Muxi Diao

According to our database1, Muxi Diao authored at least 15 papers between 2024 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

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Bibliography

2025
MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision.
CoRR, August, 2025

OJBench: A Competition Level Code Benchmark For Large Language Models.
CoRR, June, 2025

DriveRX: A Vision-Language Reasoning Model for Cross-Task Autonomous Driving.
CoRR, May, 2025

Harnessing Caption Detailness for Data-Efficient Text-to-Image Generation.
CoRR, May, 2025

CineTechBench: A Benchmark for Cinematographic Technique Understanding and Generation.
CoRR, May, 2025

CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

SEAS: Self-Evolving Adversarial Safety Optimization for Large Language Models.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024
From Simple to Professional: A Combinatorial Controllable Image Captioning Agent.
CoRR, 2024

How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data.
CoRR, 2024

We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?
CoRR, 2024

Multi-Perspective Consistency Enhances Confidence Estimation in Large Language Models.
CoRR, 2024

DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning.
CoRR, 2024

How Do Your Code LLMs perform? Empowering Code Instruction Tuning with Really Good Data.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024


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