Zero-shot visual question answering (VQA) requires models to reason over unseen image–question pairs without task-specific supervision, where performance is often undermined by incomplete visual grounding and unstable reasoning. Recent approaches attempt to mitigate this by using large language models to generate external knowledge conditioned on captions and questions. However, these methods typically operate in an open-loop manner, lacking mechanisms to regulate competing interpretations or correct misaligned knowledge. Inspired by the hippocampal pattern completion mechanism, which supports inference from partial observations through memory reactivation and competitive stabilization, we propose ARK-PC for zero-shot VQA, which formulates reasoning as a two-stage completion process. It first expands fragmented multimodal cues into multiple structured candidate hypotheses, then adaptively reinforces coherent candidates while suppressing inconsistent ones through iterative feedback. By coupling knowledge generation with competitive refinement, ARK-PC transforms open-loop inference into a closed-loop stabilization process, enabling robust reasoning under uncertainty without external supervision. Experiments on OK-VQA and A-OKVQA demonstrate consistent state-of-the-art zero-shot performance and strong generalization across diverse backbones, indicating that the improvements stem from the proposed framework rather than model scale.

This paper introduces HQT-TI, a novel indexing method designed to improve the efficiency of spatial keyword queries. HQT-TI consists of two main components: a Hilbert QuadTree (HQT) based spatial index and a Trie-Inverted index (TI) combined textual index. HQT integrates the Hilbert curve with a Quadtree, establishing a direct relationship between the two. TI combines a trie and inverted index to minimize the intersection cost associated with long lists, thus improving the speed of keyword queries. The HQT based Spatial Query algorithm (HQT-SQ) reduces overlap checks and limits irrelevant object retrieval by employing query drill-down and depth first search with limited breadth expansion in spatial queries. Meanwhile, the Segment List Intersection based Keyword Query algorithm (SLI-KQ), built on TI, efficiently handles segment list intersections for keyword queries. The combination of HQT-SQ and SLI-KQ results in HS-SK, a highly efficient spatial keyword query algorithm. Extensive experimental results demonstrate that HS-SKQ outperforms SFC-Quad by up to two orders of magnitude, achieving up to a 5.46× speedup over the best existing competitors, making it a promising solution for large-scale spatial keyword query processing.

Set Similarity Query (SSQ) is widely applied in various fields. The existing B+-tree-based SSQ approaches fail to fully exploit length filtering and require calculating similarity bounds in a node-wise manner, leading to low efficiency. To address these issues, we propose LeB, a novel length-enhanced B+-tree index, whose keys integrate set lengths and bucket mapping, enabling the direct pruning of sets that do not meet the length requirements. Building upon LeB, we present an efficient algorithm, LeBQ, which leverages length filtering and symmetric difference allocation to determine the key bounds for a query, enabling the key bounds computation only once for each query$Q$and avoiding costly similarity bounds computation in a node-wise manner. Efficient key filtering strategies are proposed to prune sets that cannot be similar, significantly reducing the number of candidates. Based on LeBQ, LeBQ+ further reduces the number of candidates by introducing length-independent key bounds. Experimental results on four real datasets demonstrate that LeBQ+ has a higher node access efficiency and accesses only 3.08% to 27.47% nodes compared to the existing B+-tree-based SSQ algorithm. LeBQ+is up to 99.8 × faster than the state-of-the-art algorithms.

Ever-growing electronic medical corpora provide unprecedented opportunities for researchers to analyze patient conditions and drug effects. Meanwhile, severe challenges emerged in the large-scale electronic medical records process phase. Primarily, emerging words for medical terms, including informal descriptions, are difficult to recognize. Moreover, although deep models can help in entity extraction on medical texts, they require large-scale labels, which are time-intensive to obtain and not always available in the medical domain. However, when encountering a situation where massive unseen concepts appear or labeled data is insufficient, the performance of existing algorithms will suffer an intolerable decline. In this article, we propose a balanced and deep active learning framework for Medical Named Entity Recognition (MedNER) to alleviate the above problems. Specifically, to describe our selection strategy precisely, we first define the uncertainty of a medical sentence as a labeling loss predicted by a loss-prediction module and define diversity as the least text distance between pairs of sentences in a sample batch computed based on word-morpheme embeddings. Furthermore, aiming to make a trade-off between uncertainty and diversity, we formulate a Distinct-K optimization problem to maximize the slightest uncertainty and diversity of chosen sentences. Finally, we propose a threshold-based approximation selection algorithm, Distinct-K Filter , which selects the most beneficial training samples by balancing diversity and uncertainty. Extensive experimental results on real datasets demonstrate that MedNER significantly outperforms existing approaches.

