Papers by Beilun Wang
LiGen: Active Lipid Generation via a Molecular Language Model (2026.acl-long)
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| Challenge: | Lipid nanoparticles (LNPs) can deliver cargos to tumor and immune cells . traditional approaches rely on experimental screening and expert judgment . |
| Approach: | They propose a method to generate lipid molecules efficiently and actively using deep learning. |
| Outcome: | The proposed method outperforms baseline methods on multiple cell lines and achieves a 30% improvement over the current methods. |
Structured Episodic Event Memory (2026.acl-long)
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| Challenge: | Current approaches to memory in Large Language Models (LLMs) rely on static Retrieval-Augmented Generation (RAG) this lacks the cognitive organization necessary to model the dynamic and associative nature of long-term interaction. |
| Approach: | They propose a hierarchical framework that transforms interaction streams into structured Episodic Event Frames (EEFs) anchored by precise provenance pointers. |
| Outcome: | The proposed framework outperforms baseline approaches on LoCoMo and LongMemEval benchmarks. |
Compressing LLM Knowledge into Graph Representations for Text-attributed Graphs Learning (2026.acl-long)
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| Challenge: | Existing GNN-LLM approaches use large language models at inference time for processing text attributes, resulting in costly deployment. |
| Approach: | They propose a framework that internalizes LLM knowledge within graph models and supports inference-efficient TAG learning. |
| Outcome: | The proposed framework internalizes LLM knowledge within graph models and supports inference-efficient TAG learning. |