From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 23 more institutions on single bylines are not listed.

Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 12Computer Science · 7 works Artificial Intelligence · 6 works Arts and Humanities · 5 works Language and Linguistics · 5 works Computer Vision and Pattern Recognition · 3 works Signal Processing · 1 work Social Sciences · 1 work Computational Theory and Mathematics · 1 work Decision Sciences · 1 work Education · 1 work Information Systems · 1 work Management Science and Operations Research · 1 work

🧭 Topics 5Language models & pretraining · 5 works Reasoning · 3 works Efficient models & compression · 1 work Vision & language · 1 work Speech & spoken language · 1 work

Works by year

2020s

20263 papers 20254 papers

Papers

The most cited first (OpenAlex and our library).

Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models (2025 · 12 citations)
Multimodal large language models can memorize private information, and erasing it is harder than in text-only LLMs.
SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models (2025 · 11 citations)
Large audio-language models (LALMs) can understand speech, but they are weak at multi-step logical reasoning.
Superficial Self-Improved Reasoners Benefit from Model Merging (2025 · 8 citations)
Self-improvement, where a language model is fine-tuned on reasoning traces it generated itself, is often proposed as a remedy for scarce high-quality data.
Growing Through Experience: Scaling Episodic Grounding in Language Models (2025 · 5 citations)
The paper addresses how language models can learn from past interaction experience (episodic grounding) to plan physical, multi-step tasks.
What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical Reasoning (2026 · 4 citations)
Curriculum learning, which sorts training examples from easy to hard, is often used to improve LLM reasoning.
Behavior Knowledge Merge in Reinforced Agentic Models (2026 · 3 citations)
The paper tackles how to combine several agents, each specialized through reinforcement learning (RL), into one generalist model without further training.
What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context (2026 · 1 citation)
LLM-based sequential recommenders are usually tuned with DPO-style alignment, which reduces user feedback to simple preferred-versus-dispreferred pairs.

Co-authors here

12 of 14 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

Cited by

Papers of our library cite their works 39 times (of the 149,439 papers whose bibliographies we read); 14 citing papers with a page here →.

Updated 2 November 2026

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🧑‍🔬 Chunhui Zhang

🏛️ Affiliations

🧭 Fields, topics and concepts

📅 Works by year

2020s

📄 Papers

🤝 Co-authors here

Venues

🔗 Cited by

Chunhui Zhang

Affiliations

Fields, topics and concepts

Works by year

Papers

Co-authors here

Cited by