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Muxi Diao

7 accepted papers

2026

MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision

AAAI 2026technical

Accurately grounding regions of interest (ROIs) is critical for diagnosis and treatment planning in medical imaging. While multimodal large language models (MLLMs) combine visual perception with natural language, current medical-grounding pipelines still rely on supervised fine-tuning with explicit

Cited by 0SourcePDFScholar
2025

CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery

ICLR 2025poster

Large language models (LLMs) have demonstrated significant potential in advancing various fields of research and society. However, the current community of LLMs overly focuses on benchmarks for analyzing specific foundational skills (e.g. mathematics and code generation), neglecting an all-round eva…

2025

CineTechBench: A Benchmark for Cinematographic Technique Understanding and Generation

NeurIPS 2025poster

Cinematography is a cornerstone of film production and appreciation, shaping mood, emotion, and narrative through visual elements such as camera movement, shot composition, and lighting. Despite recent progress in multimodal large language models (MLLMs) and video generation models, the capacity of…

Cited by 0SourcecodeScholar
2025

SEAS: Self-Evolving Adversarial Safety Optimization for Large Language Models

AAAI 2025technical

As Large Language Models (LLMs) continue to advance in capability and influence, ensuring their security and preventing harmful outputs has become crucial. A promising approach to address these concerns involves training models to automatically generate adversarial prompts for red teaming. However,…

Cited by 0SourcePDFScholar
2025

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

ACL 2025long

Visual mathematical reasoning, as a fundamental visual reasoning ability, has received widespread attention from the Large Multimodal Models (LMMs) community. Existing benchmarks mainly focus more on the end-to-end performance, but neglect the underlying principles of knowledge acquisition and gener…

2024

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

ACL 2024long

Code Large Language Models (Code LLMs) have demonstrated outstanding performance in code-related tasks. Various instruction finetuning approaches have been proposed to boost the code generation performance of pre-trained Code LLMs. In this paper, we introduce a diverse instruction model DolphCoder w…

2024

How Do Your Code LLMs perform? Empowering Code Instruction Tuning with Really Good Data

EMNLP 2024main

Recently, there has been a growing interest in studying how to construct better code instruction tuning data. However, we observe Code models trained with these datasets exhibit high performance on HumanEval but perform worse on other benchmarks such as LiveCodeBench. Upon further investigation, we…