1 citations · 2 across the 14 of their papers we have counts for
15 papers
Compile, Don't Memorize: A Context Compilation Architecture (CCA) for In-Context Learning
Jinhu Qi, Minda Hu, Wentao Zhang +4
Large language models (LLMs) increasingly handle in-context learning (ICL) tasks where a long, novel context defines the rules, knowledge, and output schema for a series of questio…
TREK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planning
Jinhu Qi, Wentao Zhang, Siu Man Ng +4
Travel planning is a demanding stress test for tool-using LLM agents: a usable itinerary is a single artifact that must be right along many axes at once - every flight, hotel, and…
Reinforcement Learning for Large Language Model Selective Evidence Adoption from Contaminated Retrieval Results
Yanyu Chen, Yue Li, Yongyi Cui +2
Retrieval-augmented large language models frequently face contexts that interleave useful evidence with misleading statements or instruction-like content. Blanket refusal discards…
Momentum for Reasoning: Dense Intrinsic Signals in Policy Optimization
Hao Chen, Zhanming Shen, Liyao Li +8
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for eliciting long-chain reasoning in large language models. However, existing methods base…
LC-ERD: Mining Latent Logic for Self-Evolving Reasoning via Consistency-Regulated Reward Decomposition
Yanyu Chen, Jiyue Jiang, Dianzhi Yu +8
The evolution of Large Language Model (LLM) reasoning is bottlenecked by the scarcity of high-quality process data. While self-alignment via endogenous rewards offers a solution, m…
GUI-CIDER: Mid-training GUI Agents via Causal Internalization and Density-aware Exemplar Reselection
Zheng Wu, Chengcheng Han, Zhengxi Lu +5
Despite the rapid progress of multimodal large language models in building Graphical User Interface (GUI) agents, their real-world task completion is fundamentally bottlenecked by…
A Principle-Driven Adaptive Policy for Group Cognitive Stimulation Dialogue for Elderly with Cognitive Impairment
Jiyue Jiang, Yanyu Chen, Pengan Chen +7
Cognitive impairment is becoming a major public health challenge. Cognitive Stimulation Therapy (CST) is an effective intervention for cognitive impairment, but traditional methods…
TRACE: Trajectory-Aware Comprehensive Evaluation for Deep Research Agents
Yanyu Chen, Jiyue Jiang, Jiahong Liu +3
The evaluation of Deep Research Agents is a critical challenge, as conventional outcome-based metrics fail to capture the nuances of their complex reasoning. Current evaluation fac…
Query-focused and Memory-aware Reranker for Long Context Processing
Yuqing Li, Jiangnan Li, Mo Yu +5
Built upon the existing analysis of retrieval heads in large language models, we propose an alternative reranking framework that trains models to estimate passage-query relevance u…
DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design
Yanting Li, Jiyue Jiang, Zikang Wang +11
Inverse Protein Folding (IPF) is a critical subtask in the field of protein design, aiming to engineer amino acid sequences capable of folding correctly into a specified three-dime…
Benchmarking Large Language Models on Multiple Tasks in Bioinformatics NLP with Prompting
Jiyue Jiang, Pengan Chen, Jiuming Wang +13
Large language models (LLMs) have become important tools in solving biological problems, offering improvements in accuracy and adaptability over conventional methods. Several bench…
Developing and Utilizing a Large-Scale Cantonese Dataset for Multi-Tasking in Large Language Models
Jiyue Jiang, Alfred Kar Yin Truong, Yanyu Chen +7
High-quality data resources play a crucial role in learning large language models (LLMs), particularly for low-resource languages like Cantonese. Despite having more than 85 millio…
GUIDE: A Global Unified Inference Engine for Deploying Large Language Models in Heterogeneous Environments
Yanyu Chen, Ganhong Huang
Efficiently deploying large language models (LLMs) in real-world scenarios remains a critical challenge, primarily due to hardware heterogeneity, inference framework limitations, a…
Training and Serving System of Foundation Models: A Comprehensive Survey
Jiahang Zhou, Yanyu Chen, Zicong Hong +6
Foundation models (e.g., ChatGPT, DALL-E, PengCheng Mind, PanGu-) have demonstrated extraordinary performance in key technological areas, such as natural language processing and…
CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models
Yanyu Chen, Yao Yao, Wai Kin Victor Chan +4
Data sparsity and cold-start problems are persistent challenges in recommendation systems. Cross-domain recommendation (CDR) is a promising solution that utilizes knowledge from th…