Papers by Yiwei Dai
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. |
Mitigate Extrinsic Social Bias in Pre-trained Language Models via Continuous Prompts Adjustment (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods of extrinsic bias mitigation rely on manual word lists for sensitive groups . however, these word lists are limited by length and scope, resulting in poor performance. |
| Approach: | They propose a method which generates continuous token lists from the entire vocabulary space and uses them to bridge the gap between outputs and targets in fairness learning process. |
| Outcome: | The proposed method outperforms baseline methods on three NLU tasks. |
CSMCIR: CoT-Enhanced Symmetric Alignment with Memory Bank for Composed Image Retrieval (2026.findings-acl)
Copied to clipboard
Zhipeng Qian, Zihan Liang, Yufei Ma, Ben Chen, Huangyu Dai, Yiwei Ma, Jiayi Ji, Chenyi Lei, Han Li, Xiaoshuai Sun
| Challenge: | Existing approaches to search for images using single-modality are limited by representation space fragmentation. |
| Approach: | They propose a unified representation framework that achieves efficient query-target alignment . they introduce a multi-level Chain-of-Thought prompting strategy that guides MLMs to generate discriminative, semantically compatible captions for target images . |
| Outcome: | The proposed framework achieves efficient query-target alignment through synergistic components. |
MoEC: A Memory-Routed Mixture-of-Experts Controller for Adaptive Minecraft Control (2026.acl-long)
Copied to clipboard
| Challenge: | Existing systems rely on a monolithic policy to execute subgoals across varying contexts, causing inconsistent outcomes and scaling only partially mitigates. |
| Approach: | They propose a memory-routed mixtureof-experts controller for Adaptive Minecraft Control that routes via a subgoal-indexed expert memory and regulates capacity through failure-triggered expert growth and redundancy-aware consolidation. |
| Outcome: | The proposed controller shows significant gains in adaptability, robustness, and execution consistency over strong baselines. |
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. |