Large Language Model (LLM) agents trained with reinforcement learning (RL) show great promise for solving complex, multi-step tasks. However, their performance is often crippled by ''Context Explosion'', where the accumulation of long text outputs overwhelms the model's context window and leads to reasoning failures. To address this, we introduce CoDA, a Context-Decoupled hierarchical Agent, a simple but effective reinforcement learning framework that decouples high-level planning from low-level execution. It employs a single, shared LLM backbone that learns to operate in two distinct, contextually isolated roles: a high-level Planner that decomposes tasks within a concise strategic context, and a low-level Executor that handles tool interactions in an ephemeral, isolated workspace. We train this unified agent end-to-end using PECO (Planner-Executor Co-Optimization), a reinforcement learning methodology that applies a trajectory-level reward to jointly optimize both roles, fostering seamless collaboration through context-dependent policy updates. Extensive experiments demonstrate that CoDA achieves significant performance improvements over state-of-the-art baselines on complex multi-hop question-answering benchmarks, and it exhibits strong robustness in long-context scenarios, maintaining stable performance while all other baselines suffer severe degradation, thus further validating the effectiveness of our hierarchical design in mitigating context overload. Our code is available at https://github.com/liuxuanzhang718/CoDA.

Traditional conversion rate prediction models suffer from suboptimal performance due to sharing the same network parameters for all instances, failing to capture heterogeneous underlying distributions across instances. Recent parameter personalized network based models address this by grouping instances and adjust parameters for each group. However, existing parameter personalization methods face challenges: (1) taking prior information features as grouping condition for model parameter personalization leads to suboptimal performance due to human's limited understanding of data distribution, or (2) using all the features for both parameter generation module and deep neural network (DNN) of conversion prediction tasks causes gradient conflicts during backpropagation. A better approach is to automatically select features as grouping condition based on data distribution through iterative learning. Therefore, we propose Automatic Feature Partitioning-Based Parameter Personalized Network (APPNet), which consists of two components: Automatic Feature Partitioning (AFP) and Parameter Personalized Network (PPNet). The AFP module automatically partitions all the features into two parts: one part for DNN of conversion prediction tasks, and the other part for PPNet module to generate weights to adjust DNN parameters of conversion prediction tasks. Specifically, we implemented two versions of AFP: feature-wise AFP and bit-wise AFP. The feature-wise AFP partitions features at the feature field granularity, while the bit-wise AFP partitions each bit of the feature embeddings. The PPNet module adjusts model parameters of conversion prediction task for each group of instances by applying element-wise multiplication to the DNN parameters of conversion tasks. Extensive offline experiments demonstrate APPNet outperforms previous parameter personalized models. Furthermore, online A/B testing in production system achieved a 1.09% improvement on conversion rate, validating its practical effectiveness.

Online Travel Platforms (OTPs) suffer significant revenue loss from supply strikes, where rooms with physical vacancies appear sold out due to delays in manual inventory updates from hotels. While proactively adding inventory is a potential solution, this intervention faces a dual risk: hotels may later reject the booking, and more critically, the intervention might not generate platform-wide revenue, but merely shift sales from a competing hotel. This paper is the first to formalize the inventory decision on OTPs as a causal inference problem. We propose CS2NET, a Causality-Driven, Scarcity- and Service-Aware Network that estimates the platform-wide Individual Treatment Effect of each inventory addition. CS2NET addresses the unique challenges of the OTP environment by integrating: (1) a Room Type Scarcity Representation module for inferring true room availability, (2) a Hotel Service-Engagement Representation module for predicting hotel acceptance, and (3) a bias-corrected causal framework to estimate platform-level uplift while mitigating selection bias. Extensive experiments and an online A/B test on a major OTP, demonstrate that CS2NET significantly increases confirmed bookings and platform revenue, generating over 10 million RMB in additional annual GMV. We also release the first causality dataset for third-party inventory management.

The past decades witness the significant advancements in time series forecasting (TSF) across various real-world domains, including e-commerce and disease spread prediction. However, TSF is usually constrained by the uncertainty dilemma of predicting future data with limited past observations. To settle this question, we explore the use of Cross-Future Behavior (CFB) in TSF, which occurs before the current time but takes effect in the future. We leverage CFB features and propose the CRoss-Future Behavior Awareness based Time Series Forecasting method (CRAFT). The core idea of CRAFT is to utilize the trend of cross-future behavior to mine the trend of time series data to be predicted. Specifically, to settle the sparse and partial flaws of cross-future behavior, CRAFT employs the Koopman Predictor Module to extract the key trend and the Internal Trend Mining Module to supplement the unknown area of the cross-future behavior matrix. Then, we introduce the External Trend Guide Module with a hierarchical structure to acquire more representative trends from higher levels. Finally, we apply the demand-constrained loss to calibrate the distribution deviation of prediction results. We conduct experiments on real-world dataset. Experiments on both offline large-scale dataset and online A/B test demonstrate the effectiveness of CRAFT. Our dataset and code are available at https://github.com/CRAFTinTSF/CRAFT.

With the flourishing development of aviation and the convenience of booking flights online, nearby flight recommendation has become the core business of Online Travel Platforms (OTPs). Nearby flight addresses the issue of inadequate flight options for travelers by offering more cost-effective alternatives, such as recommending flights from nearby cities or on nearby departure dates. Currently, mainstream OTPs adopt rule-based or simple user preference-based strategies to recommend nearby flights. However, the insufficient emphasis on the user's historical behaviors and the ignorance of nearby flight's context make these existing strategies less effective in solving the nearby flight recommendation. To this end, a Context-based Personalized Deep Net work (CPNet) is proposed in this paper for nearby flight recommendation. In CPNet, a Personalized Preferences Learning (PPL) component is first proposed to encapsulate users' individual preferences, leveraging crucial feature correlations between historical behaviors and target nearby flight. Then, a Historical Cost Learning (HCL) component is designed to learn the price sensitivity of users under the same query and the same nearby flight recommendation. Finally, we present a Context Potential Gain Learning (CPGL) component, where the important cost between target nearby flight and context flights are emphasized and learned. Offline experiments on a production dataset and a world-scale online A/B test at Fliggy. Fliggy: https://www.fliggy.com/ both demonstrate the superiority of the proposed CPNet over baselines. CPNet is now successfully deployed at Fliggy, one of the largest OTPs in China, serving millions of users every day for flight reservations.

Personalized product search provides significant benefits to e-commerce platforms by extracting more accurate user preferences from historical behaviors. Previous studies largely focused on the user factors when personalizing the search query, while ignoring the item perspective, which leads to the following two challenges that we summarize in this paper: First, previous approaches relying only on co-occurrence frequency tend to overestimate the conversion rates for popular items and underestimate those for long-tail items, resulting in inaccurate item similarities; Second, user purchasing propensity is highly heterogeneous according to the popularity of the target item: it is less correlated with the user's historical behavior for a popular item and more correlated for a long-tail item. To address these challenges, in this paper we propose NAM, a Normalization Attention Model, which optimizes ''when to personalize'' by utilizing Inverse Item Frequency (IIF) and employing a gating mechanism, as well as optimizes ''how to personalize'' by normalizing the attention mechanism from a global perspective. Through comprehensive experiments, we demonstrate that our proposed NAM model significantly outperforms state-of-the-art baseline models. Furthermore, we conducted an online A/B test at Fliggy, and obtained a significant improvement of 0.8% over the latest production system in conversion rate.