Structured tabular data is a fundamental data type in numerous fields, and the capacity to reason over tables is crucial for answering questions and validating hypotheses. However, constructing labeled data for complex reasoning tasks is labor-intensive, and the quantity of annotated data remains insufficient to support the intricate demands of real-world applications. To address the insufficient annotation challenge, we present a self-training framework for unsupervised complex tabular reasoning (UCTR-ST) by generating diverse synthetic data with complex logic. Specifically, UCTR-ST incorporates several essential techniques: we aggregate diverse programs and execute them on tables based on a “Program-Management” component, and we bridge the gap between programs and text with a powerful “Program-Transformation” module that generates natural language sentences with complex logic. Furthermore, we optimize the procedure using “Table-Text Manipulator” to handle joint table-text reasoning scenarios. The entire framework utilizes self-training techniques to leverage the unlabeled training data, which results in significant performance improvements when tested on real-world data. Experimental results demonstrate that UCTR-ST achieves above 90% of the supervised model performance on different tasks and domains, reducing the dependence on manual annotation. Additionally, our approach can serve as a data augmentation technique, significantly boosting the performance of supervised models in low-resourced domains.

Structured tabular data exist across nearly all fields. Reasoning task over these data aims to answer questions or determine the truthiness of hypothesis sentences by understanding the semantic meaning of a table. While previous works have devoted significant efforts to the tabular reasoning task, they always assume there are sufficient labeled data. However, constructing reasoning samples over tables (and related text) is labor-intensive, especially when the reasoning process is complex. When labeled data is insufficient, the performance of models will suffer an unendurable decline. In this paper, we propose a unified framework for unsupervised complex tabular reasoning (UCTR), which generates sufficient and diverse synthetic data with complex logic for tabular reasoning tasks, assuming no human-annotated data at all. Specifically, we first utilize a random sampling strategy to collect diverse programs of different types and execute them on tables based on a "Program-Executor" module. To bridge the gap between the programs and natural language sentences, we design a powerful "NL-Generator" module to generate natural language sentences with complex logic from these programs. Since a table often occurs with its surrounding texts, we further propose novel "Table-to-Text" and "Text-to-Table" operators to handle joint table-text reasoning scenarios. This way, we can adequately exploit the unlabeled table resources to obtain a well-performed reasoning model under an unsupervised setting. Our experiments cover different tasks (question answering and fact verification) and different domains (general and specific), showing that our unsupervised methods can achieve at most 93% performance compared to supervised models. The impressive performance demonstrates that UCTR can significantly reduce the dependence on manual annotation. Moreover, we also find that it can substantially boost the supervised performance in low-resourced domains as a data augmentation technique.

Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base, which is significant and fundamental for various downstream applications, e.g., knowledge base completion, question answering, and information extraction. While great efforts have been devoted to this task, most of these studies follow the assumption that large-scale labeled data is available. However, when the labeled data is insufficient for specific domains due to labor-intensive annotation work, the performance of existing algorithms will suffer an intolerable decline. In this paper, we endeavor to solve the problem of few-shot entity linking, which only requires a minimal amount of in-domain labeled data and is more practical in real situations. Specifically, we firstly propose a novel weak supervision strategy to generate non-trivial synthetic entity-mention pairs based on mention rewriting. Since the quality of the synthetic data has a critical impact on effective model training, we further design a meta-learning mechanism to assign different weights to each synthetic entity-mention pair automatically. Through this way, we can profoundly exploit rich and precious semantic information to derive a well-trained entity linking model under the few-shot setting. The experiments on real-world datasets show that the proposed method can extensively improve the state-of-the-art few-shot entity linking model and achieve impressive performance when only a small amount of labeled data is available. Moreover, we also demonstrate the outstanding ability of the model's transferability. Our code and models will be open-sourced.

Similarity search is an essential operation in many applications. Given a collection of set records and a query, the exact set similarity search aims at finding all the records that are similar to the query from the collection. Existing methods adopt a filter-and-verify framework, which make use of inverted indexes. However, as the complexity of verification is rather low for setbased similarity metrics, they always fail to make a good tradeoff between filter power and filter cost. In this paper, we proposed an efficient framework for exact set similarity search based on tree index structure. We defined a hash-based ordering to effectively import data into the index structure and then make optimizations to reduce the filter cost. To further improve the filter power, we proposed a dynamic algorithm to partition the dataset into several parts and propose a multiple-index framework. Experimental results on real-world datasets show that our method significantly outperform the state-of-the-art algorithms.