Dinh-Truong Do — accepted AI-conference papers

2026

AdaSpec: Adaptive Multilingual Speculative Decoding with Self-Synthesized Language-Aware Training and Vocabulary Simplification

Dinh-Truong Do, Nguyen-Khang Le, Le-Minh Nguyen

AAAI 2026technical

Speculative decoding accelerates large language model (LLM) inference by using a lightweight drafter to propose multiple tokens, which are then verified in parallel by the base model. While effective in English, existing methods often struggle in multilingual scenarios due to static vocabularies and

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2026

Docora: A System for Interactive Knowledge Extraction and Visualization from Scientific PDFs

Dinh-Truong Do, Hoang-An Trieu, Van-Thuy Phi, Le-Minh Nguyen, Yuji Matsumoto

AAAI 2026technical

Scientific research articles, typically distributed in PDF format, contain valuable knowledge but remain challenging to convert into structured datasets due to fragmented workflows that separate parsing, annotation, and visualization. Existing annotation platforms operate on plain text, which requir

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