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Large language models (LLMs) have been incorporated into numerous industrial applications. Meanwhile, a vast array of API assets is scattered across various functions in the financial domain. An online financial question-answering system can leverage both LLMs and private APIs to provide timely financial analysis and information. The key is equipping the LLM model with function calling capability tailored to a financial scenario. However, a generic LLM requires customized financial APIs to call and struggles to adapt to the financial domain. Additionally, online user queries are diverse and contain out-of-distribution parameters compared with the required function input parameters, which makes it more difficult for a generic LLM to serve online users. In this paper, we propose a data-driven pipeline to enhance function calling in LLM for our online, deployed financial QA, comprising dataset construction, data augmentation, and model training. Specifically, we construct a dataset based on a previous study and update it periodically, incorporating queries and an augmentation method named AugFC. The addition of user query-related samples will exploit our financial toolset in a data-driven manner, and AugFC explores the possible parameter values to enhance the diversity of our updated dataset. Then, we train an LLM with a two-step method, which enables the use of our financial functions. Extensive experiments on existing offline datasets, as well as the deployment of an online scenario, illustrate the superiority of our pipeline. The related pipeline has been adopted in the financial QA of YuanBao. https://yuanbao.tencent.com/chat/, one of the largest chat platforms in China.
Industrial-scale recommender systems rely on a cascade pipeline in which the retrieval stage must return a high-recall candidate set from billions of items under tight latency. Existing solutions either (i) suffer from limited expressiveness in capturing fine-grained user-item interactions, as seen in decoupled dual-tower architectures that rely on separate encoders, or generative models that lack precise target-aware matching capabilities, or (ii) build structured indices (tree, graph, quantization) whose item-centric topologies struggle to incorporate dynamic user preferences and incur prohibitive construction and maintenance costs. We present GRank, a novel structured-index-free retrieval paradigm that seamlessly unifies target-aware learning with user-centric retrieval. Our key innovations include: (1) A target-aware Generator trained to perform personalized candidate generation via GPU-accelerated MIPS, eliminating semantic drift and maintenance costs of structured indexing; (2) A lightweight but powerful Ranker that performs fine-grained, candidate-specific inference on small subsets; (3) An end-to-end multi-task learning framework that ensures semantic consistency between generation and ranking objectives. Extensive experiments on two public benchmarks and a billion-item production corpus demonstrate that GRank improves Recall@500 by over 30% and 1.7× the P99 QPS of state-of-the-art tree- and graph-based retrievers. GRank has been fully deployed in production in our recommendation platform since Q2 2025, serving 400 million active users with 99.95% service availability. Online A/B tests confirm significant improvements in core engagement metrics, with Total App Usage Time increasing by 0.160% in the main app and 0.165% in the Lite version.
Accurately modeling long-term value (LTV) at the ranking stage of short-video recommendation systems remains a practical challenge. Though production systems and recent research have begun exploring delayed feedback and extended user engagement, modeling LTV with fine-grained attribution and robust positional normalization for billion-scale platforms is underdeveloped. In this work, we present a practical ranking-stage LTV framework that systematically addresses three core challenges: position bias, attribution ambiguity, and temporal limitations. First, to address position bias in sequential video feeds, we introduce a Position-aware Debias Quantile (PDQ) module that normalizes engagement signals using quantile-based distributions, enabling position-robust LTV estimation without requiring architectural changes. Second, we propose a multi-dimensional attribution module that learns continuous strengths across contextual, behavioral, and content-related signals, moving beyond static rule sets to capture nuanced influences among videos. Explicit noise filtering is incorporated via a customized hybrid loss, improving causal clarity in LTV attribution. Third, our cross-temporal author modeling module constructs censoring-aware, day-level long-term value targets, capturing creator-driven re-engagement over extended time windows. While our framework currently focuses on the author dimension, it is readily extensible to further aspects such as topics or styles. Extensive offline experiments and online A/B tests demonstrate statistically significant gains in LTV-related metrics and stable trade-offs with short?term objectives. The framework is realized as task augmentation within an existing ranking model, facilitates billion-scale deployment on Taobao's production system with efficient training and serving, achieving sustained user engagement improvements while remaining compatible with industrial constraints.
