10 citations · 18 across the 14 of their papers we have counts for
15 papers
Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability
Sizhe Zhou, Sheldon Yu, Hui Wei +8
Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, and reorganizes through generi…
How Hard Does It Think? Analyzing Step-Aware Reasoning Energy in LLM Chain-of-Thought Trajectories
Hui Wei, Junda Wu, Sheldon Yu +8
Understanding how computational effort is allocated across individual chain-of-thought (CoT) reasoning steps remains an open challenge: existing interpretability methods rely on ou…
F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking
Rohan Surana, Gagan Mundada, Junda Wu +9
Traditional retrieval pipelines optimize utility through stages of candidate retrieval and reranking, where ranking operates over a predefined candidate set. Large Language Models…
OLIVIA: Online Learning via Inference-time Action Adaptation for Decision Making in LLM ReAct Agents
Sheldon Yu, Junda Wu, Xintong Li +6
Large language model agents interleave reasoning, action selection, and observation to solve sequential decision-making tasks. In deployed settings where agents repeatedly handle r…
Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning
Rohan Surana, Gagan Mundada, Xunyi Jiang +19
Reinforcement learning (RL) has become a central post-training tool for improving the reasoning abilities of large language models (LLMs). In these systems, the rollout, the trajec…
MultiCube-RAG for Multi-hop Question Answering
Jimeng Shi, Wei Hu, Runchu Tian +8
Multi-hop question answering (QA) necessitates multi-step reasoning and retrieval across interconnected subjects, attributes, and relations. Existing retrieval-augmented generation…
A Simple Yet Strong Baseline for Long-Term Conversational Memory of LLM Agents
Sizhe Zhou, Jiawei Han
LLM-based conversational agents still struggle to maintain coherent, personalized interaction over many sessions: fixed context windows limit how much history can be kept in view,…
TEXT2DB: Integration-Aware Information Extraction with Large Language Model Agents
Yizhu Jiao, Sha Li, Sizhe Zhou +2
The task of information extraction (IE) is to extract structured knowledge from text. However, it is often not straightforward to utilize IE output due to the mismatch between the…
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation
Jiashuo Sun, Xianrui Zhong, Sizhe Zhou +1
Retrieval-augmented generation (RAG) systems combine large language models (LLMs) with external knowledge retrieval, making them highly effective for knowledge-intensive tasks. A c…
Hypercube-Based Retrieval-Augmented Generation for Scientific Question-Answering
Jimeng Shi, Sizhe Zhou, Bowen Jin +5
Large language models (LLMs) often need to incorporate external knowledge to solve theme-specific problems. Retrieval-augmented generation (RAG) has shown its high promise, empower…
Deep Learning and Foundation Models for Weather Prediction: A Survey
Jimeng Shi, Azam Shirali, Bowen Jin +10
Physics-based numerical models have been the bedrock of atmospheric sciences for decades, offering robust solutions but often at the cost of significant computational resources. De…
Establishing Knowledge Preference in Language Models
Sizhe Zhou, Sha Li, Yu Meng +3
Language models are known to encode a great amount of factual knowledge through pretraining. However, such knowledge might be insufficient to cater to user requests, requiring the…
Automated Construction of Theme-specific Knowledge Graphs
Linyi Ding, Sizhe Zhou, Jinfeng Xiao +1
Despite widespread applications of knowledge graphs (KGs) in various tasks such as question answering and intelligent conversational systems, existing KGs face two major challenges…
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction
Sizhe Zhou, Yu Meng, Bowen Jin +1
Relation extraction (RE) aims to identify semantic relationships between entities within text. Despite considerable advancements, existing models predominantly require extensive an…
Towards End-to-End Open Conversational Machine Reading
Sizhe Zhou, Siru Ouyang, Zhuosheng Zhang +1
In open-retrieval conversational machine reading (OR-CMR) task, machines are required to do multi-turn question answering given dialogue history and a textual knowledge base. Exist…