        best = torch.where(full0, ntry + bb, best)
                        maxlen = torch.where(full0, mv, maxlen)
                        chosen_k = torch.where(full0, order[:, 0], order[ar, best.clamp(max=ntry - 1)])
                    else:
                        chosen_k = order[ar, best]
                    chosen = cands[ar, best]
                    if return_traj:
                        cert_pts = pts.view(B, nc, commit_n, 6)[ar, best]
                        cert_mids = mids.view(B, nc, commit_n, 6)[ar, best]
                        cert_gaps = gaps6[ar, best]
                else:
                    for kk in range(ntry):
                        ck = inc[ar, order[:, kk], r:r + commit_n]
                        pts, mids = execution_points(kin, qe, ck)
                        allq = torch.cat([pts, mids], 1).reshape(-1, 6)
                        gaps6 = kin.gaps(allq, gcap)['gm'].min(-1).values.view(B, 2 * commit_n)
                        ok6 = gaps6 > gap_margin
                        okstep = ok6[:, :commit_n] & ok6[:, commit_n:]
                        plen = torch.cumprod(okstep.long(), 1).sum(1)
                        take = plen > maxlen
                        chosen = torch.where(take[:, None, None], ck, chosen); chosen_k = torch.where(take, order[:, kk], chosen_k)
                        if return_traj:
                            cert_pts = torch.where(take[:, None, None], pts, cert_pts)
                            cert_mids = torch.where(take[:, None, None], mids, cert_mids)
                            cert_gaps = torch.where(take[:, None], gaps6, cert_gaps)
                        maxlen = torch.maximum(maxlen, plen)
                        if B == 1 and int(maxlen[0]) >= commit_n: break   # 实时单题：首个全安全候选即停（安全层 16 点→2-4 点）
                msk = (torch.arange(commit_n, device=device)[None] < maxlen[:, None])
                commit = torch.where(msk[..., None], chosen, torch.zeros_like(chosen))
                if return_traj: certified = msk.clone()
                brakes += (maxlen == 0).float(); braked = maxlen == 0
                pick = chosen_k; committed = maxlen > 0
            elif safety:
                ok_any = torch.zeros(B, dtype=torch.bool, device=device)
-                chosen = torch.zeros(B, 3, 6, device=device); chosen_k = pick.clone()
+                chosen = torch.zeros(B, commit_n, 6, device=device); chosen_k = pick.clone()
-                for kk in range(K):
+                ntry = min(K_eff, safety_try) if safety_try > 0 else K_eff
-                    ck = inc[ar, order[:, kk], r:r + 3]
+                for kk in range(ntry):
-                    pts = qe[:, None] + torch.cumsum(ck, 1)
+                    ck = inc[ar, order[:, kk], r:r + commit_n]
-                    mids = torch.cat([qe[:, None], pts[:, :-1]], 1) + 0.5 * ck
+                    pts, mids = execution_points(kin, qe, ck)
                    allq = torch.cat([pts, mids], 1).reshape(-1, 6)
-                    okk = (kin.gaps(allq, 0.5)['gm'].min(-1).values > 0).view(B, 6).all(-1)
+                    gaps6 = kin.gaps(allq, gcap)['gm'].min(-1).values.view(B, 2 * commit_n)
                    okk = (gaps6 > gap_margin).all(-1)
                    take = okk & ~ok_any
                    chosen = torch.where(take[:, None, None], ck, chosen); chosen_k = torch.where(take, order[:, kk], chosen_k); ok_any |= okk
