--- title: "Hai Zhao — papers, page 2 · Shanghai Jiao Tong University" description: "Hai Zhao, Shanghai Jiao Tong University: 29 papers on Elamur Research (2009–2026), h-index 43 — affiliations, topics, co-authors and who…" canonical: "https://research.elamur.ai/free/en/authors/hai-zhao/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/hai-zhao/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Hai Zhao](https://md.research.elamur.ai/free/en/authors/hai-zhao/) / page 2 # Hai Zhao · page 2 29 papers in our library · 2009–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/hai-zhao/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/hai-zhao/) [1](https://md.research.elamur.ai/free/en/authors/hai-zhao/) **2** 26. **[PAR: Training-Free Positional Perturbation and Attention Recycling for Faithful OCR](https://md.research.elamur.ai/free/en/papers/yao-2026-par-training-free-positional-perturbation/)** *(2026)* The paper addresses Linguistic Priors Hallucination in vision-language models used for OCR. When a model sees familiar text, it tends to output the memorized version instead of transcribing what the image actually shows. To measure this, the authors build the GlitchText Probing Dataset. It takes highly familiar… 27. **[ParaCook: On Time-Efficient Planning for Multi-Agent Systems](https://md.research.elamur.ai/free/en/papers/zhang-2026-paracook-time-efficient-planning-multi/)** *(2026)* Most LLM agent benchmarks check whether a task gets done, not how quickly. They ignore idle time, blocking and contention for shared resources when work runs in parallel or asynchronously. ParaCook fills this gap with a benchmark for time-efficient planning in multi-agent systems. It is inspired by Overcooked and set… 28. **[TrigReason: Trigger-Based Collaboration between Small and Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/zhao-2026-trigreason-trigger-based-collaboration-between/)** *(2026)* Large Reasoning Models (LRMs) solve hard problems well, but their long chains of thought make inference slow. 29. **[Towards a Semantic Annotation of English Television News - Building and Evaluating a Constraint Grammar FrameNet](https://md.research.elamur.ai/free/en/papers/yang-2012-semantic-annotation-english-television-news/)** *(2012)* Note: the requested title does not match the supplied text. The supplied text is instead a PACLIC 2012 paper by the same authors that applies machine translation to converting entire Chinese sentences from Pinyin into characters, and this summary covers that text. [← prev](https://md.research.elamur.ai/free/en/authors/hai-zhao/) [1](https://md.research.elamur.ai/free/en/authors/hai-zhao/) **2**