Papers by Yongxuan Wu

2 papers
LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams (2026.findings-acl)

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Challenge: Existing studies show that spoken text exhibits unique linguistic properties, such as high redundancy and repetitive phrases.
Approach: They propose a long-text dataset that better handles redundancy in spoken text . their results highlight key limitations of current methods and suggest future directions .
Outcome: The proposed benchmark improves existing methods and improves on redundancy in spoken text.
CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems (2026.acl-long)

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Challenge: LLM-based multi-agent systems (MAS) have demonstrated remarkable capabilities in solving complex tasks.
Approach: They propose a communication inference attack that constructs new adversarial queries to induce intermediate agents’ reasoning outputs and models their semantic correlations through the global bias disentanglement and LLM-guided weak supervision.
Outcome: The proposed attack achieves an average AUC of 0.87 and a peak AUC up to 0.99, revealing the privacy risk in MAS.

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