8 citations · 13 across the 21 of their papers we have counts for
21 papers
MemoryCPT: An End-to-End Agent Memory Framework for Cost-Performance Trade-off
Songxin Lei, Kun Ouyang, Weilin Ruan +4
Long-horizon LLM agents require memory systems that recover useful evidence from large interaction histories without passing excessive context to downstream models. Existing memory…
When Memory Lies: An Empirical Study of Spatial Memory Staleness in VLM Agents
Yushi Sun, Yanjie Zhang
Memory-augmented VLM agents act on persistent spatial knowledge, yet that knowledge silently goes stale as the environment changes. We ask what happens when an agent must reconcile…
The Personalization Mirage: How LLMs Fabricate User Profiles, and Why Self-Monitoring Misleads
Yushi Sun, Yanjie Zhang, Rui Sheng
Personalized LLMs with persistent memory are increasingly deployed, yet the faithfulness of their user models remains unexamined. We study over-inference (OI): the phenomenon where…
MolecularCanvas: LLM-assisted Small-Molecule Drug Discovery via Structure-Guided Constraints
Haoyu Dong, Rui Sheng, Shuhao Zhang +7
Small-molecule drug discovery relies on iterative molecular optimization, where chemists repeatedly modify candidate compounds to balance multiple competing properties such as effi…
Fewer Clarifications, Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistants
Zijian Xu, Wenshuo Zhang, Zisen Qin +4
AI-assisted coding increasingly translates informal user intent into executable software, yet coding requests often contain ambiguities that recur in user-specific ways across task…
Are LLMs Ready for Scientific Discovery? A Capability-Oriented Benchmark for AI Scientists
Chuhan Shi, Xiaoquan Ren, Sicheng Song +3
Existing benchmarks for scientific data analysis evaluate LLMs primarily on code execution or workflow completion, overlooking that scientific analysis serves to support distinct t…
When Classic Cache Policies Fail: Learning-Augmented Replacement for Semantic Retrieval Buffers
Yushi Sun, Bowen Cao, Wai Lam
LLM agents increasingly rely on retrieval buffers to store and reuse past experience, yet the cache management policies governing these buffers remain largely ad-hoc. We formalize…
STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?
Hanxiang Chao, Yihan Bai, Rui Sheng +2
Large Language Model (LLM) agents are increasingly expected to maintain coherent, long-term personalized memory, yet current benchmarks primarily measure static fact retrieval, ove…
GRAVITY: Architecture-Agnostic Structured Anchoring for Long-Horizon Conversational Memory
Yushi Sun, Bowen Cao, Dong Fang +2
Long-horizon memory systems increasingly improve how evidence is stored and retrieved, yet the generator must still reason over fragments whose cross-session relationships are impl…
CacheRAG: A Semantic Caching System for Retrieval-Augmented Generation in Knowledge Graph Question Answering
Yushi Sun, Lei Chen
The integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) has significantly advanced Knowledge Graph Question Answering (KGQA). However, existing LL…
VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphs
Yurui Xiang, Xingyi Mao, Rui Sheng +7
Large language models (LLMs) show promise in medical diagnosis, but real-world deployment remains challenging due to high-stakes clinical decisions and imperfect reasoning reliabil…
InconLens: Interactive Visual Diagnosis of Behavioral Inconsistencies in LLM-based Agentic Systems
Shuo Yan, Xiaolin Wen, Shaolun Ruan +5
Large Language Model (LLM)-based agentic systems have shown growing promise in tackling complex, multi-step tasks through autonomous planning, reasoning, and interaction with exter…
Navigating the Mirage: A Dual-Path Agentic Framework for Robust Misleading Chart Question Answering
Yanjie Zhang, Yafei Li, Rui Sheng +5
Despite the success of Vision-Language Models (VLMs), misleading charts remain a significant challenge due to their deceptive visual structures and distorted data representations.…
APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL
Bowen Cao, Weibin Liao, Yushi Sun +3
Text-to-SQL systems powered by Large Language Models have excelled on academic benchmarks but struggle in complex enterprise environments. The primary limitation lies in their reli…
LakeHopper: Knowledge-Aware Adaptation of Column Type Annotators across Data Lakes
Yushi Sun, Xujia Li, Nan Tang +3
Column Type Annotation (CTA), which assigns a semantic type to a table column, underpins data integration, cleaning, and search over data lakes. State-of-the-art annotators are pre…
MedKGI: Iterative Differential Diagnosis with Medical Knowledge Graphs and Information-Guided Inquiring
Qipeng Wang, Rui Sheng, Yafei Li +3
Recent advancements in Large Language Models (LLMs) have demonstrated significant promise in clinical diagnosis. However, current models struggle to emulate the iterative, diagnost…
KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering
Yushi Sun, Kai Sun, Yifan Ethan Xu +4
Retrieval-Augmented Generation (RAG) mitigates hallucination in Large Language Models (LLMs) by incorporating external data, with Knowledge Graphs (KGs) offering crucial informatio…
Are Large Language Models a Good Replacement of Taxonomies?
Yushi Sun, Hao Xin, Kai Sun +5
Large language models (LLMs) demonstrate an impressive ability to internalize knowledge and answer natural language questions. Although previous studies validate that LLMs perform…
Cross-domain-aware Worker Selection with Training for Crowdsourced Annotation
Yushi Sun, Jiachuan Wang, Peng Cheng +3
Annotation through crowdsourcing draws incremental attention, which relies on an effective selection scheme given a pool of workers. Existing methods propose to select workers base…
CRAG -- Comprehensive RAG Benchmark
Xiao Yang, Kai Sun, Hao Xin +24
Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)'s deficiency in lack of knowledge. Existing RAG datasets,…
Fake News Detection with Heterogeneous Transformer
Tianle Li, Yushi Sun, Shang-ling Hsu +2
The dissemination of fake news on social networks has drawn public need for effective and efficient fake news detection methods. Generally, fake news on social networks is multi-mo…