Papers by Yili Wang

4 papers
BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks (2026.acl-long)

Copied to clipboard

Challenge: Existing supervised defense methods rely on labeled malicious agents to train a supervised model of malicious behavior.
Approach: They propose an unsupervised defense method that learns without requiring any attack-specific labels or prior knowledge of malicious behaviors.
Outcome: The proposed method detects diverse attack types across MAS with various communication patterns while maintaining superior generalizability compared to baselines.
Pioneering Reliable Assessment in Text-to-Image Knowledge Editing: Leveraging a Fine-Grained Dataset and an Innovative Criterion (2024.findings-emnlp)

Copied to clipboard

Challenge: Text-to-image models encode factual knowledge into their parameters, but they may become obsolete over time.
Approach: They propose a framework for T2I knowledge editing that integrates paraphrase and multi-object test to enable more fine-grained assessment on knowledge generalization.
Outcome: The proposed framework improves on existing models and improves their performance.
CrystalICL: Enabling In-Context Learning for Crystal Generation (2025.emnlp-main)

Copied to clipboard

Challenge: Existing methods for crystal generation are limited to zero-shot scenarios and are unable to benefit from few-shot situations.
Approach: They propose a model designed for few-shot crystal generation that exploits in-context learning by capturing structure-property relationships from limited data.
Outcome: The proposed model reduces complexity of modeling crystal symmetry in LLMs and exploits ICL by capturing structure-property relationships from limited data.
Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems (2025.emnlp-main)

Copied to clipboard

Challenge: Empirical studies for communication topology design often overlook why and when sparse and dense topologies help or hinder collaboration.
Approach: They propose a topology design approach that balances error suppression and beneficial information propagation by fusing connectivity patterns from dense and sparse graphs.
Outcome: The proposed topology design achieves superior performance across tasks with sparse and dense graphs.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations