Accurate estimation of delivery time (EDT) is a critical factor in web e-commerce user experience. The pursuit of higher EDT accuracy has predominantly centered on designing increasingly complex model architectures. While valuable, this architecture-centric paradigm creates a tension between its high iteration costs and the industrial demand for agile deployment. This work, therefore, explores a complementary dimension: enhancing model performance by optimizing the learning process itself. We propose EDTF, a novel, plug-and-play composite learning framework that empowers existing models by augmenting their learning objective. EDTF first transforms the traditional regression problem into a structured ordinal classification task to address the training difficulties inherent in direct regression and preserve temporal order. It then introduces a cross-view consistency paradigm, decomposing the prediction task into two related views: the macroscopic end-to-end delivery time and the microscopic next-hop duration. By enforcing a self-supervised signal that aligns the sum of future next-hop durations with the overall EDT, our framework enables models to learn more robust temporal representations without extra features. Extensive experiments on a large-scale industrial dataset show that EDTF, as a plugin, consistently enhances performance and accelerates convergence across five diverse architectures. Critically, an EDTF-optimized model has been successfully deployed in a live production environment, demonstrating significant improvements over its predecessor. This work thus presents a validated and valuable new paradigm for the economical and efficient application of web services reliant on trajectory-based forecasting, from e-commerce to ride-hailing and food delivery.
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We consider the problem of graph sparsification while preserving the test accuracy of Graph Neural Networks (GNNs). Prior work in this area is often motivated by the Lottery Ticket Hypothesis, which aims to prune redundant edges. However, these sparsification approaches typically operate as black boxes and provide no justification for which edges are removed. In contrast, edge importance scores obtained from GNN explanation methods provide a principled and interpretable basis for sparsification. In particular, we show that Shapley value–based explainers such as GNNShap enable effective sparsification, allowing up to 80% of edges to be removed without degrading model accuracy. We show that Shapley values are well-suited for this task due to their robustness in identifying less influential edges, resulting in sparse yet faithful subgraphs that are efficient for downstream applications.
With the evolving generative models, generated images are closer to reality, raising concerns about information authenticity and malicious misuse. Invisible watermarks offer a practical approach to detecting and tracing them. However, while image watermarking inevitably introduces quality degradation, most existing methods primarily focus on improving watermark robustness. To address this limitation, we propose AWMA-MoE, a framework that enhances the quality of generated images while preserving strong watermark robustness. Specifically, we design an attention-based adapter that adaptively embeds watermarks with spatially varying strengths across image regions. Building upon this, we introduce an MoE architecture that leverages diverse experts to further improve image quality while retaining watermark robustness. Experiments demonstrate that AWMA-MoE can reduce the distortion of generated images and exhibit competitive watermark performance, thus striking an improved balance for watermarking generated image tasks and better linking post-hoc and in-generation methods.
As pretrained models are increasingly shared on the web, ensuring that models can forget or delete sensitive, copyrighted, or private information upon request has become crucial. Machine unlearning has been proposed to address this challenge. However, current evaluations for unlearning methods rely on output-based metrics, which cannot verify whether information is completely deleted or merely suppressed at the representation level, where suppression is insufficient for true unlearning. To address this gap, we propose a novel restoration-based analysis framework that uses Sparse Autoencoders to identify class-specific expert features in intermediate layers and applies inference-time steering to quantitatively distinguish between suppression and deletion. Applying our framework to 12 major unlearning methods in image classification tasks, we find that most methods achieve high restoration rates of unlearned information, indicating that they only suppress information at the decision-boundary level, while preserving semantic features in intermediate representations. Notably, even retraining from pretrained checkpoints shows high restoration, revealing that robust semantic features inherited from pretraining are not removed by retraining. These results demonstrate that representation-level retention poses significant risks overlooked by output-based metrics, highlighting the need for new unlearning evaluation criteria. We propose new evaluation guidelines that prioritize representation-level verification, especially for privacy-critical applications in the era of pre-trained models.