                    if return_traj:
                        cert_pts = torch.where(take[:, None, None], pts, cert_pts)
                        cert_mids = torch.where(take[:, None, None], mids, cert_mids)
                        cert_gaps = torch.where(take[:, None], gaps6, cert_gaps)
                commit = torch.where(ok_any[:, None, None], chosen, torch.zeros_like(chosen))
-                brakes += (~ok_any).float()
+                if return_traj: certified = ok_any[:, None].expand(-1, commit_n).clone()
                brakes += (~ok_any).float(); braked = ~ok_any
                pick = chosen_k; committed = ok_any
            if safety and fb_gain > 0 and fb_fallback > 0:
                fbcom = torch.zeros(B, commit_n, 6, device=device)
                fbcom[:, :1] = dls_steps(kin, qe, T, R, n=1) * fb_fallback
                pts, mids = execution_points(kin, qe, fbcom)
                allq = torch.cat([pts, mids], 1).reshape(-1, 6)
                gaps6 = kin.gaps(allq, gcap)['gm'].min(-1).values.view(B, 2 * commit_n)
                takef = braked & (gaps6 > gap_margin).all(-1)
                commit = torch.where(takef[:, None, None], fbcom, commit)
                if return_traj:
                    cert_pts = torch.where(takef[:, None, None], pts, cert_pts)
                    cert_mids = torch.where(takef[:, None, None], mids, cert_mids)
                    cert_gaps = torch.where(takef[:, None], gaps6, cert_gaps)
                    certified = certified | takef[:, None]
            if near.any():
-                ref = dls_steps(kin, qe, T, R)
+                ref = dls_steps(kin, qe, T, R, n=commit_n)
-                commit = torch.where(near[:, None, None], ref, commit)
+                take = near
-                dls_it = torch.where(near & (dls_it < 0), torch.full_like(dls_it, it), dls_it)
+                if dls_safety and safety:   # 2026-10-20：近目标 DLS 接管也要过安全层（部署端 _certCommit 本来就会验；不过则退回候选计划）
-                committed = committed & ~near
+                    pts, mids = execution_points(kin, qe, ref)
                    allq = torch.cat([pts, mids], 1).reshape(-1, 6)
                    gaps6 = kin.gaps(allq, gcap)['gm'].min(-1).values.view(B, 2 * commit_n)
                    okd = (gaps6 > gap_margin).all(-1)
                    take = near & okd
                    if return_traj:
                        cert_pts = torch.where(take[:, None, None], pts, cert_pts)
                        cert_mids = torch.where(take[:, None, None], mids, cert_mids)
                        cert_gaps = torch.where(take[:, None], gaps6, cert_gaps)
                if return_traj:
                    certified = torch.where(take[:, None], torch.full_like(certified, bool(dls_safety and safety)), certified)
                commit = torch.where(take[:, None, None], ref, commit)
                dls_it = torch.where(take & (dls_it < 0), torch.full_like(dls_it, it), dls_it)
                committed = committed & ~take
            # 2026-10-19（F5）：last 记真正提交的那条候选（安全层换了候选时是换后的那条）；刹停 / DLS 接管的行无效
-            last = torch.cat([qe[:, None], fut[ar, pick]], 1); last_ok = committed
+            if fb_ki > 0:   # 刹停/精修接管时清积分（防止原地累积到限幅后突然冲出去）
                fb_i = torch.where(committed[:, None], fb_i, torch.zeros_like(fb_i))
            last = torch.cat([qe[:, None], fut[ar, pick]], 1); last_ok = committed; last_brake = braked
