{
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   "episode": "motherboard_0510_episode_006.pt",
   "t_start": 1566,
   "first_pred_frame": 5,
   "metrics": {
    "delta": {
     "view": {
      "psnr": 23.0775,
      "ssim": 0.78158,
      "lpips": 0.07877
     },
     "tl": {
      "psnr": 49.3075,
      "ssim": 0.99253,
      "lpips": 0.00775
     },
     "tr": {
      "psnr": 33.8873,
      "ssim": 0.94809,
      "lpips": 0.12501
     }
    },
    "chunk": {
     "view": {
      "psnr": 22.7761,
      "ssim": 0.78098,
      "lpips": 0.08714
     },
     "tl": {
      "psnr": 49.3288,
      "ssim": 0.99244,
      "lpips": 0.00776
     },
     "tr": {
      "psnr": 34.3849,
      "ssim": 0.95275,
      "lpips": 0.1511
     }
    },
    "noisolate": {
     "view": {
      "psnr": 22.1697,
      "ssim": 0.77313,
      "lpips": 0.09779
     },
     "tl": {
      "psnr": 49.7369,
      "ssim": 0.99269,
      "lpips": 0.00775
     },
     "tr": {
      "psnr": 34.814,
      "ssim": 0.951,
      "lpips": 0.12084
     }
    }
   }
  }
 ],
 "probe": {
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    "hl": true
   },
   {
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    "n": 72,
    "hl": false
   },
   {
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    "all": 6.984157711832033,
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    "n": 72,
    "hl": true
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   {
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    "rot": 7.045984472272579,
    "n": 72,
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   },
   {
    "model": "aimotion",
    "all": 8.258736775837962,
    "trans": 9.085174534106638,
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   {
    "model": "gelz",
    "all": 10.085044564277691,
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    "n": 72,
    "hl": false
   },
   {
    "model": "gelzF",
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    "n": 72,
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   {
    "model": "gelz_jitv",
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    "trans": 10.594921144738326,
    "rot": 12.152321467208576,
    "n": 72,
    "hl": false
   },
   {
    "model": "gelz_jit",
    "all": 11.05161020331287,
    "trans": 13.076652824230079,
    "rot": 9.474547521704462,
    "n": 72,
    "hl": false
   },
   {
    "model": "a9dpixflow",
    "all": 11.305266392230571,
    "trans": 17.589166409564083,
    "rot": 7.439902752467703,
    "n": 72,
    "hl": false
   },
   {
    "model": "gelnoF",
    "all": 11.48036148470494,
    "trans": 12.50174121704579,
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    "n": 72,
    "hl": false
   },
   {
    "model": "a9dfilm",
    "all": 12.466668534348521,
    "trans": 13.040619197036108,
    "rot": 11.897830995778977,
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   {
    "model": "gelF",
    "all": 13.866101828751605,
    "trans": 13.891061561531655,
    "rot": 13.866101828751605,
    "n": 72,
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   },
   {
    "model": "a9dfilm_isolate",
    "all": 14.687242374900606,
    "trans": 14.71178700072263,
    "rot": 14.687242374900606,
    "n": 72,
    "hl": false
   },
   {
    "model": "gelz_hist",
    "all": 16.011046508034084,
    "trans": 13.780634660653924,
    "rot": 16.956675106067426,
    "n": 72,
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  ],
  "pairs": [
   {
    "a": "dforce_delta_fps15",
    "b": "dforce_chunk_fps15",
    "all": {
     "n": 72,
     "median": -1.3116269323562968,
     "wins": 53,
     "p": 7.555811484431396e-05,
     "a": 5.245501504649816,
     "b": 6.145055975215692
    },
    "trans": {
     "n": 36,
     "median": -2.2652683953032384,
     "wins": 28,
     "p": 0.0011932429624721408,
     "a": 5.496748470011644,
     "b": 6.94608168472537
    },
    "rot": {
     "n": 36,
     "median": -0.8625600155135884,
     "wins": 25,
     "p": 0.028816719655878842,
     "a": 4.929571771852032,
     "b": 5.794825691069963
    },
    "runs": {
     "0": {
      "n": 12,
      "median": -2.228386865543139,
      "wins": 9,
      "p": 0.14599609375,
      "a": 5.6835861087535235,
      "b": 10.339709685000507
     },
     "1": {
      "n": 12,
      "median": -0.45043635166933127,
      "wins": 8,
      "p": 0.3876953125,
      "a": 5.800509729174208,
      "b": 7.600540206910532
     },
     "2": {
      "n": 12,
      "median": -2.888250582566119,
      "wins": 10,
      "p": 0.03857421875,
      "a": 3.4212059820458522,
      "b": 5.838904213568022
     },