Web recommendations provide personalized items from massive catalogs for users, which rely heavily on retrieval stages to trade off the effectiveness and efficiency of selecting a small relevant set from billion-scale candidates in online digital platforms. As one of the largest Chinese search engine and news feed providers, Baidu resorts to Deep Neural Network (DNN) and graph-based Approximate Nearest Neighbor Search (ANNS) algorithms for accurate relevance estimation and efficient search for relevant items. However, current retrieval at Baidu fails in comprehensive user-item relational understanding due to dissected interaction modeling, and performs inefficiently in large-scale graph-based ANNS because of suboptimal traversal navigation and the GPU computational bottleneck under high concurrency. To this end, we propose a GPU-accelerated Multi-relational Parallel Graph Retrieval (GMP-GR) framework to achieve effective yet efficient retrieval in web-scale recommendations. First, we propose a multi-relational user-item relevance metric learning method that unifies diverse user behaviors through multi-objective optimization and employs a self-covariant loss to enhance pathfinding performance. Second, we develop a hierarchical parallel graph-based ANNS to boost graph retrieval throughput, which conducts breadth-depth-balanced searches on a large-scale item graph and cost-effectively handles irregular neural computation via adaptive aggregation on GPUs. In addition, we integrate system optimization strategies in the deployment of GMP-GR in Baidu. Extensive experiments demonstrate the superiority of GMP-GR in retrieval accuracy and efficiency. Deployed across more than twenty applications at Baidu, GMP-GR serves hundreds of millions of users with a throughput exceeding one hundred million requests per second.
Retrieval-Augmented Generation (RAG) is becoming increasingly essential for Question Answering (QA) in the financial sector, where accurate and contextually grounded insights from complex public disclosures are crucial. However, existing financial RAG systems face two significant challenges: (1) they struggle to process heterogeneous data formats, such as text, tables, and figures; and (2) they encounter difficulties in balancing general-domain applicability with company-specific adaptation. To overcome these challenges, we present VeritasFi, an innovative hybrid RAG framework that incorporates a multi-modal preprocessing pipeline alongside a cutting-edge two-stage training strategy for its re-ranking component. VeritasFi enhances financial QA through three key innovations: (1) A multi-modal preprocessing pipeline that seamlessly transforms heterogeneous data into a coherent, machine-readable format. (2) A tripartite hybrid retrieval engine that operates in parallel, combining deep multi-path retrieval over a semantically indexed document corpus, real-time data acquisition through tool utilization, and an expert-curated memory bank for high-frequency questions, ensuring comprehensive scope, accuracy, and efficiency. (3) A two-stage training strategy for the document re-ranker, which initially constructs a general, domain-specific model using anonymized data, followed by rapid fine-tuning on company-specific data for targeted applications. By integrating our proposed designs, VeritasFi presents a novel framework that greatly enhances the adaptability and robustness of financial RAG systems, providing a scalable solution for both general-domain and company-specific QA tasks. Code accompanying this work is available at https://github.com/simplew4y/VeritasFi.git.
