{
  "schema": "project-experience.writing-task-input.v1",
  "status": "constructed_example_not_evaluated",
  "context_rule": "Only supplied excerpts with Overleaf revision <= cutoff are included. Prompts and selected cutoffs were designed retrospectively; this is not original-context replay or a sealed evaluation.",
  "task": {
    "id": "WA1",
    "title": "Make the formulation explicit without manufacturing novelty",
    "action": "Revise scientific positioning",
    "cutoff_revision": 439,
    "prompt": "Using only the supplied introduction excerpts and working note, write a short replacement passage that makes learning from expert examples explicit. Preserve the described prefix-to-next-decision supervision. Respond to the note's comparison request, but do not invent facts about the comparator named “graphq.” If the supplied material cannot support a substantive comparison, identify the missing evidence separately rather than assert an advantage. Do not claim a new invention date, priority, measured speedup, training stability or algorithm switch.",
    "output_request": "A proposed introduction passage of roughly 80–140 words, followed by a brief missing-evidence note. The length is a new task constraint, not a historical requirement.",
    "skill_ids": [
      "WS01",
      "WS05",
      "WS06"
    ],
    "input_note": "The revision-439 note is supplied with older revision-256 passages. This is deliberately a constructed archival exercise; the full introduction at revision 439 is unavailable.",
    "current_source_ids": [
      "A02"
    ],
    "earlier_context_ids": [
      "A01",
      "A09"
    ],
    "sources": [
      {
        "evidence_id": "A02",
        "source_file": "introduction.tex",
        "content_kind": "working_note",
        "text": "Use imitation learning to compare with graphq",
        "text_view": "full_source_excerpt",
        "presented_text_sha256": "c576b1f6d1559dd8c497d453c750aebcedcbbc6361f315bd47021ef55b79f88f",
        "context_status": "A selected manuscript passage, not a complete record of the context available when it was written. The records do not establish who wrote it; scientific claims are unverified.",
        "excerpt_sha256": "c576b1f6d1559dd8c497d453c750aebcedcbbc6361f315bd47021ef55b79f88f",
        "manuscript_revision": 439
      },
      {
        "evidence_id": "A01",
        "source_file": "introduction.tex",
        "content_kind": "manuscript_prose",
        "text": "Most learning-based SAT methods either provide instance-level guidance or learn state-centric policies; neither directly models the decision sequence executed at runtime.\nThis suggests aligning learning targets with solver behavior by modeling the decision sequence itself. Long‑context sequence models—particularly Transformers~\\citep{vaswani2017attention}-are well-suited to this formulation and can be used to propose the next branch online within CDCL.",
        "text_view": "full_source_excerpt",
        "presented_text_sha256": "3c563569a464806b863e25684389da27e5b06c30bf79f221801f55e670e13456",
        "context_status": "A selected manuscript passage, not a complete record of the context available when it was written. The records do not establish who wrote it; scientific claims are unverified.",
        "excerpt_sha256": "3c563569a464806b863e25684389da27e5b06c30bf79f221801f55e670e13456",
        "manuscript_revision": 256
      },
      {
        "evidence_id": "A09",
        "source_file": "introduction.tex",
        "content_kind": "manuscript_prose",
        "text": "Training data are [keytraces] distilled from solved instances. From a full CDCL run, contiguous backtracks are collapsed to obtain a near conflict‑free sequence of branching decisions along a solution or refutation path. Each prefix paired with its subsequent decision provides a clear stepwise target.",
        "text_view": "full_source_excerpt",
        "presented_text_sha256": "4b72f3dad06de7b0d9f0ee8a7acaa7d6dfdb62d60f6a9dc86fd866b7406455d7",
        "context_status": "A selected manuscript passage, not a complete record of the context available when it was written. The records do not establish who wrote it; scientific claims are unverified.",
        "excerpt_sha256": "4b72f3dad06de7b0d9f0ee8a7acaa7d6dfdb62d60f6a9dc86fd866b7406455d7",
        "manuscript_revision": 256
      }
    ],
    "prompt_origin": "newly_authored_derived_instruction_not_historical_prompt",
    "historical_note_available": true,
    "reference_completion_included": false,
    "independent_project_group": "ImitSAT-single-project"
  },
  "version": "1.0",
  "use_notice": "For discussing a possible data partnership. Training, redistribution and other uses require a separate agreement."
}