EvoDAGRec: A Multi-Agent Code DAG Evolution Framework for Generative Recommendation

Large Language Model (LLM) agents have enabled automated code evolution, offering a promising paradigm for algorithm discovery. However, current recursive self-improvement (RSI) research for sequential recommendation faces a critical dilemma: general-purpose code evolution frameworks possess flexible search mechanisms but lack domain-specific feedback, whereas recommender-specific evolution frameworks utilize rich feedback but rely on linear evolution. More critically, naively integrating them fails when applied to Generative Recommendation (GR). Unlike traditional end-to-end models, GR relies on a module-cascaded training pipeline (i.e., tokenization, pre-training, and alignment). In this paradigm, existing naive evolution methods inevitably suffer from i) cost blindness: squandering budgets by ignoring stage-asymmetric retraining overheads; and ii) short-sighted evaluation: discarding promising upstream innovations that temporarily regress metrics before downstream adaptation. To bridge these gaps, we propose EvoDAGRec, a multi-agent code evolution framework explicitly tailored for GR. EvoDAGRec organizes code evolution as a Directed Acyclic Graph (DAG) that explicitly maintains multiple evolution branches and merges complementary innovations, and harmonizes an advanced search mechanism with rich GR-specific diagnostic feedback. We further introduce stage-aware cost planning to optimize real-world GPU-hour budgets, and active downstream co-adaptation to navigate temporary regressions and unlock deferred cross-stage gains. On public Amazon datasets, EvoDAGRec marks the first autonomous evolution of a complete GR pipeline, improving offline metrics by up to 37.4% and boosting Agent4Rec simulated user satisfaction by 19.7% over the strongest GR baseline. Furthermore, in industrial settings, the evolved GR achieves a 12.16% relative improvement in offline Hit@1 and, when deployed as an additional retrieval channel on a large-scale commercial media platform, a statistically significant 0.40% increase in online click-through rate (CTR).