8 citations · 16 across the 10 of their papers we have counts for
11 papers
Memorizing Who, Knowing How: Benchmarking Personal and Procedural Memory for Persistent Assistants
Maosheng Qin, Qi Zhang, Mingxuan Xia +5
Memory systems for persistent personal agents must move beyond isolated memories, maintaining both personal memory about whom they serve and procedural memory about how to act. Cru…
Reasoning Error from Known Fact: Step-Level Self-Consistency Group Relative Policy Optimization for LLMs
Xiaomeng Hu, Jiaqi Hu, Hao Chen +4
With the rapid advancement of large language models (LLMs), modern systems not only possess strong foundational capabilities and extensive knowledge, but can also solve complex pro…
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…
SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization
Qi Zhang, Zhaopeng Feng, Xiaonan Shi +8
Agent skills, which consist of reusable strategies that guide agent reasoning and action, have shown strong potential for improving model capability at inference time. However, cur…
DeltaMem: Towards Agentic Memory Management via Reinforcement Learning
Qi Zhang, Shen Huang, Chu Liu +4
Recent advances in persona-centric memory have revealed the powerful capability of multi-agent systems in managing persona memory, especially in conversational scenarios. However,…
LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization
Qi Zhang, Shouqing Yang, Lirong Gao +8
Large language models (LLMs) have demonstrated impressive capabilities in reasoning with the emergence of reasoning models like OpenAI-o1 and DeepSeek-R1. Recent research focuses o…
D.Va: Validate Your Demonstration First Before You Use It
Qi Zhang, Zhiqing Xiao, Ruixuan Xiao +2
In-context learning (ICL) has demonstrated significant potential in enhancing the capabilities of large language models (LLMs) during inference. It's well-established that ICL heav…
TableGPT2: A Large Multimodal Model with Tabular Data Integration
Aofeng Su, Aowen Wang, Chao Ye +30
The emergence of models like GPTs, Claude, LLaMA, and Qwen has reshaped AI applications, presenting vast new opportunities across industries. Yet, the integration of tabular data r…
RECOST: External Knowledge Guided Data-efficient Instruction Tuning
Qi Zhang, Yiming Zhang, Haobo Wang +1
In the current landscape of large language models (LLMs), the process of instruction tuning serves as an essential step. Considering the high computing power overhead, data-efficie…
Towards Domain-Specific Features Disentanglement for Domain Generalization
Hao Chen, Qi Zhang, Zenan Huang +2
Distributional shift between domains poses great challenges to modern machine learning algorithms. The domain generalization (DG) signifies a popular line targeting this issue, whe…
Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning
Hao Chen, Yiming Zhang, Qi Zhang +5
Instruction tuning for large language models (LLMs) has gained attention from researchers due to its ability to unlock the potential of LLMs in following instructions. While instru…