36 citations · 62 across the 32 of their papers we have counts for
37 papers
ARGUS: An Agenda-Driven Framework for Reliable User Simulation
Hang Zheng, Nan Jiang, Zichen Zhu +6
High-fidelity user simulators are essential for evaluating and training agents in interactive environments. However, current LLM-based simulators struggle with reliability issues,…
VARI: Robust Text-to-Video Generation via Group-Structured Intent Alignment
Mingxuan Zhu, Hohei Chan, Shiyuan Liu +2
Modern text-to-video (T2V) models generate visually compelling videos, yet meaning-preserving changes in prompt wording can substantially alter semantic realization and overall qua…
LigBench: A Unified and Human-Aligned Benchmark for LLM-based Research Idea Generation
Chenrun Wang, Mingxuan Zhu, Tiancheng Huang +6
With the rapid advancement of large language models (LLMs), research idea generation has attracted increasing attention. Existing approaches enable LLMs to retrieve relevant litera…
Hierarchical Data Selection via Manifold Coverage and Sparse Feature Coverage in LLM Post-training
Peng Sun, Yi Yang, Antong Zhang +7
As supervised fine-tuning data continues to scale, selecting high-value subsets from large candidate pools is crucial for reducing training cost and improving model performance. Ex…
Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training
Peng Sun, Yi Yang, Antong Zhang +7
Data selection in supervised fine-tuning aims to select a small set of effective samples from large-scale candidate data, reducing training cost while preserving model performance.…
Making AI Scientists Auditable from Evidence to Claim
Zijian Wang, Hanqi Li, Ziyue Yang +17
AI research systems can report improved results without establishing that the tested implementation matches the method described in their conclusions. We introduce Xcientist, a res…
IEA: Amateur-Friendly Conversational Image Editing Agent via Three Stages of Multitask Alignment
Zichen Zhu, Yuheng Sun, Mingxuan Zhu +11
Current image editing software often hinges on fixed filters or expert tuning, leaving a gap between amateur users' intent and outcomes. Creations by generative models may contain…
OmniMatBench: A Human-Calibrated Multimodal Reasoning Benchmark Across 19 Materials Science Subfields
Wanhao Liu, Jiaqing Xie, Qian Tan +10
As multimodal language models play an increasingly important role in scientific research, materials science offers a critical testbed due to its interdisciplinary, multimodal, and…
RTP-LLM: High-Performance Alibaba LLM Inference Engine
Boyu Tan, Jiarui Guo, Zongwei Lv +26
Large Language Models (LLMs) have revolutionized AI applications, but deploying them at scale presents significant challenges. We present RTP-LLM, a high-performance inference engi…
DeepSurvey: Agent-Oriented Automated Survey Generation with Analytical Depth and Citation Reliability
Ziyue Yang, Da Ma, Hanqi Li +8
As scientific literature grows rapidly and research increasingly involves AI agents, automated survey generation has become a key capability for both agents and human researchers.…
Diagnosing CFG Interpretation in LLMs
Hanqi Li, Lu Chen, Kai Yu
As LLMs are increasingly integrated into agentic systems, they must adhere to dynamically defined, machine-interpretable interfaces. We evaluate LLMs as in-context interpreters: gi…
CharTool: Tool-Integrated Visual Reasoning for Chart Understanding
Situo Zhang, Yifan Zhang, Zichen Zhu +6
Charts are ubiquitous in scientific and financial literature for presenting structured data. However, chart reasoning remains challenging for multimodal large language models (MLLM…
PACER: Blockwise Pre-verification for Speculative Decoding with Adaptive Length
Situo Zhang, Yifan Zhang, Zichen Zhu +5
Speculative decoding (SD) is a powerful technique for accelerating the inference process of large language models (LLMs) without sacrificing accuracy. Typically, SD employs a small…
DiSRouter: Distributed Self-Routing for LLM Selections
Hang Zheng, Hongshen Xu, Yongkai Lin +3
The proliferation of Large Language Models (LLMs) has created a diverse ecosystem of models with highly varying performance and costs, necessitating effective query routing to bala…
AirQA: A Comprehensive QA Dataset for AI Research with Instance-Level Evaluation
Tiancheng Huang, Ruisheng Cao, Yuxin Zhang +8
The growing volume of academic papers has made it increasingly difficult for researchers to efficiently extract key information. While large language models (LLMs) based agents are…
NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering
Ruisheng Cao, Hanchong Zhang, Tiancheng Huang +8
The increasing number of academic papers poses significant challenges for researchers to efficiently acquire key details. While retrieval augmented generation (RAG) shows great pro…
ProgRM: Build Better GUI Agents with Progress Rewards
Danyang Zhang, Situo Zhang, Ziyue Yang +5
LLM-based (Large Language Model) GUI (Graphical User Interface) agents can potentially reshape our daily lives significantly. However, current LLM-based GUI agents suffer from the…
Delusions of Large Language Models
Hongshen Xu, Zixv yang, Zichen Zhu +7
Large Language Models often generate factually incorrect but plausible outputs, known as hallucinations. We identify a more insidious phenomenon, LLM delusion, defined as high beli…
Alignment for Efficient Tool Calling of Large Language Models
Hongshen Xu, Zihan Wang, Zichen Zhu +4
Recent advancements in tool learning have enabled large language models (LLMs) to integrate external tools, enhancing their task performance by expanding their knowledge boundaries…
Task-Specific Data Selection for Instruction Tuning via Monosemantic Neuronal Activations
Da Ma, Gonghu Shang, Zhi Chen +6
Instruction tuning improves the ability of large language models (LLMs) to follow diverse human instructions, but achieving strong performance on specific target tasks remains chal…
Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling
Hang Zheng, Hongshen Xu, Yuncong Liu +3
Large language models (LLMs) are prone to hallucination stemming from misaligned self-awareness, particularly when processing queries exceeding their knowledge boundaries. While ex…
From Generalist to Specialist: A Survey of Large Language Models for Chemistry
Yang Han, Ziping Wan, Lu Chen +2
Large Language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP). However, the predominant pretraining…
AdaEAGLE: Optimizing Speculative Decoding via Explicit Modeling of Adaptive Draft Structures
Situo Zhang, Hankun Wang, Da Ma +4
Speculative Decoding (SD) is a popular lossless technique for accelerating the inference of Large Language Models (LLMs). We show that the decoding speed of SD frameworks with stat…
Reducing Tool Hallucination via Reliability Alignment
Hongshen Xu, Zichen Zhu, Lei Pan +6
Large Language Models (LLMs) have expanded their capabilities beyond language generation to interact with external tools, enabling automation and real-world applications. However,…