Papers by Yichen Yuan
TaoType: Predicting Fine-Grained Typing Intent for Faster Search (2026.acl-industry)
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| Challenge: | Large-scale industrial ranking systems operate under stringent real-time performance requirements. |
| Approach: | They propose a client-side framework that determines whether a user’s query is complete at each typing . this method leverages client-based typing behavior for real-time early prediction . |
| Outcome: | The proposed framework achieves offline precision/recall/accuracy of 0.7936/0.8196/0.7742 and decreases online response time by 640.5193.65 milliseconds. |
PaSa: An LLM Agent for Comprehensive Academic Paper Search (2025.acl-long)
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| Challenge: | We introduce PaSa, an advanced Paper Search agent powered by large language models . despite being trained on synthetic data, PaSA outperforms existing baselines on RealScholarQuery . |
| Approach: | They introduce PaSa, an advanced Paper Search agent powered by large language models . they optimize PaSA using a synthetic dataset, AutoScholarQuery, which includes 35k fine-grained queries . |
| Outcome: | The paper analyzes the performance of a paper search agent using a synthetic dataset . it significantly outperforms existing benchmarks on RealScholarQuery . |