Athiwaratkun, Ben

Mixture-of-Agents Enhances Large Language Model Capabilities

Wang, J., Wang, J., Athiwaratkun, B., Zhang, C., & Zou, J. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 386

Multi-lingual Evaluation of Code Generation Models

Athiwaratkun, B., Gouda, S. K., Wang, Z., Li, X., Tian, Y., Tan, M., Ahmad, W. U., Wang, S., Sun, Q., Shang, M., et al. (2022) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 38

Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies

Wang, J., Jain, S., Zhang, D., Ray, B., Kumar, V., & Athiwaratkun, B. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 8

$V_1$: Unifying Generation and Self-Verification for Parallel Reasoners

Singh, H., Li, X., Sareen, K., Maheswaran, M., Tan, S., Wu, X., Wang, J., Ariyak, A., Wu, Q., Khaki, S., et al. (2026) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 0

Squeeze Evolve: Unified Multi-Model Orchestration for Verifier-Free Evolution

Maheswaran, M., Lakhani, L., Zhou, Z., Yang, S., Wang, J., Hooper, C., Hu, Y., Tiwari, R., Wang, J., Singh, H., et al. (2026) · arXiv

Evolutionary Algorithms and ApplicationsSoftware Engineering Research

Cited by 0