2 citations · 8 across the 20 of their papers we have counts for
20 papers
Scientific Data Skills: Enabling Agent-Ready Scientific Data Services at Scale
Xiaohan Huang, Qingqing Long, Xiaolei Du +8
Scientific data are increasingly used by AI agents, yet existing dataset representations provide limited support for reliable dataset discovery and interpretation, constraining the…
Test-Time Deep Thinking to Explore Implicit Rules
Wentong Chen, Xin Cong, Zhong Zhang +8
With the continuous advancement of Large Language Models (LLMs), intelligent agents are becoming increasingly vital. However, these agents often fail in environments governed by im…
From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching
Siyu Pu, Qingqing Long, Xiaohan Huang +7
Single-cell RNA sequencing (scRNA-seq) provides high-dimensional profiles of cellular states, enabling data-driven modeling of cellular dynamics over time. In practice, time-resolv…
ResPrune: Text-Conditioned Subspace Reconstruction for Visual Token Pruning in Large Vision-Language Models
Xu Li, Yi Zheng, Yuxuan Liang +5
Large Vision-Language Models (LVLMs) rely on dense visual tokens to capture fine-grained visual information, but processing all these tokens incurs substantial computational and me…
AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents
Shengda Fan, Xuyan Ye, Yupeng Huo +9
While Large Language Models (LLMs) have evolved into tool-using agents, they remain brittle in long-horizon interactions. Unlike mathematical reasoning where errors are often recti…
AgentCPM-Explore: Realizing Long-Horizon Deep Exploration for Edge-Scale Agents
Haotian Chen, Xin Cong, Shengda Fan +16
While Large Language Model (LLM)-based agents have shown remarkable potential for solving complex tasks, existing systems remain heavily reliant on large-scale models, leaving the…
DeepEra: A Deep Evidence Reranking Agent for Scientific Retrieval-Augmented Generated Question Answering
Haotian Chen, Qingqing Long, Siyu Pu +6
With the rapid growth of scientific literature, scientific question answering (SciQA) has become increasingly critical for exploring and utilizing scientific knowledge. Retrieval-A…
ScienceDB AI: An LLM-Driven Agentic Recommender System for Large-Scale Scientific Data Sharing Services
Qingqing Long, Haotian Chen, Chenyang Zhao +6
The rapid growth of AI for Science (AI4S) has underscored the significance of scientific datasets, leading to the establishment of numerous national scientific data centers and sha…
AtomMem : Learnable Dynamic Agentic Memory with Atomic Memory Operation
Yupeng Huo, Yaxi Lu, Zhong Zhang +2
Equipping agents with memory is essential for solving real-world long-horizon problems. However, most existing agent memory mechanisms rely on static and hand-crafted workflows. Th…
Do LLMs Signal When They're Right? Evidence from Neuron Agreement
Kang Chen, Yaoning Wang, Kai Xiong +4
Large language models (LLMs) commonly boost reasoning via sample-evaluate-ensemble decoders, achieving label free gains without ground truth. However, prevailing strategies score c…
SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generation with LLMs
Haotian Chen, Qingqing Long, Meng Xiao +6
Scientific literature question answering is a pivotal step towards new scientific discoveries. Recently, \textit{two-stage} retrieval-augmented generated large language models (RAG…
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning
Zhong Zhang, Yaxi Lu, Yikun Fu +22
The recent progress of large language model agents has opened new possibilities for automating tasks through graphical user interfaces (GUIs), especially in mobile environments whe…
MiniCPM4: Ultra-Efficient LLMs on End Devices
MiniCPM Team, Chaojun Xiao, Yuxuan Li +80
This paper introduces MiniCPM4, a highly efficient large language model (LLM) designed explicitly for end-side devices. We achieve this efficiency through systematic innovation in…
ToLeaP: Rethinking Development of Tool Learning with Large Language Models
Haotian Chen, Zijun Song, Boye Niu +8
Tool learning, which enables large language models (LLMs) to utilize external tools effectively, has garnered increasing attention for its potential to revolutionize productivity a…
Collaborative Evolving Strategy for Automatic Data-Centric Development
Xu Yang, Haotian Chen, Wenjun Feng +7
Artificial Intelligence (AI) significantly influences many fields, largely thanks to the vast amounts of high-quality data for machine learning models. The emphasis is now on a dat…
Towards Data-Centric Automatic R&D
Haotian Chen, Xinjie Shen, Zeqi Ye +6
The progress of humanity is driven by those successful discoveries accompanied by countless failed experiments. Researchers often seek the potential research directions by reading…
Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction
Haotian Chen, Bingsheng Chen, Xiangdong Zhou
Document-level relation extraction (DocRE) attracts more research interest recently. While models achieve consistent performance gains in DocRE, their underlying decision rules are…
Does Debiasing Inevitably Degrade the Model Performance
Yiran Liu, Xiao Liu, Haotian Chen +1
Gender bias in language models has attracted sufficient attention because it threatens social justice. However, most of the current debiasing methods degraded the model's performan…
Knowledge is Power: Understanding Causality Makes Legal judgment Prediction Models More Generalizable and Robust
Haotian Chen, Lingwei Zhang, Yiran Liu +2
Legal Judgment Prediction (LJP), aiming to predict a judgment based on fact descriptions according to rule of law, serves as legal assistance to mitigate the great work burden of l…
Contextualized End-to-End Neural Entity Linking
Haotian Chen, Andrej Zukov-Gregoric, Xi David Li +1
We propose yet another entity linking model (YELM) which links words to entities instead of spans. This overcomes any difficulties associated with the selection of good candidate m…