     "3": {
      "n": 12,
      "median": -1.8519421387485397,
      "wins": 11,
      "p": 0.00634765625,
      "a": 2.3179310684290044,
      "b": 5.026677519221099
     },
     "4": {
      "n": 12,
      "median": -1.283557732579828,
      "wins": 9,
      "p": 0.14599609375,
      "a": 12.319647867492531,
      "b": 15.393194855924587
     },
     "5": {
      "n": 12,
      "median": 0.5194583649894791,
      "wins": 6,
      "p": 1.0,
      "a": 4.666715917626309,
      "b": 3.811945583095117
     }
    },
    "title": "delta_action vs action_chunking (the action representation)",
    "line": "data.action_repr",
    "a_label": "delta_action + isolate",
    "b_label": "action_chunking + isolate"
   },
   {
    "a": "dforce_delta_fps15",
    "b": "dforce_noisolate_fps15",
    "all": {
     "n": 72,
     "median": -0.572607464120025,
     "wins": 41,
     "p": 0.28878428041410487,
     "a": 5.245501504649816,
     "b": 6.984157711832033
    },
    "trans": {
     "n": 36,
     "median": -0.8336529561546118,
     "wins": 22,
     "p": 0.2429849540349096,
     "a": 5.496748470011644,
     "b": 6.864965269826607
    },
    "rot": {
     "n": 36,
     "median": -0.02830317712130287,
     "wins": 19,
     "p": 0.8679394004284404,
     "a": 4.929571771852032,
     "b": 7.063216639848914
    },
    "runs": {
     "0": {
      "n": 12,
      "median": -3.036514013419933,
      "wins": 11,
      "p": 0.00634765625,
      "a": 5.6835861087535235,
      "b": 9.858984594453315
     },
     "1": {
      "n": 12,
      "median": -0.5457524930651716,
      "wins": 7,
      "p": 0.7744140625,
      "a": 5.800509729174208,
      "b": 7.629233074890825
     },
     "2": {
      "n": 12,
      "median": -1.1711654143403654,
      "wins": 8,
      "p": 0.3876953125,
      "a": 3.4212059820458522,
      "b": 4.670578280567762
     },
     "3": {
      "n": 12,
      "median": -3.1314645567478934,
      "wins": 10,
      "p": 0.03857421875,
      "a": 2.3179310684290044,
      "b": 6.133644409478805
     },
     "4": {
      "n": 12,
      "median": 1.8775548781807263,
      "wins": 2,
      "p": 0.03857421875,
      "a": 12.319647867492531,
      "b": 11.46528591319611
     },
     "5": {
      "n": 12,
      "median": 1.0405645574619888,
      "wins": 3,
      "p": 0.14599609375,
      "a": 4.666715917626309,
      "b": 3.547993966650809
     }
    },
    "title": "isolate_sensors true vs false (do the two gels attend each other?)",
    "line": "model.isolate_sensors",
    "a_label": "delta_action + isolate",
    "b_label": "delta_action, no isolate"
   }
  ]
 },
 "lede": "Three runs encode the tactile force as a <b>change</b> of indentation (<code>data.force_term: delta</code>, k_f&middot;(f[t+1]&minus;f[t])) on the 15 fps latent tree, with no pixel-flow branch. They form two one-line comparisons that share a corner: <code>data.action_repr</code> (per-step <code>delta_action</code> vs anchor-relative <code>action_chunking</code>) and <code>model.isolate_sensors</code> (may the two gels attend each other?). Same data, same seed, and the held-out rollouts below draw the same 24 windows in every arm (asserted, not assumed).",
 "verdict": "<b>The action representation splits by stream, and the cross-sensor attention mask costs tactile accuracy.</b> <code>delta_action</code> beats <code>action_chunking</code> on the camera view (24.30 vs 23.99 dB, 18&ndash;20 of 24 windows) and on commanded actions (5.5 vs 6.9 px median translation error), while <code>action_chunking</code> wins the tactile images (right gel 42.23 vs 41.47 dB, 5 of 24). Turning the isolation mask OFF &mdash; letting the two gels attend each other again &mdash; improves both gels (right gel 42.30 vs 41.47 dB; SSIM wins 20/24 and 19/24 for the unmasked arm, p&le;0.007) at no measurable cost to the view (15/24, p=0.31). <b>This is the first time the isolate ablation has moved anything</b> &mdash; it tied on val loss at 6 fps and 15 fps with the absolute force term, and it ties again here (0.0901 / 0.0902 / 0.0901). On the probes its median favours the masked arm (5.2 vs 7.0 px) but the paired count is only 41 of 72 (p=0.29) and flips sign on two of the six start frames, so that gap is not supported.",
 "diff": "data:\n  force_term: delta            # both arms\n  action_repr: delta_action    # job 514906\n  action_repr: action_chunking # job 514907\n\ndelta_action     row k = pose step idx[k] -> idx[k+1],  force f[k+1] - f[k]\naction_chunking  row k = pose idx[0] -> idx[k+1],       force f[k+1] - f[0]\n                 (idx[0] = the window's first frame, the anchor)",
