MessageSieve: Runtime Message Pruning for Accurate and Efficient LLM-Based Multi-Agent Communication

@article{nianhao2026,
  title = {MessageSieve: Runtime Message Pruning for Accurate and Efficient LLM-Based Multi-Agent Communication},
  author = {You, Nianhao and Dong, Yubo and Wang, Jun and Fan, Hehe and Yang, Yi},
  journal = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
  year = {2026},
  month = {Jan},
}

You, Nianhao et al. (2026). MessageSieve: Runtime Message Pruning for Accurate and Efficient LLM-Based Multi-Agent Communication. Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing.

You, Nianhao, et al.. "MessageSieve: Runtime Message Pruning for Accurate and Efficient LLM-Based Multi-Agent Communication." Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, 2026.

You, Nianhao, et al.. "MessageSieve: Runtime Message Pruning for Accurate and Efficient LLM-Based Multi-Agent Communication." Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (2026).

You, Nianhao et al., 2026. MessageSieve: Runtime Message Pruning for Accurate and Efficient LLM-Based Multi-Agent Communication. Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing.

TY  - JOUR
TI  - MessageSieve: Runtime Message Pruning for Accurate and Efficient LLM-Based Multi-Agent Communication
AU  - You, Nianhao
AU  - Dong, Yubo
AU  - Wang, Jun
AU  - Fan, Hehe
AU  - Yang, Yi
PY  - 2026
JO  - Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
ER  -