{
  "paper": {
    "arxiv_id": "2607.24439",
    "title": "Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence",
    "url": "https://arxiv.org/abs/2607.24439",
    "organization": "Kuaishou / IIE, CAS / UCAS"
  },
  "dataset": {
    "name": "MovieLens-1M",
    "users": 320,
    "items": 480
  },
  "setup": {
    "seed": 42,
    "steps_per_model": 130,
    "sid_levels": 2,
    "codebook_size": 16,
    "ranking_objectives": [
      "next-item",
      "genre-affinity"
    ]
  },
  "variants": {
    "separate cascade": {
      "initial_loss": 6.19913501739502,
      "final_loss": 5.417207288742065,
      "parameters": 51116,
      "device": "mps",
      "hit_at_10": 0.034375,
      "ndcg_at_10": 0.014269003302980424,
      "head_share_at_10": 0.0403125,
      "sid_code_accuracy": 0.2203125
    },
    "UniR2": {
      "initial_loss": 6.236644124984741,
      "final_loss": 5.050851678848266,
      "parameters": 41274,
      "device": "mps",
      "hit_at_10": 0.03125,
      "ndcg_at_10": 0.012386374417872801,
      "head_share_at_10": 0.0634375,
      "sid_code_accuracy": 0.2953125
    }
  },
  "relative": {
    "hit_at_10_percent": -9.090909090909099,
    "ndcg_at_10_percent": -13.19383593326656,
    "sid_code_accuracy_percent": 34.042553191489354
  },
  "paper_results": {
    "kuaishou_play_volume_percent": 1.177,
    "kuaishou_follow_rate_percent": 0.655,
    "kuaishou_like_rate_percent": 2.56,
    "lite_gifting_users_percent": 0.717,
    "lite_gifting_intention_percent": 1.567,
    "lite_gifting_amount_percent": 2.569
  },
  "scope": "实际训练 Res-KMeans 两级 SID、统一异构序列、generation prefix-causal query、ranking mutual-visible query、共享 Q/K/V、ranking-only LoRA 与 stop-gradient，并和独立 recall/ranker cascade 做同数据全目录对照。MovieLens genre-affinity 代理快手 long-view/gift 等私有多目标，未复刻 8129 codebook、beam service、3×640 线上模型和 5% 流量实验。",
  "runtime": {
    "requested_device": "mps",
    "cpu_threads": null,
    "platform": "macOS arm64",
    "resolved_device": "mps",
    "torch_version": "2.13.0",
    "accelerator": "Apple Metal Performance Shaders"
  },
  "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": [
      "Kuaishou private full-stream multi-objective samples",
      "three-level 8129-way SID and production beam search",
      "3-layer 640-d online service and pipeline parallelism"
    ]
  },
  "schema_version": 2,
  "manifest_ref": "reproduction:unir2",
  "evaluation_protocol": {
    "tier": "l2_public_dataset",
    "seeds": [
      42
    ],
    "formal_comparison": false,
    "claim_policy": "single/few-seed smoke result; do not claim a stable improvement"
  },
  "provenance": {
    "artifact_path": "docs/reproductions/2607.24439-unir2/metrics/movielens-1m-seed42.json",
    "historical_migration": "historical-metrics-v2-2026-08-09",
    "original_code_commit": "not recorded",
    "dataset_fingerprint": "not recorded in historical artifact"
  }
}