 "diff_note": "The two are the same converter on a different pose pair; a window's delta rows telescope to the chunk's last row. They also compress differently into the 3 latent transitions the model conditions on: <code>delta_action</code> average-pools the 5 frame rows of a block, <code>action_chunking</code> takes the block's LAST row (averaging anchor-relative rows would be meaningless).",
 "loss_note": "Both ran 100 epochs on 2xH100 and overfit after ~ep35, so these are the best-* checkpoints, not <code>last.ckpt</code>: final-epoch val is 0.0950 / 0.0949. The run's split file warns that val aliases test, so treat every number here as a development metric.",
 "metric_note": "<b>Predicted frames only.</b> The rollout is given 2 context latent frames (decoded frames 0&ndash;4) and generates 2 more (frames 5&ndash;12); averaging over all 13 frames, as the older decode script does, adds ~2 dB of free PSNR from frames that are identical by construction. Green = better of the two arms.",
 "sign_note": "Paired over the same 24 held-out windows. Windows inside one episode overlap in time, so they are not fully independent and the p-values are optimistic; the win counts are the more honest summary. 24 windows can at best reach p=1.2e-7.",
 "probe_note": "React <code>probes_v1</code>: 72 commanded axis-aligned motions from 6 real start frames, scored geometrically by tracking the gel centre through the decoded video with AllTracker (median final error, crop px; lower is better). All models on one table, but the 15 fps rows measure a 0.53 s horizon and the 6 fps rows 1.33 s &mdash; compare within a rate only. Do-nothing scores 38.7 px on translations.",
 "pair_note": "The 72 probes are 6 start frames x 12 directions, and probes sharing a start frame share context and episode, so the 'all 72' p-value treats correlated samples as independent. Read the per-start-frame rows: delta is ahead on 5 of 6.",
 "strip_note": "Row 1 is the model's prediction, row 2 the ground truth, one column per decoded frame. Frames 0&ndash;4 come from the 2 given context latents (they are GT in both rows); the red box marks frame 5, the first generated one.",
 "caveats": [
  "<b>The isolate comparison is a mask-only change</b>: no parameter count or weight shape changes, so the two arms differ solely in which attention edges exist. The same ablation has come out a tie on val loss twice before (fps6 and fps15, absolute force); this is the third setting it is asked in.",
  "<b>The delta arms' force channel is dead on these probes.</b> With the commanded future force held constant, every per-step row has &Delta;f = 0, so <code>act_wdforce_hold</code> IS the plain rigid action. The chunk arm still sees f_commanded &minus; f_anchor. The two arms are not on equal footing on the force channel &mdash; that is a property of the encodings, not of the eval.",
  "<b>The absolute-force reference rows are not paired</b> with the dforce cells: they were rolled out on 10 windows drawn independently, and random.sample(N, 24) does not extend random.sample(N, 10).",
  "<b>Only the two named pairs are controlled.</b> action_chunking+isolate against delta_action-without-isolate differs in two lines at once; the summary table shows all three arms side by side, but read a difference only along one axis.",
  "<b>Probe grid offset.</b> For start frames run1 and run4 the probe's commanded pose p[0] sits one native frame off the window grid, up to 10.3 mm from the real pose at t_ctx. The spliced per-step stream silently drops that jump; the anchor-relative rows carry it. Pre-existing, and it hits the absolute-force runs identically.",
  "<b>Bug fixed to make this eval valid:</b> <code>scripts/inference_rollout_mot.py</code> called <code>_compress_action(act, W_lat)</code> without <code>action_repr</code>, so every <code>action_chunking</code> run was rolled out with average-pooled anchor-relative rows instead of block-last ones. Any earlier short-horizon eval of a chunk run is suspect.",
  "Single seed (0) for both the rollouts and the probes."
 ],
 "repro": "# held-out rollout + PSNR/SSIM/LPIPS + PRED-over-GT strips; ARMS_SPEC selects the\n# arms and the cells are idempotent, so an added arm reuses the existing ones\nsbatch vm_diffusion/run_eval_dforce_ab.sh\n\n# commanded rows for the delta encoding (act_wdforce_* / act_chunk_wdforce_*)\nPROBE_FPS=15 python -m vm_diffusion.probe_eval.add_dforce_actions\n\n# React probe eval (MODELS_SPEC picks the arms; default = the first two)\nsbatch vm_diffusion/probe_eval/run_probe_eval_dforce.sh\n\n# this page\npython -m vm_diffusion.scripts.build_hf_space_dforce --push\n"
}