In the era of large language models (LLMs), supervised neural methods remain the state-of-the-art (SOTA) for Coreference Resolution. Yet, their full potential is underexplored, particularly in incremental clustering, which faces the critical challenge of balancing efficiency with performance for long texts. To address the limitation, we propose MEIC-DT, a novel dual-threshold, memory-efficient incremental clustering approach based on a lightweight Transformer. MEIC-DT features a dual-threshold constraint mechanism designed to precisely control the Transformer’s input scale within a predefined memory budget. This mechanism incorporates two key components: a Statistics-Aware Eviction Strategy (SAES) and an Internal Regularization Policy (IRP). The SAES utilizes distinct statistical profiles from the training and inference phases for intelligent cache management. The IRP strategically condenses clusters by selecting the most representative mentions, thereby preserving semantic integrity. Extensive experiments on common benchmarks demonstrate that MEIC-DT achieves highly competitive coreference performance under stringent memory constraints.

While Large Language Models (LLMs) demonstrate remarkable capabilities in open-ended creative generation, they notably struggle with Format-Constrained Generation tasks—such as poetry and lyrics—where strict adherence to multidimensional structural constraints (i.e., format, phonetics, and rhyme) is prerequisite to aesthetic value. Existing paradigms predominantly rely on unreliable prompting or rigid constrained decoding strategies; the former often fails to ensure compliance, while the latter compromises inference latency and disrupts the natural probability distribution, degrading generation quality. To bridge this gap, we establish CCP-Arena, a rigorous testbed for Chinese Classical Poetry, and proposeProgressive Structural Internalization (PSI) a novel framework designed to embed external constraints into the model’s intrinsic intuition. PSI initiates withStructural Scaffolding via Explicit Cognitive Planning, utilizing explicit template to provide a structural scaffold for subsequent generation. This is followed by a Cascaded Reinforcement Learning stage guided by a Holistic Reward Model, which optimizes for precise structural-semantic alignment. Extensive experiments demonstrate that PSI achieves state-of-the-art performance, surpassing baselines in both strict constraint adherence and literary aesthetics. Furthermore, mechanistic analysis confirms that our method effectively internalizes structural information into the model’s latent representations, offering a robust and efficient solution for constrained creative generation.

Coreference Resolution (CR) is a critical task in Natural Language Processing (NLP). Current research faces a key dilemma: whether to further explore the potential of supervised neural methods based on small language models, whose detect-then-cluster pipeline still delivers top performance, or embrace the powerful capabilities of Large Language Models (LLMs). However, effectively combining their strengths remains underexplored. To this end, we propose ImCoref-CeS, a novel framework that integrates an enhanced supervised model with LLM-based reasoning. First, we present an improved CR method (ImCoref) to push the performance boundaries of the supervised neural method by introducing a lightweight bridging module to enhance long-text encoding capability, devising a biaffine scorer to comprehensively capture positional information, and invoking a hybrid mention regularization to improve training efficiency. Importantly, we employ an LLM acting as a multi-role Checker-Splitter agent to validate candidate mentions (filtering out invalid ones) and coreference results (splitting erroneous clusters) predicted by ImCoref. Extensive experiments demonstrate the effectiveness of ImCoref-CeS, which achieves superior performance compared to existing state-of-the-art (SOTA) methods.

Knowledge Graph Completion (KGC), which aims to infer missing or incomplete facts, is a crucial task for KGs. However, integrating the vital structural information of KGs into Large Language Models (LLMs) and outputting predictions deterministically remains challenging. To address this, we propose a new method called GLTW, which encodes the structural information of KGs and merges it with LLMs to enhance KGC performance. Specifically, we introduce an improved Graph Transformer (iGT) that effectively encodes subgraphs with both local and global structural information and inherits the characteristics of language model, bypassing training from scratch. Also, we develop a subgraph-based multi-classification training objective, using all entities within KG as classification objects, to boost learning efficiency. Importantly, we combine iGT with an LLM that takes KG language prompts as input. Our extensive experiments on various KG datasets show that GLTW achieves significant performance gains compared to SOTA baselines.

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge. A critical yet underexplored challenge in RAG is document segmentation, also known as document chunking. Existing widely-used rule-based chunking methods usually lead to suboptimal splits, where overly large chunks introduce irrelevant information and small chunks lack semantic coherence. Existing semantic-based approaches either require costly LLM calls or fail to adaptively group contextually related sentences. To address these limitations, we propose PIC, Pseudo-Instruction for document Chunking), a simple yet effective method that leverages document summaries as pseudo-instructions to guide chunking. By computing semantic similarity between sentences and the summary, PIC dynamically groups sentences into chunks that align with the document’s key themes, ensuring semantic completeness and relevance to potential user instructions. Experiments on multiple open-domain question-answering benchmarks demonstrate that PIC can significantly improve retrieval accuracy (Hits@k) and end-to-end QA performance (Exact Match) without any additional training.