Papers by Ziheng Qi

5 papers
TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents (2025.emnlp-demos)

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Challenge: Existing research systems often design and use agentic workflows to perform research tasks such as ideation, scientific coding, review writing, and tree-based search.
Approach: They propose an open-source codebase, an interactive web demonstration, and a PyPI Python package to make state-of-the-art auto-research pipelines broadly accessible to every researcher and developer.
Outcome: The proposed framework adapts easily to new tools and supports iterative growth.
MovieUN: A Dataset for Movie Understanding and Narrating (2022.findings-emnlp)

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Challenge: Automatic movie narration generation and narration grounding are important to provide a true movie experience for the blind and visually impaired.
Approach: They propose to use movie clips as a benchmark to support automatic movie narration generation and narration grounding tasks.
Outcome: The proposed methods are effective in supporting two movie-based tasks for the blind and visually impaired.
Dual-Alignment Pre-training for Cross-lingual Sentence Embedding (2023.acl-long)

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Challenge: Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding.
Approach: They propose a dual-alignment pre-training framework that incorporates both sentence-level and token-level alignment.
Outcome: The proposed framework improves cross-lingual sentence embedding on three cross-linguistic benchmarks.
Movie101: A New Movie Understanding Benchmark (2023.acl-long)

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Challenge: Existing methods to narrate movies with no actors are difficult to implement in real situations . a new metric is proposed to provide the best correlation with human evaluation .
Approach: They propose a large-scale Chinese movie benchmark to help visually impaired enjoy movies . they propose metric called Movie Narration Score (MNScore) which achieves best correlation with human evaluation.
Outcome: The proposed method outperforms baselines and the existing methods.
SafeScientist: Enhancing AI Scientist Safety for Risk-Aware Scientific Discovery (2025.emnlp-main)

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Challenge: Recent advances in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet raised critical ethical and safety concerns.
Approach: They propose a framework to enhance safety and ethical responsibility in AI-driven scientific exploration.
Outcome: The proposed framework significantly improves safety performance by 35% compared to traditional frameworks.

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