Sustaining industrial recommendation research requires using the results of one experiment to decide what to investigate next. We present AgentX-Model, the next generation of AgentX's model research framework, which connects proposal development and model experimentation within sandboxes defined by business inputs and...
Shuang Yang, Zi-Jie Zhuang, Chang-Xin Lao et al.· 0 citations
Generative recommendation formulates recommendation task into an SID sequence autoregressive generation paradigm, but the decoding process is often dominated by generation likelihood. This may conflict with real-world business objectives, where high-value candidates can receive low generation probability and be pruned...
Results show that generative retrieval can combine shared modeling with objective-specific control and complementary candidate generation, and under the same 512-item retrieval budget, Multi-Decoder OneRec improves over the single-decoder OneRec baseline.
This work proposes UniR, a decoder-only Transformer that unifies Generative and Multi-Objective ranking within a single heterogeneous sequence comprising user context, SID trajectory, and item features, validating the practicality of unified model in large-scale recommendation systems.
These results show that RecoReward trains the MLLM to produce item features that benefit downstream recommendation while retaining content-only serving, and shows that RecoReward-9B outperforms its Qwen3.5-9B baseline and all other evaluated models across seven recall metrics.
Guohong Mu, Yue-Yang Liu, Jiangxia Cao et al.· arXiv.org· 0 citations
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