activity
20242026
most citedLearning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation

1 citations · 2 across the 10 of their papers we have counts for

collaborators

12 papers

cs.LG2026

Quantifying Event Impacts on Time Series via Multiscale Contrastive Learning

Yiming Sun, Shengyu Chen, Zhengzhang Chen +3

Shocks that spread through the web, such as cybersecurity breach disclosures, can abruptly disrupt financial time series and cause substantial abnormal losses. While these events a…

cs.IR2026

HARP: Hierarchical Adaptive Ranking with Preference-Adaptive Fusion for Query-Based CVE Prioritization

Haochen Liu, Zhengzhang Chen, Haoyu Wang +3

Vulnerability prioritization is inherently preference dependent, since the same CVE can receive different remediation priority under different operational preference scenarios. Exi…

cs.AI2026

MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models

Dong Li, Yanchi Liu, Xujiang Zhao +6

Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments. However, directly updating their li…

cs.AI2026

HierFlow: Hierarchical Coupled Dual-Space Search for Automatic Agentic Workflow Generation

Dong Li, Yanchi Liu, Xujiang Zhao +6

Although structured workflows empower Large Language Models (LLMs) to tackle complex problems, automating their creation is severely hindered by a vast combinatorial search space,…

cs.LG2026

The Power of Order: Fooling LLMs with Adversarial Table Permutations

Xinshuai Dong, Haifeng Chen, Xuyuan Liu +5

Large Language Models have achieved remarkable success and are increasingly deployed in critical applications involving tabular data, such as Table Question Answering. However, the…

cs.CL2026

Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models

Binchi Zhang, Xujiang Zhao, Jundong Li +2

Large language models (LLMs) are increasingly deployed in culturally sensitive real-world tasks. However, existing cultural alignment approaches fail to align LLMs' broad cultural…

cs.MA2026

City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modification

Rui Liu, Steven Jige Quan, Zhong-Ren Peng +6

Urban renewal requires incremental modifications to existing geospatial plans, yet manually updating complex layouts under spatial constraints is labor-intensive and error-prone. T…

cs.AI2026

Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement

Wangyang Ying, Yanchi Liu, Xujiang Zhao +5

Automatically extracting workflows as procedural graphs from natural language is promising yet underexplored, demanding both structural validity and logical alignment. While recent…

cs.AI2025

Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs

Xuyuan Liu, Shengyu Chen, Xinshuai Dong +6

Large language models (LLMs) often produce incorrect or outdated content after being employed. Efficient and accurate knowledge updates without costly retraining are a major challe…

cs.LG2025

SolverLLM: Leveraging Test-Time Scaling for Optimization Problem via LLM-Guided Search

Dong Li, Xujiang Zhao, Linlin Yu +7

Large Language Models (LLMs) offer promising capabilities for tackling complex reasoning tasks, including optimization problems. However, existing methods either rely on prompt eng…

cs.CL2025★ 1 cited

Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation

Haoyue Bai, Haoyu Wang, Shengyu Chen +5

Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA), yet they often struggle in domain-specific scenarios where accurate and up-to-dat…

cs.LG2024★ 1 cited

Multi-modal Causal Structure Learning and Root Cause Analysis

Lecheng Zheng, Zhengzhang Chen, Jingrui He +1

Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses, and ensuring the smooth operation and management of complex systems. Previous data-d…