2 citations · 2 across the 14 of their papers we have counts for
16 papers
SURE-EVAL: A Systematic and Unified Agentic Framework for Reproducible Evaluation
Jing Peng, Junhao Du, Yixuan Wang +21
Audio and speech models are released rapidly, but reported scores often conflate model capability with deployment and evaluation choices. The same checkpoint can produce different…
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…
Distributed Agent System: Fault-Tolerant Collaboration Among Embodied Agents
Kai Yu, Lu Chen, Hanqi Li
AI engineering is shifting from passive text generation by large language models (LLMs) to agent-driven task execution, creating new reliability challenges for long-horizon tasks u…
A Unified and Reproducible Experimentation Framework for Speech Understanding
Jing Peng, Junhao Du, Chenghao Wang +21
Speech foundation models and Speech LLMs have advanced speech understanding, yet deployment-oriented model selection is hindered by non-comparable evaluations caused by mismatched…
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.…
Audio-Mind: An Auditable Agentic Framework for Audio Understanding
Yucheng Wang, Jing Peng, Hanqi Li +6
Audio agents extend large audio-language models (LALMs) by decomposing audio questions into tool calls, intermediate evidence, and iterative reasoning steps. However, as LALMs beco…
VCap: Hypergeometric Rewards for Weak-to-Strong Visual Captioning
Xingyu Lu, Jinpeng Wang, Yi-Fan Zhang +13
Visual captioning requires models to capture visual content faithfully while minimizing both omission and hallucination. As the dominant paradigm for captioning, MLLMs have achieve…
MetaphorVU: Towards Metaphorical Video Understanding
Zhuoqun Li, Boxi Cao, Guiping Jiang +13
Metaphorical videos are prevalent across various real-world scenarios to convey complex ideas, and understanding them typically requires high-order cognitive capabilities. The lack…
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…
PaperGuide: Making Small Language-Model Paper-Reading Agents More Efficient
Zijian Wang, Tiancheng Huang, Hanqi Li +3
The accelerating growth of the scientific literature makes it increasingly difficult for researchers to track new advances through manual reading alone. Recent progress in large la…
When Long Helps Short: How Context Length in Supervised Fine-tuning Affects Behavior of Large Language Models
Yingming Zheng, Hanqi Li, Kai Yu +1
Large language models (LLMs) have achieved impressive performance across natural language processing (NLP) tasks. As real-world applications increasingly demand longer context wind…
RetroDFM-R: Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning
Situo Zhang, Hanqi Li, Lu Chen +7
Retrosynthetic planning is a cornerstone of organic synthesis and drug discovery. Yet existing AI methods often rely on pattern matching rather than transferable chemical reasoning…
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…
Compressing KV Cache for Long-Context LLM Inference with Inter-Layer Attention Similarity
Da Ma, Lu Chen, Situo Zhang +8
The rapid expansion of context window sizes in Large Language Models~(LLMs) has enabled them to tackle increasingly complex tasks involving lengthy documents. However, this progres…
Evolving Subnetwork Training for Large Language Models
Hanqi Li, Lu Chen, Da Ma +3
Large language models have ushered in a new era of artificial intelligence research. However, their substantial training costs hinder further development and widespread adoption. I…
Sparsity-Accelerated Training for Large Language Models
Da Ma, Lu Chen, Pengyu Wang +6
Large language models (LLMs) have demonstrated proficiency across various natural language processing (NLP) tasks but often require additional training, such as continual pre-train…