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Fei Tan

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4 papers

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AAAI Conference 2026 Conference Paper

MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy

Shaoxiong Zhan
Yanlin Lai
Ziyu Lu
Dahua Lin
Ziqing Yang
Fei Tan

Large language models have achieved substantial progress in mathematical reasoning, yet their advancement is limited by the scarcity of high-quality, high-difficulty training data. Existing synthesis methods largely rely on transforming human-written templates, limiting both diversity and scalability. We propose MathSmith, a novel framework for synthesizing challenging mathematical problems to enhance LLM reasoning. Rather than modifying existing problems, MathSmith constructs new ones from scratch by randomly sampling concept–explanation pairs from PlanetMath, ensuring data independence and avoiding contamination. To increase difficulty, we design nine predefined strategies as soft constraints during rationales. We further adopts reinforcement learning to jointly optimize structural validity, reasoning complexity, and answer consistency. The length of the reasoning trace generated under autoregressive prompting is used to reflect cognitive complexity, encouraging the creation of more demanding problems aligned with long-chain-of-thought reasoning. Experiments across five benchmarks, categorized as easy & medium (GSM8K, MATH-500) and hard (AIME2024, AIME2025, OlympiadBench), show that MathSmith consistently outperforms existing baselines under both short and long CoT settings. Additionally, a weakness-focused variant generation module enables targeted improvement on specific concepts. Overall, MathSmith exhibits strong scalability, generalization, and transferability, highlighting the promise of high-difficulty synthetic data in advancing LLM reasoning capabilities.

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AAAI Conference 2023 Conference Paper

PUnifiedNER: A Prompting-Based Unified NER System for Diverse Datasets

Jinghui Lu
Rui Zhao
Brian Mac Namee
Fei Tan

Much of named entity recognition (NER) research focuses on developing dataset-specific models based on data from the domain of interest, and a limited set of related entity types. This is frustrating as each new dataset requires a new model to be trained and stored. In this work, we present a ``versatile'' model---the Prompting-based Unified NER system (PUnifiedNER)---that works with data from different domains and can recognise up to 37 entity types simultaneously, and theoretically it could be as many as possible. By using prompt learning, PUnifiedNER is a novel approach that is able to jointly train across multiple corpora, implementing intelligent on-demand entity recognition. Experimental results show that PUnifiedNER leads to significant prediction benefits compared to dataset-specific models with impressively reduced model deployment costs. Furthermore, the performance of PUnifiedNER can achieve competitive or even better performance than state-of-the-art domain-specific methods for some datasets. We also perform comprehensive pilot and ablation studies to support in-depth analysis of each component in PUnifiedNER.

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AAAI Conference 2020 Conference Paper

DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature

Chaoran Cheng
Fei Tan
Zhi Wei

We consider the problem of Named Entity Recognition (NER) on biomedical scientiﬁc literature, and more speciﬁcally the genomic variants recognition in this work. Signiﬁcant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domainspeciﬁc areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the ﬁrst successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any handcrafted features or post-processing rules. Our extensive experiments and results may shed light on other similar lowresource NER applications.

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IJCAI Conference 2019 Conference Paper

Success Prediction on Crowdfunding with Multimodal Deep Learning

Chaoran Cheng
Fei Tan
Xiurui Hou
Zhi Wei

We consider the problem of project success prediction on crowdfunding platforms. Despite the information in a project profile can be of different modalities such as text, images, and metadata, most existing prediction approaches leverage only the text dominated modality. Nowadays rich visual images have been utilized in more and more project profiles for attracting backers, little work has been conducted to evaluate their effects towards success prediction. Moreover, meta information has been exploited in many existing approaches for improving prediction accuracy. However, such meta information is usually limited to the dynamics after projects are posted, e. g. , funding dynamics such as comments and updates. Such a requirement of using after-posting information makes both project creators and platforms not able to predict the outcome in a timely manner. In this work, we designed and evaluated advanced neural network schemes that combine information from different modalities to study the influence of sophisticated interactions among textual, visual, and metadata on project success prediction. To make pre-posting prediction possible, our approach requires only information collected from the pre-posting profile. Our extensive experimental results show that the image features could improve success prediction performance significantly, particularly for project profiles with little text information. Furthermore, we identified contributing elements.

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