Personalized search ranking systems are critical for driving engagement and revenue in modern e-commerce platforms. Existing methods primarily model users' broad interests from historical behaviors but often fail to explicitly align these with real-time intent expressed in user queries. In this paper, we propose DiffusionGS, a scalable generative framework that treats user queries as explicit intent anchors to extract user interests from long-term, noisy behavior histories. Specifically, we formulate interest extraction as a conditional denoising task, where the user's query guides a conditional diffusion process to produce a robust, user intent-aware representation from their behavioral sequence. A User-aware Denoising Layer (UDL) further refines attention distribution using user-specific profiles. By reframing queries as intent priors and leveraging diffusion-based denoising, our method provides a powerful mechanism for capturing dynamic user interest shifts. Extensive offline and online experiments demonstrate the superiority of DiffusionGS over state-of-the-art methods.
Graph retrieval-augmented generation (GRAG) places high demands on graph-specific retrievers. However, existing retrievers often rely on language models pretrained on plain text, limiting their effectiveness due to domain misalignment and structure ignorance. To address these challenges, we propose GPR, a graph-based retriever pretrained directly on knowledge graphs. GPR aligns natural language questions with relevant subgraphs through LLM-guided graph augmentation and employs a structure-aware objective to learn fine-grained retrieval strategies. Experiments on two datasets, three LLM backbones, and five baselines show that GPR consistently improves both retrieval quality and downstream generation, demonstrating its effectiveness as a robust retrieval solution for GRAG.
Balancing multiple objectives is critical for user satisfaction in modern recommender and search systems, yet current Multi-Task Fusion (MTF) methods rely on static, manually-tuned weights that fail to capture individual user intent. While Reinforcement Learning (RL) offers a path to personalization, traditional approaches often falter due to training instability and the sparse rewards inherent in these large-scale systems. To address these limitations, we propose Group-Relative Reinforcement Learning with Adaptive Dirichlet Exploration (GRADE), a novel and robust framework for personalized multi-task fusion. GRADE leverages a critic-free, Group Relative Policy Optimization (GRPO) paradigm, enabling stable and efficient policy learning by evaluating the relative performance of candidate weight groups. Its core innovations include employing the Dirichlet distribution for principled and structured exploration of the weight space, and a composite reward function that combines sparse user feedback with dense model priors and rule-based constraints to guide the search effectively. Deployed in the in-app marketplace of an application with over hundreds of millions daily active users, GRADE significantly outperforms established baselines, achieving substantial gains in rigorous large-scale A/B tests: +0.595% in CTR, +1.193% in CVR, +1.788% in OPM, and +1.568% in total order volume. Following its strong performance, GRADE has been fully deployed in the marketplace search scenario of Kuaishou, serving hundreds of millions of users.
Effective relevance modeling is crucial for e-commerce search, as it aligns search results with user intent and enhances customer experience. Recent work has leveraged large language models (LLMs) to address the limitations of traditional relevance models, particularly their inability to handle long-tail and ambiguous queries. By incorporating Chain-of-Thought (CoT) reasoning, these approaches further improve both accuracy and interpretability through explicit, multi-step reasoning pathways. However, two key limitations remain: (1) most existing approaches rely on single-perspective CoT reasoning, which fails to capture the multifaceted nature of e-commerce relevance (e.g., user intent vs. attribute-level matching vs. business-specific rules); and (2) although CoT-enhanced LLMs offer rich reasoning capabilities, their high inference latency necessitates knowledge distillation for real-time deployment, yet current distillation methods discard the CoT rationale structure at inference, using it only as a transient auxiliary signal and thereby forfeiting its reasoning utility for online serving. To address these challenges, we propose a novel framework that better exploits CoT semantics throughout the optimization pipeline. Specifically, the teacher model leverages Multi-Perspective CoT (MPCoT) to generate diverse rationales and combines Supervised Fine-Tuning (SFT) with Direct Preference Optimization (DPO) to construct a more robust reasoner. For distillation, we introduce Latent Reasoning Knowledge Distillation (LRKD), which endows a student model with a lightweight inference-time latent reasoning extractor, allowing efficient and low-latency internalization of the LLM's sophisticated reasoning capabilities. Evaluated through offline experiments and online A/B tests on an e-commerce search advertising platform serving tens of millions of users daily, our method delivers significant offline gains, along with a 1.42% online improvement in Revenue Per Mille (RPM) and a 0.4% increase in relevance satisfaction score (RS), demonstrating clear benefits in both commercial performance and user experience.
