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title: "Hao Peng — papers, page 2 — Elamur Research"
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# Hao Peng · page 2

31 papers in our library · 2015–2026 · page 2 of 2

[The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/hao-peng/)

## Papers

The most cited first (OpenAlex and our library).

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26. **[Pre-training Distillation for Large Language Models: A Design Space Exploration](https://md.research.elamur.ai/free/en/papers/peng-2025-pre-training-distillation-large-language/)** *(2025 · 14 citations)* Knowledge distillation for large language models is usually applied after pre-training, when a student is fine-tuned on responses written by a stronger teacher.
27. **[MAVEN-FACT: A Large-scale Event Factuality Detection Dataset](https://md.research.elamur.ai/free/en/papers/li-2024-maven-fact-large-scale-event/)** *(2024 · 6 citations)* Event Factuality Detection (EFD) asks whether a mentioned event actually happened, might happen, or did not happen.
28. **[MixKVQ: Query-Aware Mixed-Precision KV Cache Quantization for Long-Context Reasoning](https://md.research.elamur.ai/free/en/papers/zhang-2026-mixkvq-query-aware-mixed-precision/)** *(2026 · 5 citations)* Reasoning-oriented LLMs produce very long Chain-of-Thought outputs, so their KV cache consumes large amounts of GPU memory and bandwidth.
29. **[LLMs are Vulnerable to Malicious Prompts Disguised as Scientific Language](https://md.research.elamur.ai/free/en/papers/ge-2025-llms-vulnerable-malicious-prompts-disguised/)** *(2025 · 5 citations)* The paper asks whether safety-aligned LLMs can be pushed into producing stereotyped or toxic output when a harmful request is framed in academic terms.
30. **[Activation-Guided Local Editing for Jailbreaking Attacks](https://md.research.elamur.ai/free/en/papers/wang-2026-activation-guided-local-editing-jailbreaking/)** *(2026 · 1 citation)* The paper addresses the weaknesses of current jailbreak techniques used to red-team aligned LLMs. Token-level attacks tend to yield gibberish suffixes that filters catch easily and that rarely transfer to other models. Prompt-level attacks depend on manual crafting or costly iterative search. The authors present…
31. **[WildReward: Learning Reward Models from In-the-Wild Human Interactions](https://md.research.elamur.ai/free/en/papers/peng-2026-wildreward-learning-reward-models-wild/)** *(2026)* Reward models for LLM training usually depend on large sets of human-annotated preference pairs, which are expensive to collect.

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