Papers by Yili Wang
BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks (2026.acl-long)
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| 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)
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| 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)
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| 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)
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| 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. |