8 citations · 31 across the 25 of their papers we have counts for
25 papers
DE-Venus: A Data-Efficient RLVR Framework for Large Language Models
Shenzhi Yang, Guangcheng Zhu, Kai Tang +11
Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning, but its practical scaling is constrained by expensive on-policy rollouts and the cost…
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,…
Stop Unnecessary Reflection: Training LRMs for Efficient Reasoning with Adaptive Reflection and Length Coordinated Penalty
Zewei Yu, Lirong Gao, Yuke Zhu +4
Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex reasoning tasks by employing test-time scaling. However, they often generate over-long chains-of-t…
A Syllogistic Probe: Tracing the Evolution of Logic Reasoning in Large Language Models
Zhengqing Zang, Yuqi Ding, Yanmei Gu +5
Human logic has gradually shifted from intuition-driven inference to rigorous formal systems. Motivated by recent advances in large language models (LLMs), we explore whether LLMs…
TraPO: A Semi-Supervised Reinforcement Learning Framework for Boosting LLM Reasoning
Shenzhi Yang, Guangcheng Zhu, Xing Zheng +7
Reinforcement learning with verifiable rewards (RLVR) has proven effective in training large reasoning models (LRMs) by leveraging answer-verifiable signals to guide policy optimiz…
CrowdAgent: Multi-Agent Managed Multi-Source Annotation System
Maosheng Qin, Renyu Zhu, Mingxuan Xia +8
High-quality annotated data is a cornerstone of modern Natural Language Processing (NLP). While recent methods begin to leverage diverse annotation sources-including Large Language…
Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence
Jiaming Tian, Liyao Li, Wentao Ye +6
Tables are fundamental in domains such as finance, healthcare, and public administration, yet real-world table tasks often involve noise, structural heterogeneity, and semantic com…
Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation
Mingxuan Xia, Haobo Wang, Yixuan Li +4
Recently, Large Language Models (LLMs) have demonstrated significant potential for data annotation, markedly reducing the labor costs associated with downstream applications. Howev…
RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis
Pengzuo Wu, Yuhang Yang, Guangcheng Zhu +10
With the rapid advancement of Large Language Models (LLMs), there is an increasing need for challenging benchmarks to evaluate their capabilities in handling complex tabular data.…
FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts
Xinyi Wang, Lirong Gao, Haobo Wang +2
Parameter-Efficient Fine-Tuning (PEFT) methods have emerged as a widely adopted strategy for adapting pre-trained Large Language Models (LLMs) to downstream tasks, significantly re…
Harnessing Feature Resonance under Arbitrary Target Alignment for Out-of-Distribution Node Detection
Shenzhi Yang, Junbo Zhao, Sharon Li +4
Detecting out-of-distribution (OOD) nodes in the graph-based machine-learning field is challenging, particularly when in-distribution (ID) node multi-category labels are unavailabl…
Towards Robust Incremental Learning under Ambiguous Supervision
Rui Wang, Mingxuan Xia, Chang Yao +4
Traditional Incremental Learning (IL) targets to handle sequential fully-supervised learning problems where novel classes emerge from time to time. However, due to inherent annotat…
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…
FlowBench: Revisiting and Benchmarking Workflow-Guided Planning for LLM-based Agents
Ruixuan Xiao, Wentao Ma, Ke Wang +5
LLM-based agents have emerged as promising tools, which are crafted to fulfill complex tasks by iterative planning and action. However, these agents are susceptible to undesired pl…
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey
Lin Long, Rui Wang, Ruixuan Xiao +4
Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models (LLMs) offers…
Data Contamination Calibration for Black-box LLMs
Wentao Ye, Jiaqi Hu, Liyao Li +3
The rapid advancements of Large Language Models (LLMs) tightly associate with the expansion of the training data size. However, the unchecked ultra-large-scale training sets introd…
Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models
Jiachen Ma, Yijiang Li, Zhiqing Xiao +4
Text-to-image (T2I) models can be maliciously used to generate harmful content such as sexually explicit, unfaithful, and misleading or Not-Safe-for-Work (NSFW) images. Previous at…
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…
3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp Features and Parametric Control?
Zeqing Yuan, Haoxuan Lan, Qiang Zou +1
Recent advancements in implicit 3D representations and generative models have markedly propelled the field of 3D object generation forward. However, it remains a significant challe…
Energy-based Automated Model Evaluation
Ru Peng, Heming Zou, Haobo Wang +3
The conventional evaluation protocols on machine learning models rely heavily on a labeled, i.i.d-assumed testing dataset, which is not often present in real world applications. Th…
FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models
Ruixuan Xiao, Yiwen Dong, Junbo Zhao +4
Collecting high-quality labeled data for model training is notoriously time-consuming and labor-intensive for various NLP tasks. While copious solutions, such as active learning fo…
Revisiting the Knowledge Injection Frameworks
Peng Fu, Yiming Zhang, Haobo Wang +2
In recent years, large language models (LLMs), such as GPTs, have attained great impact worldwide. However, how to adapt these LLMs to better suit the vertical domain-specific task…