20 citations · 33 across the 7 of their papers we have counts for
10 papers
Safety stock calculation with heteroscedastic, autocorrelated and heavy-tailed forecast errors, an adaptive parametric method.e
Luis Fernández-Palacios, Manuel Ceballos, Yolanda Muñoz-Ocaña
This study develops and validates an adaptive parametric method for sizing safety stock when forecast errors are autocorrelated, heteroscedastic, asymmetric, and heavy-tailed. Usin…
BioDyad: Synchronize Biomedical Discovery and Machine Learning Engineering
Xingbo Du, Fadli Aulawi Al Ghiffari, Leonard Song +5
Agentic biomedical machine learning (ML) draws on complementary advances in biomedical evidence acquisition and executable program search. Existing systems connect aspects of these…
BioXArena: Benchmarking LLM Agents on Multi-Modal Biomedical Machine Learning Tasks
Loka Li, Duzhen Zhang, Xingbo Du +11
Large language model (LLM) agents increasingly automate machine-learning model building, yet biomedical benchmarks emphasize question answering, reasoning, and tool use or cover na…
MemR: Memory Retrieval via Reflective Reasoning for LLM Agents
Xingbo Du, Loka Li, Duzhen Zhang +1
Memory systems have been designed to leverage past experiences in Large Language Model (LLM) agents. However, many deployed memory systems primarily optimize compression and storag…
MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph
Duzhen Zhang, Zixiao Wang, Zhong-Zhi Li +10
The rapid expansion of medical literature challenges the scalable structuring of domain knowledge. Knowledge Graphs (KGs) offer a solution, yet current construction methods lack ge…
A Survey of Context Engineering for Large Language Models
Lingrui Mei, Jiayu Yao, Yuyao Ge +12
The performance of Large Language Models (LLMs) is fundamentally determined by the contextual information provided during inference. This survey introduces Context Engineering, a f…
LifelongAgentBench: Evaluating LLM Agents as Lifelong Learners
Junhao Zheng, Xidi Cai, Qiuke Li +5
Lifelong learning is essential for intelligent agents operating in dynamic environments. Current large language model (LLM)-based agents, however, remain stateless and unable to ac…
Safety in Large Reasoning Models: A Survey
Cheng Wang, Yue Liu, Baolong Bi +9
Large Reasoning Models (LRMs) have exhibited extraordinary prowess in tasks like mathematics and coding, leveraging their advanced reasoning capabilities. Nevertheless, as these ca…
From System 1 to System 2: A Survey of Reasoning Large Language Models
Zhong-Zhi Li, Duzhen Zhang, Ming-Liang Zhang +18
Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning. While System 1 excels in qu…
Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs
Zixiao Wang, Duzhen Zhang, Ishita Agrawal +3
Previous approaches to persona simulation large language models (LLMs) have typically relied on learning basic biographical information, or using limited role-play dialogue dataset…