Papers by Alan Wee-Chung Liew
Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection (2026.acl-long)
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Junjun Pan, Yixin Liu, Rui Miao, Kaize Ding, Yu Zheng, Quoc Viet Hung Nguyen, Alan Wee-Chung Liew, Shirui Pan
| Challenge: | Existing graph anomaly detection methods rely on coarse sentence-level information and overlook fine-grained lexical cues, limiting their reliability and real-world applicability. |
| Approach: | They propose an explainable and fine-grained safeguarding framework for detecting malicious agents in multi-agent systems (MAS) to incorporate both coarse and fine lexical information for anomalous agent identification. |
| Outcome: | Extensive experiments across diverse MAS topologies and attack scenarios demonstrate robust detection performance and strong interpretability of XG-Guard. |
Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction (2026.findings-acl)
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| Challenge: | Existing LLM-based methods rely on implicit language-level reasoning, resulting in opaque causal assumptions and fragile predictions. |
| Approach: | They propose an explicit and auditable causal reasoning framework for context-free intervention-based question answering that uses four modular stages rather than implicit end-to-end prediction. |
| Outcome: | The proposed framework outperforms existing LLM-based methods while providing interpretable and auditable causal reasoning traces. |