            if return_traj:
                plans['it'].append(it + r)
                plans['r'].append(r)
                for key, value in (('qe', qe), ('commit', commit), ('points', cert_pts), ('midpoints', cert_mids), ('gaps', cert_gaps), ('certified', certified)):
                    plans[key].append(value)
            queue = torch.cat([queue, commit], 1)
            cold = False
        d = queue[:, 0]; queue = queue[:, 1:]
@@ -264,10 +500,22 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
        hit = ((gm[:B] <= 0) | (gm[B:] <= 0)) & ~done & (tn <= limit + 1e-6)
        col += hit.float()
        first_hit = torch.where(hit & torch.isnan(first_hit), torch.full_like(first_hit, tn), first_hit)
        act_ = ((~done) & (tn <= limit + 1e-6)).float()          # 只计自己时限内的移动（与 col 同口径）
        exec_d = (q - qp).abs()
        vratio = torch.maximum(vratio, (exec_d / step).max(-1).values * act_)       # 实际速度 / VMAX
        aratio = torch.maximum(aratio, ((exec_d - prev_exec) / step).max(-1).values * act_)   # 每控制步的速度变化 / VMAX
        prev_exec = exec_d
        sgn = torch.sign(q - qp)      # 换向率：轮次 39 的分解证明"行程超出 2.4 倍"里同轴来回占大头
        rev += ((sgn != 0) & (prev_sgn != 0) & (sgn == -prev_sgn)).float().sum(-1) * act_
        prev_sgn = torch.where(sgn != 0, sgn, prev_sgn)      # 零步不重置方向（站住再走同向不算换向）
        nact += act_
        nmov += act_ * (exec_d.sum(-1) > 1e-6).float()       # 只在"动了"的步上算换向：站住会稀释分母（刹停越多的臂会显得越不抖）
        e, oe = goal_err(kin, q, T, R)
        if it + 1 == k2s: e2 = e.clone()
        newly = (~done) & (e < 1) & (oe < 0.5)
        t_done = torch.where(newly, torch.full_like(t_done, tn), t_done); done |= newly
        if progress is not None and (it == 0 or (it + 1) % 50 == 0 or it + 1 == nmax):
            progress(it + 1, nmax)
        if done.all(): break
    ok = done & (t_done <= limit + 1e-6)
    if moved_start is None: moved_start = travel.clone()
@@ -281,15 +529,27 @@ def rollout(model, kin, tasks, device, K=8, safety=False, seed=0, max_sec=None,
           'collision_rate': (col > 0).float().mean().item(), 'collision_steps_mean': col.mean().item(),
           'travel_ratio_median': (travel[okr] / tr[okr].clamp(min=1)).median().item() if okr.any() else None,
           'time_mean_s': t_done[ok].mean().item() if ok.any() else None, 'brakes_mean': brakes.mean().item(),
           'backoff_mean': bo.mean().item(),
           'start_fail': start_fail.float().mean().item(), 'goal_ratio_2s': ratio2.median().item(),
           'speed_ratio_mean': vratio.mean().item(), 'over_vmax_rate': (vratio > 1.001).float().mean().item(),
           'time_budget': 'teacher×2(≥3 s)' if bool(has_ref.all()) else ('fixed %.0f s' % (max_sec or NOREF_SEC) if not has_ref.any() else 'mixed')}
    # 绕行侧：J1 累计位移的正向/负向极值哪个更大（+1 / −1），供 R7 左右比例门禁
    # 首次方向；全程没偏离到 FIRST_DEG 的（基本不绕 J1）退回净方向
    side = torch.where(j1_first != 0, j1_first, torch.where(j1_max >= -j1_min, torch.ones_like(j1_max), -torch.ones_like(j1_max)))
    per = {'done': done.cpu(), 'ok': ok.cpu(), 't_done': t_done.cpu(), 'limit': limit.cpu(), 'col': col.cpu(), 'travel': travel.cpu(), 'err': e_end.cpu(),
-           'side': side.cpu(), 'start_fail': start_fail.cpu(), 'brakes': brakes.cpu(), 'first_hit': first_hit.cpu(), 'dls_it': dls_it.cpu(),
+           'side': side.cpu(), 'start_fail': start_fail.cpu(), 'brakes': brakes.cpu(), 'backoff': bo.cpu(), 'first_hit': first_hit.cpu(), 'dls_it': dls_it.cpu(),