Online financial systems for stock prediction, portfolio optimization, and algorithmic trading must remain robust against rare and volatile market events, but historical data often fails to capture diverse unprecedented financial risks, creating a major bottleneck for systematic stress testing. To address this, we propose Financial Wind Tunnel (FWT), a deployable, retrieval-augmented market simulator that generates realistic, controllable, and adaptable financial dynamics for industrial-scale training and testing. FWT integrates both macro- and micro-level market patterns through a retrieval mechanism that conditions diffusion on relevant trends, supporting real-time and interpretable generation. Unlike existing methods that lack transferability across markets and scales, FWT supports large-scale cross-market pattern synthesis and what-if causal generation, enabling generalizable simulation beyond local historical regimes. We further introduce a simulation-driven optimizer for downstream models, enabling continuous improvement of online quantitative strategies. Deployed in production pipelines on CSI300 and HKSE components, FWT demonstrates measurable gains in stock prediction and portfolio optimization performance while maintaining scalability and operational reliability, offering a practical system for quantitative finance and stress testing.
Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, where semantic understanding and complex reasoning directly shape the user experience and exert an indirect, yet substantial impact on business conversion. Large Language Models (LLMs) enable generative, reasoning-based approaches, typically aligned via supervised fine-tuning (SFT) or preference optimization methods like Direct Preference Optimization (DPO). However, the increasing complexity of business rules and user queries exposes the inability of existing methods to endow models with robust reasoning capacity for long-tail and challenging cases. Efforts to address this via reinforcement learning strategies like Group Relative Policy Optimization (GRPO) often suffer from sparse terminal rewards, offering insufficient guidance for multi-step reasoning, which in turn slows convergence. To address these challenges, we propose TaoSR-AGRL, an Adaptive Guided Reinforcement Learning framework for LLM-based relevance prediction in Taobao Search Relevance. TaoSR-AGRL introduces two key innovations: (1) Rule-aware Reward Shaping, which decomposes the final relevance judgment into dense, structured rewards aligned with domain-specific relevance criteria; and (2) Adaptive Guided Replay, which identifies low-accuracy rollouts during training and injects targeted ground-truth guidance to steer the policy away from stagnant, rule-violating reasoning patterns toward compliant trajectories. TaoSR-AGRL was evaluated on large-scale datasets and through online evaluations on Taobao Search. It consistently outperforms DPO and GRPO baselines, improving relevance accuracy and rule adherence, with measurable gains in user engagement and stable training dynamics. The model has been deployed on Taobao, serving hundreds of millions of users.
A practical log anomaly detection system for large-scale web services, suitable for real industrial deployment, should exhibit three key characteristics: fidelity to decisive lexical cues, human-auditable explanations, and low inference latency. Existing approaches often compromise one or more of these requirements by discarding crucial tokens during parsing, obscuring decisions through dense embeddings, or incurring heavy computational costs. We introduce Smart Eye, a deployable two-stage key pattern retrieval framework that employs large language models (LLMs) strictly as proposal engines, coupled with a deterministic verifier. In Stage I, the system enhances recall of anomalous logs under a configurable false positive budget using budgeted maximum coverage selection. In Stage II, precision is monotonically improved by refining parent patterns into specialized child patterns and introducing targeted exclusions. These essential patterns, extracted by LLMs and then confirmed, will be formulated into regular expressions to enable efficient online matching of anomalous logs. We provide both theoretical justification and a practical implementation that runs efficiently on commodity CPUs. When evaluated on three real world Huawei Cloud Core log datasets, Smart Eye achieves state-of-the-art anomaly detection with a average F1 score of 0.9877, outperforming the strongest neural baseline. Moreover, it produces human interpretable evidence chains and supports low latency inference. These results demonstrate that combining LLM-guided proposal generation with deterministic verification forms a robust and deployable alternative to traditional end to end embedding pipelines for industrial log analysis.
