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# Yue Zhang · page 3

55 papers in our library · 2008–2026 · page 3 of 3

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

## Papers

The most cited first (OpenAlex and our library).

[← prev](https://md.research.elamur.ai/free/en/authors/yue-zhang/page/2/) [1](https://md.research.elamur.ai/free/en/authors/yue-zhang/) [2](https://md.research.elamur.ai/free/en/authors/yue-zhang/page/2/) **3**

51. **[DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning](https://md.research.elamur.ai/free/en/papers/sivakumaran-2026-dart-leveraging-multi-agent-disagreement/)** *(2026 · 1 citation)* Debates between vision-language models (VLMs) often stall because the agents share similar perceptual blind spots and bring no new evidence.
52. **[Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extraction](https://md.research.elamur.ai/free/en/papers/zhang-2026-unveiling-deficiencies-pre-trained-text/)** *(2026 · 1 citation)* Pre-trained text-and-layout models (PTLMs) score very highly on standard benchmarks for extracting information from visually-rich documents (VrD-IE), but they disappoint in practice.
53. **[Breaking Down and Building Up: Mixture of Skill-Based Vision-and-Language Navigation Agents](https://md.research.elamur.ai/free/en/papers/ma-2026-breaking-down-building-up-mixture/)** *(2026)* Vision-and-Language Navigation (VLN) agents still generalize poorly to unseen buildings and unfamiliar instruction phrasing.
54. **[Modular Neural Machine Translation with a Semantic Pivot - Pilot Study Using AMR](https://md.research.elamur.ai/free/en/papers/gao-2026-modular-neural-machine-translation-semantic/)** *(2026)* The paper builds a translation pipeline with a semantic pivot, in the spirit of the translation pyramid. Each language gets two modules: one that reads a sentence into English AMR, and one that writes the target sentence from AMR. The training data is a silver AMR corpus made by a strong parser from the United…
55. **[Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection](https://md.research.elamur.ai/free/en/papers/niu-2026-routing-generated-data-annotation-free/)** *(2026)* LLM routers choose which model in a pool should handle a given query. Most existing routers learn from human-labelled, in-domain data, which is often unavailable when real user traffic is varied and unpredictable. The authors propose Routing with Generated Data (RGD). In this setting, a generator LLM writes queries…

[← prev](https://md.research.elamur.ai/free/en/authors/yue-zhang/page/2/) [1](https://md.research.elamur.ai/free/en/authors/yue-zhang/) [2](https://md.research.elamur.ai/free/en/authors/yue-zhang/page/2/) **3**