{
  "title": "Ropedia Xperience-10M Research Roadmap",
  "summary": "Staged path from the public-sample task lab to multi-episode held-out evaluation and larger omni-model extensions.",
  "current_decision_point": "Keep the public-sample task suite as the development harness, then stage enough official Xperience-10M episodes to run the 32-episode held-out pilot.",
  "phases": [
    {
      "id": "public_sample_task_lab",
      "name": "Public-Sample Task Lab",
      "status": "implemented",
      "entry_condition": "One public Xperience-10M sample episode is available.",
      "deliverables": [
        "1161 aligned windows",
        "12 task contracts",
        "minimal baseline heads",
        "neural MLP heads",
        "modality atlas",
        "task walkthroughs",
        "derived figures"
      ],
      "completion_evidence": [
        "PROJECT_STATUS.md",
        "EVALUATION_PROTOCOL.md",
        "RESEARCH_TAKEAWAYS.md",
        "docs/data/summary_metrics.json",
        "results/episode_task_suite/summary_report.json"
      ],
      "reader_takeaway": "The public sample supports task design, feature contracts, walkthroughs, and baseline comparisons."
    },
    {
      "id": "multi_episode_data_staging",
      "name": "Multi-Episode Data Staging",
      "status": "active",
      "entry_condition": "Gated dataset access and enough storage for selected episodes.",
      "deliverables": [
        "32 valid episodes",
        "episode manifest",
        "missing-view manifest",
        "held-out episode split",
        "source-discovery report"
      ],
      "completion_evidence": [
        "results/omni_finetune/DATA_ACCESS_STATUS.md",
        "results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md",
        "results/omni_finetune/source_discovery.json"
      ],
      "reader_takeaway": "The next scale decision is data staging, with train/test separation at the episode level."
    },
    {
      "id": "qwen3_omni_lora_pilot_32_episode",
      "name": "32-Episode Qwen3-Omni LoRA Pilot",
      "status": "next",
      "entry_condition": "At least 32 valid episodes are staged locally with no train/test episode leakage.",
      "deliverables": [
        "dataset JSONL/media manifests",
        "LoRA adapter checkpoint",
        "progress logs",
        "held-out predictions",
        "metrics",
        "confusion matrices",
        "run report"
      ],
      "completion_evidence": [
        "dataset_manifest.json",
        "training_metadata.json",
        "progress.jsonl",
        "metrics.json",
        "predictions.jsonl",
        "RUN_REPORT.md"
      ],
      "reader_takeaway": "The first omni-model pilot should establish a complete held-out-episode training and evaluation loop."
    },
    {
      "id": "robustness_run_64_128_episode",
      "name": "64-128 Episode Robustness Run",
      "status": "planned",
      "entry_condition": "The 32-episode pilot trains and evaluates cleanly.",
      "deliverables": [
        "split-by-session metrics",
        "modality ablations",
        "calibration/object/language error analysis",
        "missing-view sensitivity analysis"
      ],
      "completion_evidence": [
        "held-out metrics by session",
        "held-out metrics by task",
        "held-out metrics by modality",
        "ablation tables",
        "qualitative error analysis"
      ],
      "reader_takeaway": "The robustness run tests whether the pilot conclusions survive broader sessions and missing modalities."
    },
    {
      "id": "foundation_world_model_extensions",
      "name": "Foundation and World-Model Extensions",
      "status": "planned",
      "entry_condition": "Enough multi-episode data and compute budget for larger multimodal objectives.",
      "deliverables": [
        "audio encoder integration",
        "depth/image reconstruction",
        "SLAM/world modeling probes",
        "policy-style next-action tasks",
        "affordance and object-interaction tasks"
      ],
      "completion_evidence": [
        "task-specific held-out evaluations",
        "qualitative inspection",
        "updated model cards"
      ],
      "reader_takeaway": "The long-term direction is richer multimodal representation learning for embodied-AI reasoning."
    }
  ],
  "public_surfaces_to_update": [
    "README.md",
    "PROJECT_STATUS.md",
    "RESEARCH_TAKEAWAYS.md",
    "EVALUATION_PROTOCOL.md",
    "ARTIFACT_GUIDE.md",
    "docs/index.html",
    "docs/data/research_roadmap.json",
    "Hugging Face Space card",
    "Hugging Face artifact dataset card",
    "Hugging Face model card"
  ]
}