Publications

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Conference Papers

SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution

Published in ICML 2026, 2026

A benchmark for evaluating AI agents on continuous software evolution through sequences of verifiable development milestones.

Recommended citation: Gangda Deng, Zhaoling Chen, Zhongming Yu, Haoyang Fan, Yuhong Liu, **Yuxin Yang**, Dhruv Parikh, Rajgopal Kannan, Le Cong, Mengdi Wang, Qian Zhang, Viktor Prasanna, Xiangru Tang, Xingyao Wang. "SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution." International Conference on Machine Learning (ICML), 2026.
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SAGERec: Sampling and Gating for Enhanced Long-Tail Item Recommendations

Published in WSDM 2026, 2026

SAGERec introduces novel sampling and gating mechanisms to enhance recommendation performance for long-tail items, addressing the challenge of imbalanced item popularity distributions in recommendation systems.

Recommended citation: Abdulla Alshabanah, **Yuxin Yang**, Murali Annavaram. "SAGERec: Sampling and Gating for Enhanced Long-Tail Item Recommendations." Proceedings of the 19th ACM International Conference on Web Search and Data Mining (WSDM), 2026.
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SPARC-RAG: Adaptive Sequential-Parallel Scaling with Context Management for Retrieval-Augmented Generation

Published in EMNLP 2026 Main Conference, 2026

A multi-agent framework for improving retrieval-augmented generation systems on multi-hop questions through unified context management that coordinates sequential and parallel reasoning while avoiding context pollution.

Recommended citation: **Yuxin Yang**, Gangda Deng, Ömer Faruk Akgül, Nima Chitsazan, Yash Govilkar, Akasha Tigalappanavara, Shi-Xiong Zhang, Sambit Sahu, Viktor Prasanna. "SPARC-RAG: Adaptive Sequential-Parallel Scaling with Context Management for Retrieval-Augmented Generation." Accepted to the EMNLP 2026 Main Conference; arXiv preprint arXiv:2602.00083, 2026.
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Training Diverse Graph Experts for Ensembles: A Systematic Empirical Study

Published in Preprint, 2025, 2025

This paper presents a systematic empirical study on training diverse graph experts for ensemble methods, exploring different strategies to improve graph neural network performance through ensemble learning.

Recommended citation: Gangda Deng\*, **Yuxin Yang**\*, Ömer Faruk Akgül\*, Hanqing Zeng, Yinglong Xia, Rajgopal Kannan, Viktor Prasanna. "Training Diverse Graph Experts for Ensembles: A Systematic Empirical Study." arXiv preprint arXiv:2510.18370, 2025.
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RECIPE-TKG: From Sparse History to Structured Reasoning for LLM-based Temporal Knowledge Graph Completion

Published in EACL 2026, 2025

RECIPE-TKG is a lightweight and data-efficient framework that combines rule-based multi-hop retrieval, contrastive fine-tuning, and test-time semantic filtering for temporal knowledge graph completion using LLMs.

Recommended citation: Ömer Faruk Akgül\*, Feiyu Zhu\*, **Yuxin Yang**, Rajgopal Kannan, Viktor Prasanna. "RECIPE-TKG: From Sparse History to Structured Reasoning for LLM-based Temporal Knowledge Graph Completion." Accepted to EACL 2026; arXiv preprint arXiv:2505.17794, 2026.
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Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours

Published in VLDB 2025, 2024

This work proposed a practical approach to TGNN model comparison that compares models at the modular level with a standardized and optimized implementation framework. It further revealed interplay between modules and datasets.

Recommended citation: **Yuxin Yang**, Hongkuan Zhou, Rajgopal Kannan, Viktor Prasanna. "Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours." Proceedings of the VLDB Endowment, Volume 18, Issue 4, 2025.
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PA-GNN: Parameter-Adaptive Graph Neural Networks

Published in Workshop on Dynamic Neural Networks at ICML 2022, 2022

This paper designed a trainable node-specific aggregator that learns from node position and features to create an adaptive graph filter. Spotlight presentation at ICML 2022 Dynamic Neural Networks Workshop.

Recommended citation: **Yuxin Yang**, Yitao Liang, Muhan Zhang. "PA-GNN: Parameter-Adaptive Graph Neural Networks." Workshop on Dynamic Neural Networks in the 39th International Conference on Machine Learning (ICML-22-DyNN-Workshop), spotlight presentation, 2022. [Video]
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