Papers by Wenyuan Wang

5 papers
Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models (2025.naacl-long)

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Challenge: Multimodal Large Language Models have shown significant promise in various applications, but a comprehensive evaluation of their long-context capabilities remains underexplored.
Approach: They propose a benchmark to assess the long-context capabilities of multimodal large language models.
Outcome: The proposed benchmark compared MLLMs with API-based and open-source models in a long-context scenario.
Test of Time: Rethinking Temporal Signal of Benchmark Contamination (2026.acl-long)

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Challenge: Existing work on benchmarks containing publicly available information has been interpreted as a temporal signal for benchmark contamination.
Approach: They show that LLM-transformed questions can produce remarkably different temporal patterns compared to fill-in-the-blank questions directly retrieved from the very same documents.
Outcome: The proposed model can produce different temporal patterns compared to fill-in-the-blank questions retrieved from the same documents.
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation (2024.emnlp-demo)

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Challenge: Existing research on Retrieval Augmented Generation (RAG) does not address the problem of hallucinations and real-time updating of knowledge.
Approach: They propose a modular open-source library to equip LLMs with external knowledge.
Outcome: The proposed approach reduces the need for expensive open-source tools and lacks fair comparisons between novel RAG algorithms.
When 20 Agents Fail to Sort: The Distributed Sorting Benchmark for Scalable Multi-Agent Systems (2026.findings-acl)

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Challenge: MAS-BENCH isolates coordination under explicit communication constraints . CAMOC significantly improves coordination success and efficiency across backends .
Approach: They propose a distributed-sorting benchmark that isolates coordination under explicit communication constraints.
Outcome: MAS-BENCH improves coordination success and efficiency across backends . CAMOC significantly improves efficiency under shared-state interaction .
Optimal Transport Guided Correlation Assignment for Multimodal Entity Linking (2024.findings-acl)

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Challenge: Existing methods to link ambiguous mentions to entities in multimodal knowledge graphs rely on partial correlations.
Approach: They propose a framework that leverages multi-element correlations to bridge modality gap and enable fine-grained semantic matching by exploiting correlation between multimodal features and entities.
Outcome: The proposed framework outperforms state-of-the-art models and confirms the effectiveness of the proposed method.

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