Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? (2025 · 36 citations)
The paper asks where inside a large language model knowledge of different difficulty becomes linearly readable.
A Survey of Large Language Models for Text-Guided Molecular Discovery: From Molecule Generation to Optimization (2026 · 7 citations)
This survey reviews how large language models are used to design molecules from text instructions. It covers two tasks: generating new molecules from scratch and editing an existing molecule to improve its properties. The authors limit the scope to models with over 1B parameters that directly output molecular…
Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection (2026 · 2 citations)
LLM-based multi-agent systems (MAS) can be compromised when one or a few agents are hijacked through prompt injection, memory poisoning, or tool exploitation, and their harmful outputs then spread to other agents.
CoAct: Co-Active LLM Preference Learning with Human-AI Synergy (2026 · 1 citation)
Preference-based alignment of LLMs depends on human-labelled response pairs, which are costly to collect. Self-rewarding pipelines produce such pairs cheaply, but they can amplify the model's own mistakes. Active preference learning keeps labels trustworthy, yet it leaves most of the unlabeled pool unused. CoAct…

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?International Conference on Computational Linguistics (COLING)Mingyu Jin, Qinkai Yu, Jingyuan Huang, et al.2025 · 36 citations
A Survey of Large Language Models for Text-Guided Molecular Discovery: From Molecule Generation to OptimizationAnnual Meeting of the Association for Computational Linguistics (ACL)Ziqing Wang, Kexin Zhang, Zihan Zhao, et al.2026 · 7 citations
Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly DetectionAnnual Meeting of the Association for Computational Linguistics (ACL)Junjun Pan, Yixin Liu, Rui Miao, et al.2026 · 2 citations
Towards Acyclic Preference Evaluation of Language Models via Multiple Evaluators ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetZhengyu Hu et al.2026 · 2 citations
CoAct: Co-Active LLM Preference Learning with Human-AI SynergyAnnual Meeting of the Association for Computational Linguistics (ACL)Ruiyao Xu, Mihir Parmar, Tiankai Yang, et al.2026 · 1 citation

Co-authors here

12 of 21 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 43 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated 31 October 2026