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Shantou University

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Research library60linked papers
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Selected work

Representative Papers

Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL

Mar 06, 2026North American Chapter of the Association for Computational Linguistics

This work addresses the challenge that generative language models struggle to effectively model dynamically evolving database schemas and contextual dependencies in multi-turn Text-to-SQL tasks. To this end, the authors propose a dual extraction-augmented architecture comprising a semantics-enhanced schema extractor and a schema-aware context extractor, which jointly track schema evolution and contextual information across dialogue turns with high precision. These components are integrated with a generative language model and co-optimized for SQL generation. The proposed approach achieves state-of-the-art performance, yielding absolute improvements of 7.1% and 9.55% in execution accuracy on the SparC and CoSQL benchmarks, respectively.

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CDFM: Towards a General-Purpose Causal Discovery Foundation Model

Jul 13, 2026

This work addresses the challenges posed by the heterogeneity and ever-increasing scale of real-world data, which render traditional causal discovery methods fragmented and poorly scalable. To overcome these limitations, the paper introduces CDFM—the first general-purpose foundation model for causal discovery—that models unknown causal mechanisms as latent variables within a variational framework and leverages large-scale synthetic structural causal models for pretraining, enabling zero-shot inference of causal structures. The approach innovatively highlights the critical role of causal priors in identifiability and incorporates a modular learning architecture based on variational decomposition. Experimental results demonstrate that CDFM significantly outperforms existing algorithms across diverse unseen scenarios, exhibiting strong generalization capabilities and practical utility.

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Recent publications

Latest Papers

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

Jul 13, 2026

This work addresses the challenges posed by the heterogeneity and ever-increasing scale of real-world data, which render traditional causal discovery methods fragmented and poorly scalable. To overcome these limitations, the paper introduces CDFM—the first general-purpose foundation model for causal discovery—that models unknown causal mechanisms as latent variables within a variational framework and leverages large-scale synthetic structural causal models for pretraining, enabling zero-shot inference of causal structures. The approach innovatively highlights the critical role of causal priors in identifiability and incorporates a modular learning architecture based on variational decomposition. Experimental results demonstrate that CDFM significantly outperforms existing algorithms across diverse unseen scenarios, exhibiting strong generalization capabilities and practical utility.

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MAGIS: Evidence-Based Multi-Agent Reasoning for Interpretable Strabismus Clinical Decision-Making

Jun 08, 2026

This study addresses the need for fine-grained and interpretable clinical decision support in diagnosing strabismus subtypes, a task hindered by the opacity of existing deep learning approaches and the hallucination tendencies of large vision-language models. To overcome these limitations, the authors propose a multi-agent reasoning framework that transforms end-to-end diagnosis into a structured pipeline. This framework integrates visual evidence from ocular alignment photographs with clinical rules through a Dual-Evidence Constrained Context (DECC) module and employs an Evidence-Based Corrective Verification (EBCV) mechanism to ensure reliable reasoning and report generation. Evaluated on a fine-grained strabismus benchmark, the method improves the weighted F1 score from 72.0% to 91.3% and substantially enhances the clinical reliability of diagnostic reports in terms of consistency, alignment, and completeness.

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