Hao Feng
Hao Feng has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning, Efficient models & compression; often with Haiyang Yu, Han Wang, Jinghui Lu.
OpenAlex · ORCID · h-index 44 · 277 works · 6,127 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 12 times in our library · ⭐ Paper Stars ranking: #9695
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Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works
🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 1 workEfficient models & compression · 1 work
Works by year
2020s
Papers
- Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning (2025 · 11 citations)
Reasoning-tuned LLMs and MLLMs tend to produce long chain-of-thought output for every query. This costs many tokens and can even hurt accuracy on easy, fact-based questions. A pilot with Qwen2.5-0.5B on text-rich VQA and KIE data showed that direct answers were both more accurate and far cheaper than reasoning-based… - From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models (2026 · 1 citation)
Most structured pruning methods for LLMs work locally. They rebuild each layer's outputs separately from calibration data. This keeps perplexity and generic zero-shot behaviour reasonable, but it does not tie the pruning to a downstream task, so task-specific calibration data adds little. The authors return to global…
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