The rapid development and widespread adoption of open source software, facilitated and accelerated by the web, have fostered a vibrant ecosystem for collaborative development and innovation. GitHub, a leading platform for collaborative software development, currently hosts more than 100 million registered users, creating a substantial ecosystem for examining open source community behaviors. Existing tools for measuring open source communities primarily focus on metrics such as issue response time, pull request response time, or incremental stars to provide insights into community activity. However, these tools are limited in their ability to assess the influence of communities from the perspective of collaboration networks. Moreover, current data collection solutions offer fixed functionalities and lack the flexibility to support multi-source, fine-grained, and customizable data acquisition, which is essential for comprehensive analysis of Open Source Ecosystems (OSEs). In this paper, we present OpenDigger, a framework for multi-dimensional assessment of collaboration activities in OSEs. To enable scalable, modular, and continuous acquisition of OSE data, we developed OpenCrawler, a one-line service providing customizable, fine-grained control over data collection. Using the collected data, OpenDigger computes 20 statistical and 2 network-based metrics, and our empirical analysis further verifies their effectiveness in enabling a comprehensive assessment of trends in OSEs. By continuously collecting logs from GitHub and Gitee, OpenDigger has now accumulated over 9 billion records. Our framework has already been deployed across multiple industrial environments, including Alibaba Group, Ant Group, Apache Foundation, and Mulan Open Source Community.
In real-world industrial scenarios, large language models (LLMs) require Continuous Learning (CL) to adapt to diverse tasks as opera- tional requirements diversify, demanding self-evolution capabilities to autonomously refine their knowledge and adapt to dynamic envi- ronments. However, existing CL approaches, such as replay-based and parameter isolation techniques, struggle with the catastrophic forgetting problem: new task training degrades performance on prior tasks due to the model's adaptation to new data distributions, which weakens its generalization to old tasks. To address this issue, we propose a novel parameter-efficient adversarial MoE framework, MoE-CL, for industrial-scale self-evolving continual instruction tuning of LLMs. Specifically, MoE-CL employs a dual-expert archi- tecture to enable self-evolution: a dedicated LoRA expert for each task to preserve task-specific knowledge, ensuring parameter inde- pendence and mitigating forgetting, and a shared LoRA expert to facilitate cross-task knowledge transfer. Specifically, a task-aware discriminator within a Generative Adversarial Network (GAN) is integrated into the shared expert to suppress task-irrelevant noise, ensuring only task-aligned knowledge is transferred during se- quential task training. Through adversarial training, the shared ex- pert learns generalized representations that mimic the task-aware discriminator, while dedicated experts retain task-specific details, balancing knowledge retention and cross-task generalization—key to the model's self-evolution by autonomously optimizing knowl- edge integration across tasks. Extensive experiments on a public MTL5 benchmark and an industrial Tencent3 benchmark validate MoE-CL's effectiveness in self-evolving continual learning. In real- world A/B testing on content compliance review in the Tencent Video Platform, MoE-CL reduced manual review costs by 15.3%,demonstrating its applicability for large-scale industrial deploy- ment where self-evolution is critical for adapting to evolving op- erational demands. Implementation code is publicly available at https://github.com/BAI-LAB/MoE-CL.
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.
In the rapidly evolving landscape of e-commerce, visual search has become a cornerstone of user experience, enabling customers to find products using images rather than traditional text queries. However, a comprehensive analysis reveals a persistent challenge: nearly half of retrieval failures stem from systems that prioritize superficial visual similarity over semantic relevance, resulting in frustrating user experiences where searches return visually similar but functionally different products. This limitation becomes particularly acute in Business-to-Business environments, where incorrect product recommendations can have significant operational and safety implications. In this paper, we propose a novel solution, Adaptive Retrieval with Category-aware Hierarchical sEmantic Refinement (ARCHER), which presents a novel multimodal retrieval framework that addresses these challenges through progressive semantic alignment. Unlike existing approaches that treat all visual similarities equally, ARCHER employs a sophisticated three-stage learning strategy that systematically builds from coarse-grained category understanding to fine-grained product discrimination. The framework begins with Proto-Align Enhancement to establish foundational visual-textual correspondences, progresses through Cross-View Learning to develop robust viewpoint-invariant representations, and culminates with Margin-based Representation Enhancement that learns to distinguish between visually similar but functionally distinct products. Most significantly, the framework has been successfully deployed on Alibaba.com's B2B platform since December 2024, where it serves millions of daily queries and has achieved a measurable 2.1% improvement in click-through rates.
