8 citations · 15 across the 12 of their papers we have counts for
13 papers
Beyond Retrieval Failure: Understanding Why LLMs Miss Evidence in Multi-Hop RAG
Jiaqi Wei, Wenxuan Huang, Chengdong Xu +2
Multi-hop retrieval-augmented generation (RAG) requires an agent to retrieve evidence for a sequence of dependent sub-questions. When a wrong answer lacks a required passage, the p…
When LLM Meets Tree Search: A Systematic View of Inference as Search in Large Language Models
Jiaqi Wei, Xiang Zhang, Yuejin Yang +10
As pretraining scaling laws approach saturation, Test-Time Scaling (TTS) has emerged as an important direction for improving reasoning by allocating inference-time compute to a fix…
Reflection Pretraining Enables Token-Level Self-Correction in Biological Sequence Models
Xiang Zhang, Jiaqi Wei, Yuejin Yang +8
Chain-of-Thought (CoT) prompting has significantly advanced task-solving capabilities in natural language processing with large language models. Unlike standard prompting, CoT enco…
Accurate de novo sequencing of the modified proteome with OmniNovo
Yuhan Chen, Shang Qu, Zhiqiang Gao +13
Post-translational modifications (PTMs) serve as a dynamic chemical language regulating protein function, yet current proteomic methods remain blind to a vast portion of the modifi…
Unifying Tree Search Algorithm and Reward Design for LLM Reasoning: A Survey
Jiaqi Wei, Xiang Zhang, Yuejin Yang +10
Deliberative tree search is a cornerstone of modern Large Language Model (LLM) research, driving the pivot from brute-force scaling toward algorithmic efficiency. This single parad…
TimeSeriesScientist: A General-Purpose AI Agent for Time Series Analysis
Haokun Zhao, Xiang Zhang, Jiaqi Wei +4
Time series forecasting is central to decision-making in domains as diverse as energy, finance, climate, and public health. In practice, forecasters face thousands of short, noisy…
Bidirectional Representations Augmented Autoregressive Biological Sequence Generation
Xiang Zhang, Jiaqi Wei, Zijie Qiu +5
Autoregressive (AR) models, common in sequence generation, are limited in many biological tasks such as de novo peptide sequencing and protein modeling by their unidirectional natu…
From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery
Jiaqi Wei, Yuejin Yang, Xiang Zhang +24
Artificial intelligence (AI) is reshaping scientific discovery, evolving from specialized computational tools into autonomous research partners. We position Agentic Science as a pi…
IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction
The IntFold Team, Leon Qiao, Wayne Bai +5
We introduce IntFold, a controllable foundation model for general and specialized biomolecular structure prediction. Utilizing a high-performance custom attention kernel, IntFold a…
Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing
Xiang Zhang, Jiaqi Wei, Zijie Qiu +4
Peptide sequencing-the process of identifying amino acid sequences from mass spectrometry data-is a fundamental task in proteomics. Non-Autoregressive Transformers (NATs) have prov…
Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing
Zijie Qiu, Jiaqi Wei, Xiang Zhang +6
De novo peptide sequencing is a critical task in proteomics. However, the performance of current deep learning-based methods is limited by the inherent complexity of mass spectrome…
Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization
Jiaqi Wei, Hao Zhou, Xiang Zhang +6
Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs). However, standard RAG pipelines often fail t…
ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing
Zhi Jin, Sheng Xu, Xiang Zhang +6
De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de novo algorithms have encountered a bottleneck in accuracy due…