{
  "paper": {
    "arxiv_id": "2608.24034",
    "title": "TAGR"
  },
  "dataset": {
    "name": "MovieLens-1M",
    "users": 320,
    "items": 480
  },
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    "seed": 42,
    "full_catalog": true,
    "lsid_levels": 2,
    "intent_scales": 3
  },
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    "TAGR core": {
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      "ndcg_at_10": 0.08211786567929354,
      "head_share_at_10": 0.1303125
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    "head_share_at_10_percent": -82.08762886597938
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  "paper_results": {
    "live_room_entry_rate_lift_percent": 8.5,
    "shopping_cart_click_rate_lift_percent": 7.4,
    "revenue_lift_percent": 16.1
  },
  "scope": "在 MovieLens-1M 上真实执行可刷新但词表稳定的两级 LSID、多尺度 intent 聚合，以及行为/价值双分支的有界 IOPO 推理 analogue；未复刻快手实时广告日志、在线 RM/GRPO 更新和生产 serving。",
  "runtime": {
    "requested_device": "auto",
    "cpu_threads": null,
    "platform": "Darwin arm64",
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  "seed": 42,
  "schema_version": 2,
  "manifest": {
    "adapter_key": "tagr",
    "arxiv_id": "2608.24034",
    "title": "TAGR: Temporally Adaptive Generative Recommendation for Industrial Live-Streaming Advertising",
    "paper_url": "https://arxiv.org/abs/2608.24034",
    "track": "recommendation",
    "organization": "Kuaishou Technology",
    "published": "2026-08-25",
    "code_url": null,
    "topics": [
      "generative-recommendation",
      "live-streaming-advertising",
      "semantic-id",
      "online-rl"
    ],
    "local_code_dir": "src/auto_research/reproductions/tagr",
    "fidelity": "core_mechanism",
    "evaluation_tier": "l2_public_dataset",
    "datasets": [
      "MovieLens-1M"
    ],
    "baseline": "production-style first-order transition recommender",
    "metrics": [
      "Hit@10",
      "NDCG@10",
      "head share@10"
    ],
    "default_seeds": [
      42
    ],
    "budget": "adapter-defined fixed budget",
    "device_capabilities": [
      "cpu"
    ],
    "online_evidence": [
      {
        "product": "Kuaishou live-stream advertising",
        "metric": "live-room entry rate",
        "lift_percent": 8.5,
        "traffic": "production A/B under the same downstream stack",
        "source_url": "https://arxiv.org/html/2608.24034v1#S4.SS3",
        "source_location": "Section 4.3 / Table 1",
        "retrieved_at": "2026-08-26"
      },
      {
        "product": "Kuaishou live-stream advertising",
        "metric": "shopping-cart click rate",
        "lift_percent": 7.4,
        "traffic": "production A/B under the same downstream stack",
        "source_url": "https://arxiv.org/html/2608.24034v1#S4.SS3",
        "source_location": "Section 4.3 / Table 1",
        "retrieved_at": "2026-08-26"
      },
      {
        "product": "Kuaishou live-stream advertising",
        "metric": "revenue",
        "lift_percent": 16.1,
        "traffic": "multi-week production experiment",
        "source_url": "https://arxiv.org/html/2608.24034v1#S4.SS3",
        "source_location": "Section 4.3 / Table 1",
        "retrieved_at": "2026-08-26"
      }
    ],
    "selection_exception": null,
    "evolve_operators": []
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  "provenance": {
    "created_at": "2026-08-26T07:15:22.010441+00:00",
    "code_commit": "98dc66ed800aade784d908e284e3924b16d94bbf",
    "python": "3.12.9",
    "platform": "macOS-26.5.2-arm64-arm-64bit",
    "dataset_dir": "/Users/bytedance/Documents/git_daiwk/auto-research/data",
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      "transformers": "5.14.1"
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    "artifact_path": "docs/reproductions/2608.24034-tagr/metrics/movielens-1m-seed42.json"
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  "evaluation_protocol": {
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    "tier_label": "L2 公开数据集训练",
    "seeds": [
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    ],
    "budget": "paper-specific",
    "formal_comparison": false,
    "claim_policy": "single/few-seed smoke result; do not claim a stable improvement"
  },
  "reproduction_fidelity": {
    "level": "core_mechanism",
    "label": "核心机制复现",
    "description": "论文中心算法被实际执行，但生产模型、私有特征或基础设施未复刻。",
    "omitted_core_components": [
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      "online reward model and GRPO training",
      "real-time LSID and serving engine"
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  },
  "manifest_ref": "reproduction:tagr"
}