Efficiently selecting relevant content from vast candidate pools is a critical challenge in modern recommender systems. Traditional methods, such as item-to-item collaborative filtering (CF) and two-tower models, often fall short in capturing the complex user-item interactions due to uniform truncation strategies and overdue user-item crossing. To address these limitations, we propose Personalized Item-to-Item (PI2I), a novel two-stage retrieval framework that enhances the personalization capabilities of CF. In the first Indexer Building Stage (IBS), we optimize the retrieval pool by relaxing truncation thresholds to maximize Hit Rate, thereby temporarily retaining more items users might be interested in. In the second Personalized Retrieval Stage (PRS), we introduce an interactive scoring model to overcome the limitations of inner product calculations, allowing for richer modeling of intricate user-item interactions. Additionally, we construct negative samples based on the trigger-target (item-to-item) relationship, ensuring consistency between offline training and online inference. Offline experiments on large-scale real-world datasets demonstrate that PI2I outperforms traditional CF methods and rivals Two-Tower models. Deployed in the ''Guess You Like'' section on Taobao, PI2I achieved a 1.05% increase in online transaction rates. In addition, we have released a large-scale recommendation dataset collected from Taobao, containing 130 million real-world user interactions used in the experiments of this paper. The dataset is publicly available at https://huggingface.co/datasets/PI2I/PI2I, which could serve as a valuable benchmark for the research community.
Large Language Models (LLMs) enable Web-scale multilingual content analysis but face critical challenges in scaling to long-tail languages and ensuring robustness. Current research is split between two isolated trajectories: a Macro-Paradigm (system-level engineering) and a Micro-Paradigm (internal model intervention). We argue that a true Web-scale solution requires their systematic fusion, balancing large-scale data processing with fine-grained model control. We introduce the Control-Tower Framework (CTF), a novel methodology designed to systematically enhance powerful, pre-trained base models. Inspired by control-theoretic ideas, CTF transforms a base model into a controllable analysis engine via three synergistic stages: (1) Micro-enhanced pre-training that injects linguistic priors (e.g., syntax) to build a robust semantic foundation; (2) a control-inspired fine-tuning stage where a heuristic dynamic feedback loop, driven by micro-level error signals (e.g., knowledge editing loss), actively adjusts the macro-scale learning curriculum; and (3) Macro-optimized inference using Minimum Bayes Risk (MBR) decoding to enhance robustness on noisy user-generated content (UGC). Extensive experiments show that CTF surpasses the leading open-weights model, Tower+ 9B FT, by a substantial margin of +2.18 XCOMET-XXL on low-resource languages (WMT24++). Crucially, CTF unlocks large-scale cross-lingual Web mining by converting unstructured Web text into machine-analyzable assets. We evidence this with substantial gains across both document-level (on MARC) and aspect-based (on SemEval-2016) sentiment analysis tasks. Our work offers a practical pathway toward building more reliable, scalable, and controllable global information ecosystems.
Online Time Series Forecasting (OTSF) requires models to continuously adapt to concept drift. However, existing methods often treat concept drift as a monolithic phenomenon. To address this limitation, we first redefine concept drift by categorizing it into two distinct types: Recurring Drift, where previously seen patterns reappear, and Emergent Drift, where entirely new patterns emerge. We then propose DynaME (Dynamic Multi-period Experts), a novel hybrid framework designed to effectively address this dual nature of drift. For Recurring Drift, DynaME employs a committee of specialized experts that are dynamically fitted to the most relevant historical periodic patterns at each time step. For Emergent Drift, the framework detects high-uncertainty scenarios and shifts reliance to a stable, general expert. Extensive experiments on several benchmark datasets and backbones demonstrate that DynaME effectively adapts to both concept drifts and significantly outperforms existing baselines. Our source code is available at https://github.com/shhong97/DynaME.
