While the OneRec series has successfully unified the fragmented recommendation pipeline into an end-to-end generative framework, a significant gap remains between recommendation systems and general intelligence. Constrained by isolated data, they operate as domain specialists-proficient in pattern matching but lacking world knowledge, reasoning capabilities, and instruction following. This limitation is further compounded by the lack of a holistic benchmark to evaluate such integrated capabilities. To address this, our contributions are: 1) RecIF Bench & Open Data: We propose RecIF-Bench, a holistic benchmark covering 8 diverse tasks that thoroughly evaluate capabilities from fundamental prediction to complex reasoning. Concurrently, we release a massive training dataset comprising 96 million interactions from 160,000 users to facilitate reproducible research. 2) Framework & Scaling: To ensure full reproducibility, we open-source our comprehensive training pipeline, encompassing data processing, co-pretraining, and post-training. Leveraging this framework, we demonstrate that recommendation capabilities can scale predictably while mitigating catastrophic forgetting of general knowledge. 3) OneRec-Foundation: We release OneRec Foundation (1.7B and 8B), a family of models establishing new state-of-the-art (SOTA) results across all tasks in RecIF-Bench. Furthermore, when transferred to the Amazon benchmark, our models surpass the strongest baselines with an average 26.8% improvement in Recall@10 across 10 diverse datasets (Figure 1). This work marks a step towards building truly intelligent recommender systems. Nonetheless, realizing this vision presents significant technical and theoretical challenges, highlighting the need for broader research engagement in this promising direction.

翻译：尽管OneRec系列成功地将碎片化的推荐流程统一为端到端的生成框架，但推荐系统与通用智能之间仍存在显著差距。受限于孤立数据，现有系统仅作为领域专家运行——擅长模式匹配，但缺乏世界知识、推理能力和指令遵循能力。这一局限性因缺乏评估此类综合能力的整体基准而进一步加剧。为此，我们的贡献包括：1) RecIF基准与开放数据：我们提出RecIF-Bench，这是一个涵盖8个多样化任务的整体基准，全面评估从基础预测到复杂推理的能力。同时，我们发布了包含16万用户9600万交互的大规模训练数据集，以促进可复现研究。2) 框架与扩展：为确保完全可复现性，我们开源了完整的训练流程，涵盖数据处理、协同预训练和后训练。利用该框架，我们证明了推荐能力可实现可预测的扩展，同时缓解通用知识的灾难性遗忘。3) OneRec基础模型：我们发布了OneRec Foundation（1.7B和8B）模型系列，在RecIF-Bench的所有任务中均创造了新的最优性能（SOTA）。此外，当迁移至Amazon基准时，我们的模型在10个不同数据集上的Recall@10平均提升26.8%，超越了现有最强基线（图1）。这项工作标志着向构建真正智能推荐系统迈出了一步。然而，实现这一愿景仍面临重大技术与理论挑战，凸显了在这一前景广阔的方向上需要更广泛的研究投入。