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.
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.
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.
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.
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.
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.
As a key to accessing research impact, citation dynamics underpins research evaluation, scholarly recommendation, and the study of knowledge diffusion. Citation prediction is particularly critical for newborn papers, where early assessment must be performed without citation signals and under highly long-tailed distributions. We identify two key research gaps: (i) insufficient modeling of implicit factors of scientific impact, leading to reliance on coarse proxies; and (ii) a lack of bias-aware learning that can deliver stable predictions on lowly cited papers. We address these gaps by proposing a Bias-Aware Citation Prediction Framework, which combines multi-agent feature extraction with robust graph representation learning. First, a multi-agent × graph co-learning module derives fine-grained, interpretable signals, such as reproducibility, collaboration network, and text quality, from metadata and external resources, and fuses them with heterogeneous-network embeddings to provide rich supervision even in the absence of early citation signals. Second, we incorporate a set of robust mechanisms: a two-stage forward process that routes explicit factors through an intermediate exposure estimate, GroupDRO to optimize worst-case group risk across environments, and a regularization head that performs what-if analyses on controllable factors under monotonicity and smoothness constraints. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of our proposed model. Specifically, our model achieves around a 13% reduction in error metrics (MALE and RMSLE) and a notable 5.5% improvement in the ranking metric (NDCG) over the baseline methods.
Real-world multimodal knowledge graphs (MKGs) are inherently heterogeneous, modeling entities that are associated with diverse modalities. Traditional knowledge graph embedding (KGE) methods excel at learning continuous representations of entities and relations, yet they are typically designed for unimodal settings. Recent approaches extend KGE to multimodal settings but remain constrained, often processing modalities in isolation, resulting in weak cross-modal alignment, and relying on simplistic assumptions such as uniform modality availability across entities. Vision--Language Models (VLMs) offer a powerful way to align diverse modalities within a shared embedding space. We propose Vision--Language Knowledge Graph Embeddings (VL-KGE), a framework that integrates cross-modal alignment from VLMs with structured relational modeling to learn unified multimodal representations of knowledge graphs. Experiments on WN9-IMG and two novel fine art MKGs, WikiArt-MKG-v1 and WikiArt-MKG-v2, demonstrate that VL-KGE consistently improves over traditional unimodal and multimodal KGE methods in link prediction tasks. Our results highlight the value of VLMs for multimodal KGE, enabling more robust and structured reasoning over large-scale heterogeneous knowledge graphs.
Federated learning offers a promising paradigm for privacy-preserving traffic prediction, yet its performance is often challenged by the non-identically and independently distributed (non-IID) nature of decentralized traffic data. Existing federated methods frequently struggle with this data heterogeneity, typically entangling globally shared patterns with client-specific local dynamics within a single representation. In this work, we postulate that this heterogeneity stems from the entanglement of two distinct generative sources: client-specific localized dynamics and cross-client global spatial-temporal patterns. Motivated by this perspective, we introduce FedDis, a novel framework that, to the best of our knowledge, is the first to leverage causal disentanglement for federated spatial-temporal prediction. Architecturally, FedDis comprises a dual-branch design wherein a Personalized Bank learns to capture client-specific factors, while a Global Pattern Bank distills common knowledge. This separation enables robust cross-client knowledge transfer while preserving high adaptability to unique local environments. Crucially, a mutual information minimization objective is employed to enforce informational orthogonality between the two branches, thereby ensuring effective disentanglement. Comprehensive experiments conducted on four real-world benchmark datasets demonstrate that FedDis consistently achieves state-of-the-art performance, promising efficiency, and superior expandability. To ease the reproducibility, we release our implementation code online. https://github.com/Jlu-zcy/www2026_FedDis.