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AgentX-Model, the next generation of AgentX's model research framework, is presented, which connects proposal development and model experimentation within sandboxes defined by business inputs and prediction tasks within sandboxes defined by business inputs and prediction tasks.
Value-guided decoding is proposed, named RGD, a controllable decoding framework for industrial value-oriented generative recommendation that deriving a closed-form reward guided decoding distribution that principledly combines generation probability with reward signals.
These findings establish articulated rationales as a useful complement to behavioral and content signals, and demonstrate a practical role for MLLMs in scaling sparse human explanations into preference information that improves industrial recommendation.
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.
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