{
  "schemaVersion": 1,
  "lessonId": "pretraining-03",
  "contentHash": "f831dc2fce1ef37c4dd173016bbc86e0579d06d729ff6ff7c5989e9cffadba28",
  "rendererHash": "a1bd5b648f4731ae5d5f0a24811c2540d1a0a132149ce589d17c4113509cbedf",
  "kind": "executed",
  "generatedAt": "2026-10-20T10:47:24.904609+00:00",
  "provenance": "recorded_cpu_computation",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Paired contrastive initial/final: 0.564157247543335 0.013556718826293945 ; all four nearest pairs correct\n",
  "data": {
    "panels": [
      {
        "kind": "line",
        "xlabel": "completed parameter updates before measurement",
        "ylabel": "symmetric contrastive loss (nats)",
        "series": [
          {
            "label": "recorded CPU training loss",
            "x": [
              0,
              1,
              2,
              3,
              4,
              5,
              6,
              7,
              8,
              9,
              10,
              11,
              12,
              13,
              14,
              15,
              16,
              17,
              18,
              19,
              20,
              21,
              22,
              23,
              24,
              25,
              26,
              27,
              28,
              29,
              30,
              31,
              32,
              33,
              34,
              35,
              36,
              37,
              38,
              39,
              40,
              41,
              42,
              43,
              44,
              45,
              46,
              47,
              48,
              49,
              50,
              51,
              52,
              53,
              54,
              55,
              56,
              57,
              58,
              59
            ],
            "y": [
              0.564157247543335,
              0.013737868517637253,
              0.01366741955280304,
              0.01364192645996809,
              0.01363198459148407,
              0.013627370819449425,
              0.013624630868434906,
              0.013622649013996124,
              0.01362091675400734,
              0.013619386591017246,
              0.013617648743093014,
              0.01361605804413557,
              0.013614600524306297,
              0.013612993992865086,
              0.013611475005745888,
              0.013609925284981728,
              0.01360844075679779,
              0.013606919907033443,
              0.013605428859591484,
              0.013604042120277882,
              0.013602584600448608,
              0.013601195067167282,
              0.013599809259176254,
              0.013598334044218063,
              0.013596991077065468,
              0.01359555870294571,
              0.013594303280115128,
              0.0135929174721241,
              0.013591557741165161,
              0.013590333983302116,
              0.013588975183665752,
              0.01358765922486782,
              0.013586463406682014,
              0.013585162349045277,
              0.013583936728537083,
              0.013582678511738777,
              0.013581410050392151,
              0.01358022727072239,
              0.013579089194536209,
              0.013577878475189209,
              0.013576768338680267,
              0.013575555756688118,
              0.013574402779340744,
              0.013573352247476578,
              0.013572152704000473,
              0.01357104443013668,
              0.013569965027272701,
              0.013568912632763386,
              0.013567786663770676,
              0.013566764071583748,
              0.013565668836236,
              0.013564631342887878,
              0.013563626445829868,
              0.013562588021159172,
              0.013561582192778587,
              0.013560589402914047,
              0.013559656217694283,
              0.013558614999055862,
              0.013557638972997665,
              0.013556718826293945
            ]
          }
        ],
        "x": [
          0,
          1,
          2,
          3,
          4,
          5,
          6,
          7,
          8,
          9,
          10,
          11,
          12,
          13,
          14,
          15,
          16,
          17,
          18,
          19,
          20,
          21,
          22,
          23,
          24,
          25,
          26,
          27,
          28,
          29,
          30,
          31,
          32,
          33,
          34,
          35,
          36,
          37,
          38,
          39,
          40,
          41,
          42,
          43,
          44,
          45,
          46,
          47,
          48,
          49,
          50,
          51,
          52,
          53,
          54,
          55,
          56,
          57,
          58,
          59
        ]
      },
      {
        "kind": "heatmap",
        "values": [
          [
            0.9999681115150452,
            -0.03525905683636665,
            -0.9999034404754639,
            -0.02463129349052906
          ],
          [
            -0.032323677092790604,
            0.9999403357505798,
            0.010446705855429173,
            -0.9988009333610535
          ],
          [
            -0.9995343685150146,
            0.012732976116240025,
            0.9999626278877258,
            0.04714934527873993
          ],
          [
            0.006574399769306183,
            -0.9993273615837097,
            0.015307358466088772,
            0.9997304081916809
          ]
        ],
        "rows": [
          "left 0",
          "left 1",
          "left 2",
          "left 3"
        ],
        "columns": [
          "right 0",
          "right 1",
          "right 2",
          "right 3"
        ],
        "unit": "cosine similarity",
        "diverging": true,
        "xlabel": "right-view source ID",
        "ylabel": "left-view source ID",
        "title": "Final cross-view similarities before temperature scaling"
      }
    ]
  }
}