Click-through rate (CTR) prediction is a pivotal component in large-scale industrial systems. Historically, CTR prediction paradigms have been confined to monolithic architectures governed by a single-policy optimization process. However, such isolated learning paths lack the intrinsic evolutionary mechanisms necessary for optimal convergence. Without policy diversity and internal competition, models tend to get trapped in local optima as performance reaches saturation, hindering further breakthroughs in modeling capacity. In this paper, we propose DPEO (Dynamic Preference Evolution Optimization), a co-evolutionary framework that transforms CTR modeling into a dynamic policy contention task. DPEO decouples the monolithic architecture into dual sub-learners to induce policy diversity, constructing an internal preference landscape without external rewards. A performance-driven Role Arbiter then dynamically designates the superior sub-learner as the Reference Policy and the other sub-learner as the Target Policy per batch, driving continuous model evolution. Through an asymmetric gradient flow, the target policy is optimized to surpass the reference policy in both probability and logit spaces. This process drives a co-evolution, enabling the sub-learners to serve as alternating evolutionary benchmarks and 'self-evolve' toward the global optimum. Extensive experiments on public benchmarks and a massive industrial dataset with over 10 billion samples demonstrate that DPEO significantly outperforms state-of-the-art models.

Generative Recommendation (GenRec) has emerged as a significant evolutionary direction in the field of recommendation systems in recent years. However, during the reinforcement learning (RL) alignment stage of end-to-end GenRec, a core challenge is that the low signal-to-noise ratio (SNR) of feedback signals triggers reward hacking, which in turn leads to severe distributional drift. Massive click noise and sparse rewards result in highly unstable policy gradients, making it difficult to balance reward optimization and generative stability. To address this, we propose AIPO (Adaptive Anchored Intent-aware Policy Optimization) framework. AIPO synergistically enhances both optimization stability and performance from both data and prior anchoring perspectives. First, it introduces an intent-aware asymmetric resampling mechanism to purify high-confidence conversion intent data, thereby amplifying the gradient contribution of high-value paths. Second, it introduces a prior anchoring mechanism, which dynamically regulates its intensity through a barrier mapping based on the degree of policy distributional drift, mitigating distributional drift and reward hacking caused by low-SNR data. Offline experiments on both public benchmarks and JD's industrial datasets validated the superior performance of AIPO.

The ranking stage serves as the central optimization and allocation hub in advertising systems, governing economic value distribution through eCPM and orchestrating the user-centric blending of organic and advertising content. Prevailing ranking models often rely on fragmented modules and hand-crafted features, limiting their ability to interpret complex user intent. This challenge is further amplified in location-based services such as food delivery, where user decisions are shaped by dynamic spatial, temporal, and individual contexts. To address these limitations, we propose a novel generative framework that reframes ranking as a context comprehension task, modeling heterogeneous signals in a unified architecture. Our architecture consists of two core components: the Generative Contextual Encoder (GCE) and the Generative Contextual Fusion (GCF). The GCE comprises three specialized modules: a Personalized Context Enhancer (PCE) for user-specific modeling, a Collective Context Enhancer (CCE) for group-level patterns, and a Dynamic Context Enhancer (DCE) for real-time situational adaptation. The GCF module then seamlessly integrates these contextual representations through low-rank adaptation. Extensive experiments confirm that our method achieves significant gains in critical business metrics, including click-through rate and platform revenue. We have successfully deployed our method on a large-scale food delivery advertising platform, demonstrating its substantial practical impact. This work pioneers a new perspective on generative recommendation and highlights its practical potential in industrial advertising systems.

Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc-/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/.