AI-generated text detection is commonly framed as a binary document-level judgment about whether a text is human-written or machine-generated. This framing breaks down for mixed-origin writing, where content origin and expression origin may differ. We cast mixed-origin detection as dimension-to-composition source attribution, inferring content origin and expression origin before composing them into four collaboration types. We propose *Dimension-to-Composition Routing* (**D2C-Routing**), which routes content-side and expression-side evidence to supervised dimension heads before a learned gated composition layer predicts the final label. On MixD2C, a reconstructed split derived from the HART mixed-origin benchmark, our disclosed D2C-Routing-based detector system reaches 0.8603 four-way Avg TPR@1%FPR, 6.5 points above the same-split RACE-local rerun. Core ablations support the routing design, while error analysis shows that distinguishing AI-content/human-expression from fully AI-generated text remains the hardest boundary. Code is available at https://github.com/bystander563/d2c-routing-artifact.

Code retrieval is becoming central to coding agents, but agentic coding requires more than matching a natural-language query to an isolated snippet. Given a user request, a coding agent needs to navigate a concrete repository state, locate relevant files and functions, gather supporting context, and filter similar in-repository distractors. Existing code retrieval benchmarks mainly evaluate docstring-to-function or snippet-level matching, thereby missing this requirement-driven repository search problem. To address this gap, we introduce CORE-Bench, a comprehensive benchmark for code retrieval in the era of agentic coding. CORE-Bench evaluates code retrieval ability at three levels: code understanding, issue-to-edit localization, and broader context retrieval. Built from curated code-search tasks and SWE-bench-series instances, CORE-Bench contains over 180K queries and 106K broader-context relevance labels. Experiments with representative embedding models show a sharp drop from traditional code search to code retrieval in agentic coding settings. Simple supervised fine-tuning of existing embedding models significantly improves performance in this setting, suggesting substantial room for further progress.

Rubric generation improves the interpretability and actionability of large language model evaluation, but in online-to-offline (O2O) local-life AI search, user queries often reflect more complex needs, such as consumption intent, location constraints, and scenario preferences. Directly generating rubrics from a single query tends to produce generic criteria that fail to satisfy AI search evaluation needs, while leveraging candidate responses, preference samples, or external evidence at scale remains challenging. To address these limitations, we propose Category-Aware Rubric Generation (CaRG), a category-aware evaluation dimension candidate pool framework. Through embedding-based clustering, representative selection, and iterative LLM-based consolidation, CaRG constructs reusable candidate pools of both general and category-specific evaluation dimensions from representative historical queries. Subsequently, these candidate pools are leveraged to generate query-level rubrics with concrete positive and negative scoring criteria for target queries. Experimental results demonstrate that CaRG consistently outperforms Direct-Rubric and shows more stable overall quality than existing query-only baselines, while achieving the strongest overall agreement with production golden rubric rankings. Our repository is available at https://github.com/PlussLe/CaRG.

Knowledge Graph Embedding (KGE) aims to project entities and relations into a low-dimensional space, so as to enable Knowledge Graphs (KGs) to be effectively used by downstream AI tasks. Most existing KGs (e.g. Wikidata) suffer from the data imbalance issue, i.e., the occurrence frequencies vary significantly among different entities. Current KGE models use a fixed embedding size, leading to overfitting for low-frequency entities and underfitting for high-frequency ones. A simple method is to manually set embedding sizes based on frequency, but this is not feasible due to the complexity and the large number of entities. To this end, we propose CustomizE, which customizes embedding sizes in a data-driven way, assigning larger sizes for high-frequency entities and smaller sizes for low-frequency ones. We use bilevel optimization for stable learning of representations and sizes. It is noteworthy that our framework is universal and flexible, which is suitable for various KGE models. Experiments on link prediction tasks show its superiority over state-of-the-art baselines.

Generative Retrieval (GR) introduces a new information retrieval paradigm that directly generates unique document identifiers (DocIDs). The key challenge of GR lies in creating effective yet discrete DocIDs that preserve semantic relevance for similar documents while differentiating dissimilar ones. However, existing methods generate DocIDs solely based on the textual content of documents, which may result in DocIDs with weak semantic connections for similar documents due to variations in expression. Therefore, we propose using queries as a bridge to connect documents with varying relevance levels for learning improved DocIDs. In this paper, we propose Multi-lEvel Relevance document identifier learning for Generative rEtrieval (MERGE), a novel approach that utilizes multi-level document relevance to learn high-quality DocIDs. MERGE incorporates three modules: a multi-relevance query-document alignment module to effectively align document representations with related queries, an outer-level contrastive learning module to capture binary-level relevance, and an inner-level multi-level relevance learning module to distinguish documents with different relevance levels. Our approach encodes rich hierarchical semantic information and maintains uniqueness across documents. Experimental results on real-world multilingual e-commerce search datasets demonstrate that MERGE significantly outperforms existing methods, underscoring its effectiveness. The source code is available at https://github.com/zhangfw123/MERGE.

Food delivery search aims to quickly retrieve deliverable items that meet users’ needs, typically requiring faster and more accurate query understanding compared to traditional e-commerce search. Generative retrieval (GR), an emerging search paradigm, harnesses the advanced query understanding capabilities of large language models (LLMs) to enhance the retrieval of results for complex and long-tail queries in food delivery search scenarios. However, there are still challenges in deploying GR to online scenarios: 1) the large scale of items; 2) latency constraints unmet by LLM inference in online retrieval; and 3) strong location-based service restrictions on generated items. To explore the application of GR in food delivery search, we optimize both offline training and online deployment, proposing Hierarchical semantic representation enhancement for Generative Retrieval (HierGR). Specifically, for the generation of semantic IDs, we propose an optimization method that refines the residual quantization process to generate hierarchically semantic IDs for items. Additionally, to successfully deploy on a well-known food delivery platform, we utilize the query cache mechanism and integrate the GR model with the online dense retrieval model to fulfill real-world search requirements. Online A/B testing results show that our proposed method increases the number of online orders by 0.68% for complex search intents. The source code is available at https://github.com/zhangfw123/HierGR.