-           'goal_ratio_2s': ratio2.cpu()}
+           'n_plans': nplan.cpu(), 'brake_rate': (brakes / nplan.clamp_min(1.0)).cpu(),
-    if return_traj: per['traj'] = torch.stack(traj, 1).cpu()
+           'tie_opp': to.cpu(), 'tie_changed': tx.cpu(),           # tie=1 才有意义：并列次数/改选次数（每题）
           'tie_opp_rate': (to / nplan.clamp_min(1.0)).cpu(),       # 分母与 brake_rate 同口径 = 规划次数
           'tie_changed_rate': (tx / nplan.clamp_min(1.0)).cpu(),
           'goal_ratio_2s': ratio2.cpu(), 'speed_ratio': vratio.cpu(), 'accel_ratio': aratio.cpu(),
           # 口径与离线分解一致：换向的 (步,轴) 对数 / 活动的 (步,轴) 对数 ∈ [0,1]（除以 6 个轴）
           'reverse_rate': (rev / (6.0 * nact.clamp_min(1.0))).cpu(),
           'reverse_rate_moving': (rev / (6.0 * nmov.clamp_min(1.0))).cpu(),
           'moving_share': (nmov / nact.clamp_min(1.0)).cpu()}
    if return_traj:
        per['traj'] = torch.stack(traj, 1).cpu()
        per['plans'] = {k: torch.tensor(v) if k in ('it', 'r') else torch.stack(v, 1).cpu() for k, v in plans.items()}
    return res, per
@@ -73,6 +73,7 @@ def main():
    ap.add_argument('--config', default='ml/configs/base.json'); ap.add_argument('--steps', type=int, default=None)
    ap.add_argument('--resume', default=None); ap.add_argument('--no-eval', action='store_true')
    ap.add_argument('--init', default=None, help='只载入权重（优先 EMA）、优化器与步数从 0 开始（DAgger 微调用）')
    ap.add_argument('--freeze-enc', action='store_true', help='冻结编码器（enc + enc_norm），只微调解码器/头')
    ap.add_argument('--lr', type=float, default=None); ap.add_argument('--warmup', type=int, default=None)
    ap.add_argument('--stop-at', type=int, default=0, help='自检用：跑到第 N 步按停止请求处理（存 last.pt、rc 143）')
    ap.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu'); ap.add_argument('--seed', type=int, default=0)
@@ -106,7 +107,7 @@ def main():
    print(f'train samples {data.n}, val tasks {len(val_tasks)}, act_scale {act_scale}, data_ver {data.data_ver}, device {dev}', flush=True)
    model = build(cfg).to(dev); model.act_scale = act_scale
    print(f'params {sum(p.numel() for p in model.parameters()) / 1e6:.2f} M', flush=True)
-    opt = torch.optim.AdamW(model.parameters(), lr=tc['lr'], betas=tuple(tc['betas']), weight_decay=tc['wd'])
+    opt = torch.optim.AdamW([p_ for p_ in model.parameters() if p_.requires_grad], lr=tc['lr'], betas=tuple(tc['betas']), weight_decay=tc['wd'])
    step0, best, bad_evals = 0, -1.0, 0
    def check_scale(ck, src):
        s0 = float(ck.get('act_scale', 1.0))
@@ -160,7 +161,8 @@ def main():
                l_mf, xh = wta_loss(model, mem, Y); mf_raw = l_mf.detach(); xh = xh.float()
            else:
                p_neq = tc['p_neq'] if step >= warm else 0.0
-                l_mf, mf_raw, aux = meanflow_loss(model, mem, Y, p_neq=p_neq, return_aux=True, p_edge=tc.get('p_edge', 0.0))
+                l_mf, mf_raw, aux = meanflow_loss(model, mem, Y, p_neq=p_neq, return_aux=True, p_edge=tc.get('p_edge', 0.0),
                                                  target=tc.get('mf_target', 'mf'), lam=tc.get('mf_lam', 0.5))
                sel = ((aux['r'] == aux['t']) & (aux['t'] <= tc.get('fk_tmax', 0.3))).nonzero(as_tuple=True)[0]
                tt = aux['t'][sel][:, None, None]
                xh = aux['z'][sel] - tt * aux['u'][sel]          # 配对去噪估计（fp32）
--- a/Show More
+++ b/Show More