Portrait of Sifan Wu is unavailable

Sifan Wu

PhD - Université de Montréal
Supervisor
Bang Liu
Research Topics
Deep Learning
Natural Language Processing

Publications

What to Ask Next? Probing the Imaginative Reasoning of LLMs with TurtleSoup Puzzles
Mi Zhou
Sifan Wu
H. Zhang
Qi Sima
Bang Liu
We investigate the capacity of Large Language Models (LLMs) for imaginative reasoning—the proactive construction, testing, and revision of… (see more) hypotheses in information-sparse environments. Existing benchmarks, often static or focused on social deduction, fail to capture the dynamic, exploratory nature of this reasoning process. To address this gap, we introduce a comprehensive research framework based on the classic "Turtle Soup" game, integrating a benchmark, an agent, and an evaluation protocol. We present TurtleSoup-Bench, the first large-scale, bilingual, interactive benchmark for imaginative reasoning, comprising 800 turtle soup stories sourced from both the Internet and expert authors. We also propose Mosaic-Agent, a novel agent designed to assess LLMs' performance in this setting. To evaluate reasoning quality, we develop a multi-dimensional protocol measuring logical consistency, detail completion, and conclusion alignment. Experiments with leading LLMs reveal clear capability limits, common failure patterns, and a significant performance gap compared to humans. Our work offers new insights into LLMs' imaginative reasoning and establishes a foundation for future research on exploratory agent behavior.
2026-03-13
AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Towards Agentic Intelligence for Materials Science
Huan Zhang
Yizhan Li
Wenhao Huang
Ziyu Hou
Yu Song
Xuye Liu
Farshid Effaty
Jinya Jiang
Sifan Wu
Qianggang Ding
Izumi Takahara
Leonard R. MacGillivray
Teruyasu Mizoguchi
Tianshu Yu
Lizi Liao
Yuyu Luo
Yu Rong
Jia LI
Ying Diao
Heng Ji … (see 1 more)
Bang Liu
The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in d… (see more)iscovery requires moving beyond task-isolated, fine-tuned models toward agentic systems that plan, act, and learn across the full discovery loop. This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining, through domain adaptation and instruction tuning, to goal-conditioned agents interfacing with simulation and experimental platforms. Unlike prior reviews, we treat the entire process as an end-to-end system to be optimized for tangible discovery outcomes rather than proxy benchmarks. This perspective allows us to trace how upstream design choices-such as data curation and training objectives-can be aligned with downstream experimental success through effective credit assignment. To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science. We then analyze the field through two focused lenses: From the AI perspective, the survey details LLM strengths in pattern recognition, predictive analytics, and natural language processing for literature mining, materials characterization, and property prediction; from the materials science perspective, it highlights applications in materials design, process optimization, and the acceleration of computational workflows via integration with external tools (e.g., DFT, robotic labs). Finally, we contrast passive, reactive approaches with agentic design, cataloging current contributions while motivating systems that pursue long-horizon goals with autonomy, memory, and tool use. This survey charts a practical roadmap towards autonomous, safety-aware LLM agents aimed at discovering novel and useful materials.
2025-12-31
arXiv (preprint)
doi.org
arxiv.org