84 citations · 186 across the 28 of their papers we have counts for
30 papers
Entertain, then Cancel: How Thinking Makes Reasoning Models Read Some and Or Literally
Zhizhuo Kou, Jian Yang, Pengfei Hao +4
When a speaker says ``some students passed'', listeners infer that not all of them did. Reasoning models now let users decide how long a model thinks before it answers, but we do n…
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models
Yiyang Gu, Junwei Yang, Junyu Luo +15
Large language models (LLMs) are increasingly applied to scientific research, yet existing evaluations often fail to reflect the fine-grained capabilities required in practice. Mos…
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions
Zhiyin Yu, Yuchen Mou, Juncheng Yan +17
Reinforcement learning (RL) has emerged as a powerful post-training paradigm for enhancing the reasoning capabilities of large language models (LLMs). However, reinforcement learni…
MEME: Modeling the Evolutionary Modes of Financial Markets
Taian Guo, Haiyang Shen, Junyu Luo +7
LLMs have demonstrated significant potential in quantitative finance by processing vast unstructured data to emulate human-like analytical workflows. However, current LLM-based met…
AlphaPROBE: Alpha Mining via Principled Retrieval and On-graph biased evolution
Taian Guo, Haiyang Shen, Junyu Luo +6
Extracting signals through alpha factor mining is a fundamental challenge in quantitative finance. Existing automated methods primarily follow two paradigms: Decoupled Factor Gener…
Delay-Oriented Distributed Scheduling with TransGNN
Boxuan Wen, Junyu Luo
Minimizing transmission delay in wireless multi-hop networks is a fundamental yet challenging task due to the complex coupling among interference, queue dynamics, and distributed c…
BAMAS: Structuring Budget-Aware Multi-Agent Systems
Liming Yang, Junyu Luo, Xuanzhe Liu +2
Large language model (LLM)-based multi-agent systems have emerged as a powerful paradigm for enabling autonomous agents to solve complex tasks. As these systems scale in complexity…
A Survey on Efficient Large Language Model Training: From Data-centric Perspectives
Junyu Luo, Bohan Wu, Xiao Luo +8
Post-training of Large Language Models (LLMs) is crucial for unlocking their task generalization potential and domain-specific capabilities. However, the current LLM post-training…
Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
Yangning Li, Weizhi Zhang, Yuyao Yang +17
Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inf…
Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation
Junyu Luo, Yuhao Tang, Yiwei Fu +6
Unsupervised Graph Domain Adaptation (UGDA) leverages labeled source domain graphs to achieve effective performance in unlabeled target domains despite distribution shifts. However…
From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
Weizhi Zhang, Yangning Li, Yuanchen Bei +20
Information retrieval is a cornerstone of modern knowledge acquisition, enabling billions of queries each day across diverse domains. However, traditional keyword-based search engi…
FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation
Junyu Luo, Zhizhuo Kou, Liming Yang +10
Multimodal Large Language Models (MLLMs) have experienced rapid development in recent years. However, in the financial domain, there is a notable lack of effective and specialized…
Cross-Domain Diffusion with Progressive Alignment for Efficient Adaptive Retrieval
Junyu Luo, Yusheng Zhao, Xiao Luo +5
Unsupervised efficient domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, while maintaining low storage cost and high…
MASS: Muli-agent simulation scaling for portfolio construction
Taian Guo, Haiyang Shen, JinSheng Huang +9
The application of LLM-based agents in financial investment has shown significant promise, yet existing approaches often require intermediate steps like predicting individual stock…
Multifaceted Evaluation of Audio-Visual Capability for MLLMs: Effectiveness, Efficiency, Generalizability and Robustness
Yusheng Zhao, Junyu Luo, Xiao Luo +5
Multi-modal large language models (MLLMs) have recently achieved great success in processing and understanding information from diverse modalities (e.g., text, audio, and visual si…
Large Language Model Agent: A Survey on Methodology, Applications and Challenges
Junyu Luo, Weizhi Zhang, Ye Yuan +23
The era of intelligent agents is upon us, driven by revolutionary advancements in large language models. Large Language Model (LLM) agents, with goal-driven behaviors and dynamic a…
Attention Bootstrapping for Multi-Modal Test-Time Adaptation
Yusheng Zhao, Junyu Luo, Xiao Luo +4
Test-time adaptation aims to adapt a well-trained model to potential distribution shifts at test time using only unlabeled test data, without access to the original training data.…
Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention
Jingyang Yuan, Huazuo Gao, Damai Dai +12
Long-context modeling is crucial for next-generation language models, yet the high computational cost of standard attention mechanisms poses significant computational challenges. S…
RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response
Junyu Luo, Xiao Luo, Kaize Ding +3
Supervised fine-tuning (SFT) plays a crucial role in adapting large language models (LLMs) to specific domains or tasks. However, as demonstrated by empirical experiments, the coll…
GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation
Junyu Luo, Yiyang Gu, Xiao Luo +5
Source-free domain adaptation is a crucial machine learning topic, as it contains numerous applications in the real world, particularly with respect to data privacy. Existing appro…
Semi-supervised Fine-tuning for Large Language Models
Junyu Luo, Xiao Luo, Xiusi Chen +3
Supervised fine-tuning (SFT) is crucial in adapting large language model (LLMs) to a specific domain or task. However, only a limited amount of labeled data is available in practic…
DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation
Pengyun Wang, Yadi Cao, Chris Russell +5
Graph domain adaptation has recently enabled knowledge transfer across different graphs. However, without the semantic information on target graphs, the performance on target graph…
Automate Strategy Finding with LLM in Quant Investment
Zhizhuo Kou, Holam Yu, Junyu Luo +7
We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance. Our a…