Understanding how media rhetoric shapes audience engagement is crucial in the attention economy. This study examines how moral-emotional framing by mainstream news channels on YouTube influences user behavior across Korea and the United States. To capture the platform's multimodal nature, combining thumbnail images and video titles, we develop a multimodal moral emotion classifier by fine-tuning a vision–language model. The model is trained on human-annotated multimodal datasets in both languages and applied to approximately 400,000 videos from major news outlets. We analyze three engagement levels (views, likes, and comments), representing increasing degrees of commitment. The results show that other-condemning rhetoric—expressions of moral outrage that criticize others' morality—consistently increases all forms of engagement across cultures, with effect sizes strengthening from passive viewing to active commenting. These findings suggest that moral outrage is a particularly effective emotional strategy, attracting not only attention but also active participation. We discuss concerns about the potential misuse of other-condemning rhetoric, as such practices may deepen polarization by reinforcing in-group/out-group divisions. To facilitate future research and ensure reproducibility, we publicly release our Korean and English multimodal moral emotion classifiers.
With the rapid advancement of large language models (LLMs), a diverse ecosystem of models with different scales and domain specializations has emerged, including LLM-based web agents and online multi-LLM server systems. LLM routing, which opportunistically leverages this diversity to balance response quality and computational cost, has become a central problem in optimizing the performance of LLM serving systems. We propose the Budget-Adaptive Router (BARouter), a budget-adaptive online routing framework for LLM serving systems. BARouter dynamically adjusts its routing policy for incoming queries based on the estimated response quality, query costs, and real-time budget consumption, enabling seamless adaptation to varying initial budgets and evolving input distributions without manual hyperparameter tuning. Theoretically, we prove that BARouter achieves sublinear regret over the time horizon T. The extensive experiments show that BARouter effectively allocates the right budget to the queries at the right time, consistently outperforming baseline algorithms, and remains robust to varying budget levels and shifting query distributions.
With the rapid advancement of large language models (LLMs), producing realistic fake news has become increasingly effortless, challenging existing detection methods that rely on lexical and syntactic patterns. To address this, we shift our focus to the generation process and analyze how malicious prompts manipulate model outputs. We construct pairs of LLM-generated real and fake news and apply malicious prompts to reconstruct them as fake. By comparing the original-token generation probabilities recorded during reconstruction, we observe a consistent statistical divergence: tokens from real news tend to have lower reconstruction likelihoods than those from fake news. We define this distributional divergence as linguistic fingerprint. Building on this insight, we propose LIFE (Linguistic Fingerprints Extraction), a novel detection framework that reconstructs token-level probability distributions guided by malicious prompts to capture these discriminative linguistic patterns. To fully exploit the extracted fingerprints, LIFE further introduces a key-fragment amplification module that adaptively identifies and accentuates the most distinctive linguistic fragments, thereby enhancing detection reliability across diverse prompting scenarios. Extensive experiments demonstrate that LIFE achieves state-of-the-art performance in detecting LLM-generated fake news while maintaining strong generalization to human-LLM mixed cases. The code is available.
The rise of micro-videos has reshaped how misinformation spreads, amplifying its speed, reach, and impact on public trust. Existing benchmarks typically focus on a single deception type, overlooking the diversity of real-world cases that involve multimodal manipulation, AI-generated content, cognitive bias, and out-of-context reuse. Meanwhile, most detection models lack fine-grained attribution, limiting interpretability and practical utility. To address these gaps, we introduce WildFakeBench, a large-scale benchmark of over 10,000 real-world micro-videos covering diverse misinformation types and sources, each annotated with expert-defined attribution labels. Building on this foundation, we develop FakeAgent, a Delphi-inspired multi-agent reasoning framework that integrates multimodal understanding with external evidence for attribution-grounded analysis. FakeAgent jointly analyzes content and retrieved evidence to identify manipulation, recognize cognitive and AI-generated patterns, and detect out-of-context misinformation. Extensive experiments show that FakeAgent consistently outperforms existing MLLMs across all misinformation types, while WildFakeBench provides a realistic and challenging testbed for advancing explainable micro-video misinformation detection.. Data and code are available at: https://github.com/Aiyistan/FakeAgent.