While Large Language Models (LLMs) excel in general question answering, they typically generate uniform responses, ignoring users’ diverse intents. Current Personalized Question Answering (PQA) methods face dual limitations: individual-based prompting struggles with data sparsity, while collaborative explicit prompting suffers from inaccurate user matching and attention dispersion caused by text concatenation. To tackle these challenges, we propose Collaborative Latent Prompting (CLP), a PQA framework that fuses collaborative knowledge within a dynamic latent space rather than relying on explicit textual concatenation. First, our Multi-Grained Collaborative Retrieval (MGCR) module ensures precise retrieval of relevant reference users through a three-stage refinement. Next, the Dynamic Q-Former (DQ-Former) utilizes question-modulated dynamic latent queries to extract compact personalized features from noisy texts. Finally, these features are projected into dense soft prompts to implicitly steer a frozen LLM. Experiments across three PQA domains and three backbone LLMs show consistent improvements over representative PQA baselines.

Personalized Question Answering (PQA) requires models to shift from a fact-centric to a user-centric paradigm, inferring implicit intents from historical interactions. However, standard Retrieval-Augmented Generation (RAG) struggles with narrative noise and fragmented semantics. Similarly, while Agent Memory was proposed to maintain long-term consistency, existing implementations predominantly focus on accumulating objective facts, neglecting the subjective attitudes essential for personalization. We argue that for PQA, while objective facts provide the necessary foundational context, relying solely on them is insufficient; they must be augmented by explicit modeling of subjective attitudes to capture a comprehensive user profile. To address this, we introduce Mem-ABC, an Attitude-Aware Agent Memory framework grounded in the Tripartite Model of Attitudes (Affect, Behavior, and Cognition). Mem-ABC employs a two-stage offline process: first autonomously refining raw history into general memories to eliminate noise, and subsequently reorganizing them into a structured attitude-aware memory. This structure enables the agent to explicitly retrieve context aligned with user psychology. Experiments on the LaMP-QA benchmark demonstrate that Mem-ABC consistently outperforms strong baselines across multiple domains.

Multimodal Named Entity Recognition relies on visual context to resolve textual ambiguities. To mitigate data scarcity, Data Augmentation (DA) has become a standard practice; however, existing methods predominantly adopt a one-size-fits-all and random perturbation paradigm, ignoring the internal state of the target model. In this paper, we first conduct a quantitative analysis, revealing that a significant portion of errors (over 30%) are model-specific, stemming from the unique biases of different architectures. To address this, we propose Memory-Guided Hard Data Augmentation, a framework designed to systematically repair these specific defects. First, we employ K-fold cross-validation to identify model-specific Hard Data. Second, we construct a Memory Tree and utilize Large Language Models (LLMs) with a clustering mechanism to induce macro-level error patterns from micro-level failures. This facilitates a paradigm shift from stateless instance-driven augmentation to a logical pattern-driven approach. Finally, we introduce an iterative augmentation mechanism that triggers recursive generation for stubborn instances that fail initial quality filters. Extensive experiments on Twitter-2015 and Twitter-2017 benchmarks demonstrate that our framework consistently yields significant performance gains across various MNER backbones.

Code agents are increasingly used in debugging workflows, where fault localization (FL) is often evaluated as endpoint retrieval: whether a model names the faulty code location. This view is inadequate for Linux kernel debugging, where failure logs may expose downstream symptoms while the erroneous state is introduced earlier and propagates through various dependences. We introduce KernelPath, a 200-instance Linux 5.10 function-level FL benchmark that pairs each root cause function with a source-grounded state propagation path and a dependence layer. KernelPath evaluates both endpoint localization and ordered diagnostic process recovery. Experiments with SOTA agents show that agents often retrieve plausible candidates but remain unreliable at recovering the diagnostic process, with performance degrading sharply under complex dependence relations. Further diagnostic probes show that agents can use explicit state-transition evidence, yet fail to infer how functions affect the erroneous state, drifting toward neighboring objectives such as repair-site selection, or up stream suspicious function retrieval. These findings reveal an unstable task model for kernel FL in current code agents.

Despite recent advances in Multimodal Relation Extraction (MRE), existing datasets and approaches primarily focus on single-image scenarios, overlooking the prevalent real-world cases where relationships are expressed through multiple images alongside text. To address this limitation, we present MRE-MI, a novel human-annotated dataset that includes both multi-image and single-image instances for relation extraction. Beyond dataset creation, we establish comprehensive baselines and propose a simple model named Global and Local Relevance-Modulated Attention Model (GLRA) to address the new challenges in multi-image scenarios. Our extensive experiments reveal that incorporating multiple images substantially improves relation extraction in multi-image scenarios. Furthermore, GLRA achieves state-of-the-art results on MRE-MI, demonstrating its effectiveness. The datasets and source code can be found at https://github.com/JinFish/MRE-MI.

The task of translating natural language questions into query languages has long been a central focus in semantic parsing. Recent advancements in Large Language Models (LLMs) have significantly accelerated progress in this field. However, existing studies typically focus on a single query language, resulting in methods with limited generalizability across different languages. In this paper, we formally define the Text-to-Query task paradigm, unifying semantic parsing tasks across various query languages. We identify query skeletons as a shared optimization target of Text-to-Query tasks, and propose a general dynamic data augmentation framework that explicitly diagnoses model-specific weaknesses in handling these skeletons to synthesize targeted training data. Experiments on four Text-to-Query benchmarks demonstrate that our method achieves state-of-the-art performance using only a small amount of synthesized data, highlighting the efficiency and generality of our approach and laying a solid foundation for unified research on Text-to-Query tasks. We release our code at https://github.com/jjjycaptain/Skeletron

Text-to-SQL is the task of translating natural language questions into SQL queries based on relational databases. Different databases implement their own SQL dialects, leading to variations in syntax. As a result, SQL queries designed for one database may not execute properly in another, creating a dialect gap. Existing Text-to-SQL research primarily focuses on specific database systems, limiting adaptability to different dialects. This paper proposes a novel adaptive framework called Dialect-SQL, which employs Object Relational Mapping (ORM) code as an intermediate language to bridge this gap. Given a question, we guide Large Language Models (LLMs) to first generate ORM code, which is then parsed into SQL queries targeted for specific databases. However, there is a lack of high-quality Text-to-Code datasets that enable LLMs to effectively generate ORM code. To address this issue, we propose a bootstrapping approach to synthesize ORM code, where verified ORM code is iteratively integrated into a demonstration pool that serves as in-context examples for ORM code generation. Our experiments demonstrate that Dialect-SQL significantly enhances dialect adaptability, outperforming traditional methods that generate SQL queries directly. Our code and data are released at https://github.com/jieshi10/orm-sql.