Referring single-object tracking enables language-grounded target initialization and subsequent tracking by jointly leveraging semantic cues and visual templates. The core difficulty is to use language differently across stages: it is indispensable for grounding but can induce semantic drift during tracking when overemphasized. Meanwhile, current methods often require costly vision-language alignment training. We present LVTrack, a pure transformer framework that introduces a mode-conditioned Gated Feature Injector to adaptively regulate textual guidance and alleviate semantic drift. Together with targeted adaptations, it directly harnesses a frozen vision-language pretrained model, greatly reducing training cost and preserving strong language understanding. To further improve temporal localization, LVTrack integrates hybrid relative-absolute positional encodings with a lightweight memory mechanism and optimizes autoregressive box prediction using a Gaussian-smoothed KL loss. Extensive experiments on standard benchmarks demonstrate that LVTrack achieves strong performance.

In specialized domains such as space science and utilization, question answering (QA) systems are required to perform complex multi-fact reasoning over sparse knowledge graphs (KGs). Existing KG-based retrieval-augmented generation (RAG) frameworks often face challenges such as inefficient subgraph retrieval, limited reasoning capabilities, and high computational costs. These issues limit their effectiveness in specialized domains. In this paper, we propose SKRAG, a novel Skeleton-guided RAG framework for knowledge graph question answering (KGQA). SKRAG leverages a lightweight language model enhanced with the Finite State Machine (FSM) constraint to produce structurally grounded reasoning skeletons, which guide accurate subgraph retrieval. The retrieved subgraph is then used to prompt a general large language model (LLM) for answer generation. We also introduce SSUQA, a KGQA dataset in the space science and utilization domain. Experiments show that SKRAG outperforms strong baselines on SSUQA and two general-domain benchmarks, demonstrating its adaptability and practical effectiveness.

This paper explores computational approaches for detecting parallelism in classical Chinese poetry, a rhetorical device where two verses mirror each other in syntax, meaning, tone, and rhythm. We experiment with five classification methods: (1) verb position matching, (2) integrated semantic, syntactic, and word-segmentation analysis, (3) difference-based character embeddings, (4) structured examples (inner/outer couplets), and (5) GPT-guided classification. We use a manually annotated dataset, containing 6,125 pentasyllabic couplets, to evaluate performance. The results indicate that parallelism detection poses a significant challenge even for powerful LLMs such as GPT-4o, with the highest F1 score below 0.72. Nevertheless, each method contributes valuable insights into the art of parallelism in Chinese poetry, suggesting a new understanding of parallelism as a verbal expression of principal components in a culturally defined vector space.