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p-doom
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idm-eval-set

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IDM Eval Set

A validation set for evaluating Inverse Dynamics Models on macOS screen recordings. Each sample is a 5-second clip of real desktop usage (browser, IDE, terminal) paired with a ground-truth action log captured at the OS level.

The task: given a short screen recording, predict the sequence of user input actions (keypresses, mouse clicks, scrolls) that produced the observed screen changes.

Dataset Structure

clips_recording_{uuid}_seg{N}/
  clip_000_{tag}.mp4      # 5s screen recording (1728x1080)
  clip_000_{tag}.json      # ground truth action log
editing-work/
  input_{uuid}_seg{N}.msgpack    # raw input event streams
  recording_{uuid}_seg{N}.mp4    # full source recordings

Stats


Clips	46
Recordings	9
Total raw actions	10,914
Resolution	1728 x 1080
Clip duration	5 seconds

Tag distribution:

Tag	Count
scroll/drag	18
keystroke-heavy	17
mixed	4
click-heavy	4
hotkeys	2
hard-case	1

Action Log Format

Each clip JSON contains:

{
  "start_s": 206.913,
  "end_s": 211.913,
  "tag": "keystroke-heavy",
  "actions": [
    [206933331, ["KeyPress", [32, "Space"]]],
    [207233331, ["KeyRelease", [32, "Space"]]],
    [208633331, ["MousePress", ["Left", 0, 0]]],
    [209533331, ["MouseScroll", [0, -1, 0, 0]]]
  ]
}

Timestamps are absolute microseconds (subtract start_s * 1e6 for clip-relative)
Action types: KeyPress, KeyRelease, MousePress, MouseRelease, MouseMove, MouseScroll, ContextChanged
KeyPress params: [keycode, key_name]
MousePress params: [button, x, y] (coordinates not captured in this version)
MouseScroll params: [dx, dy, x, y]

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