Tootfinder

Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education
Ruiwei Xiao, Xinying Hou, Runlong Ye, Majeed Kazemitabaar, Nicholas Diana, Michael Liut, John Stamper
https://arxiv.org/abs/2506.19107

Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education
With the proliferation of large language model (LLM) applications since 2022, their use in education has sparked both excitement and concern. Recent studies consistently highlight students' (mis)use of LLMs can hinder learning outcomes. This work aims to teach students how to effectively prompt LLMs to improve their learning. We first proposed pedagogical prompting, a theoretically-grounded new concept to elicit learning-oriented responses from LLMs. To move from concept design to a proof-of-co…

Citation Recommendation using Deep Canonical Correlation Analysis
Conor McNamara, Effirul Ramlan
https://arxiv.org/abs/2507.17603 https://arxiv.org/pdf/250…

Citation Recommendation using Deep Canonical Correlation Analysis
Recent advances in citation recommendation have improved accuracy by leveraging multi-view representation learning to integrate the various modalities present in scholarly documents. However, effectively combining multiple data views requires fusion techniques that can capture complementary information while preserving the unique characteristics of each modality. We propose a novel citation recommendation algorithm that improves upon linear Canonical Correlation Analysis (CCA) methods by applyi…

TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models
Zijian Zhang, Pan Chen, Fangshi Du, Runlong Ye, Oliver Huang, Michael Liut, Al\'an Aspuru-Guzik
https://arxiv.org/abs/2507.18945

TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models
Efficiently navigating and understanding academic papers is crucial for scientific progress. Traditional linear formats like PDF and HTML can cause cognitive overload and obscure a paper's hierarchical structure, making it difficult to locate key information. While LLM-based chatbots offer summarization, they often lack nuanced understanding of specific sections, may produce unreliable information, and typically discard the document's navigational structure. Drawing insights from a formative st…

Geometric fragmentation and anomalous thermalization in cubic dimer model
Joel Steinegger, Debasish Banerjee, Emilie Huffman, Lukas Rammelm\"uller
https://arxiv.org/abs/2508.03802

Geometric fragmentation and anomalous thermalization in cubic dimer model
While quantum statistical mechanics triumphs in explaining many equilibrium phenomena, there is an increasing focus on going beyond conventional scenarios of thermalization. Traditionally examples of non-thermalizing systems are either integrable, or disordered. Recently, examples of translationally-invariant physical systems have been discovered whose excited energies avoid thermalization either due to local constraints (whether exact or emergent), or due to higher-form symmetries. In this art…