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20202026
most citedDomain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey

74 citations · 106 across the 26 of their papers we have counts for

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9 papers · 1 filter

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

Coupled Hierarchical Search over Topology and Execution for Agentic Workflow Synthesis

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.AI2026

AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration

Jiaqi Liu, Shi Qiu, Mairui Li +33

Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail…

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Large language models (LLMs) often produce incorrect or outdated content after being employed.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.AI2024

Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation

Minhua Lin, Zhengzhang Chen, Yanchi Liu +6

Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. High-quality annotations are essential for effectively understanding time s…

cs.AI2023

Adaptation Speed Analysis for Fairness-aware Causal Models

Yujie Lin, Chen Zhao, Minglai Shao +2

For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the trai…

cs.AI2023★ 7 cited

Knowledge-enhanced Neural Machine Reasoning: A Review

Tanmoy Chowdhury, Chen Ling, Xuchao Zhang +5

Knowledge-enhanced neural machine reasoning has garnered significant attention as a cutting-edge yet challenging research area with numerous practical applications. Over the past f…

cs.AI2022★ 10 cited

A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning

Zhen Guo, Zelin Wan, Qisheng Zhang +7

An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Deep/machine learning (ML/DL) has been hugely leveraged to solve complex…