Tootfinder

Supernova Event Dataset: Interpreting Large Language Model's Personality through Critical Event Analysis
Pranav Agarwal, Ioana Ciuc\u{a}
https://arxiv.org/abs/2506.12189

Supernova Event Dataset: Interpreting Large Language Model's Personality through Critical Event Analysis
Large Language Models (LLMs) are increasingly integrated into everyday applications. As their influence grows, understanding their decision making and underlying personality becomes essential. In this work, we interpret model personality using our proposed Supernova Event Dataset, a novel dataset with diverse articles spanning biographies, historical events, news, and scientific discoveries. We use this dataset to benchmark LLMs on extracting and ranking key events from text, a subjective and c…

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Shova Kuikel, Aritran Piplai, Palvi Aggarwal
https://arxiv.org/abs/2506.13746

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Phishing attacks remain one of the most prevalent and persistent cybersecurity threat with attackers continuously evolving and intensifying tactics to evade the general detection system. Despite significant advances in artificial intelligence and machine learning, faithfully reproducing the interpretable reasoning with classification and explainability that underpin phishing judgments remains challenging. Due to recent advancement in Natural Language Processing, Large Language Models (LLMs) sho…

The Limits of Tractable Marginalization
Oliver Broadrick, Sanyam Agarwal, Guy Van den Broeck, Markus Bl\"aser
https://arxiv.org/abs/2506.12020 https:/…

The Limits of Tractable Marginalization
Marginalization -- summing a function over all assignments to a subset of its inputs -- is a fundamental computational problem with applications from probabilistic inference to formal verification. Despite its computational hardness in general, there exist many classes of functions (e.g., probabilistic models) for which marginalization remains tractable, and they can be commonly expressed by polynomial size arithmetic circuits computing multilinear polynomials. This raises the question, can all…

Directed Acyclic Graph Convolutional Networks
Samuel Rey, Hamed Ajorlou, Gonzalo Mateos
https://arxiv.org/abs/2506.12218 https://arxi…

Directed Acyclic Graph Convolutional Networks
Directed acyclic graphs (DAGs) are central to science and engineering applications including causal inference, scheduling, and neural architecture search. In this work, we introduce the DAG Convolutional Network (DCN), a novel graph neural network (GNN) architecture designed specifically for convolutional learning from signals supported on DAGs. The DCN leverages causal graph filters to learn nodal representations that account for the partial ordering inherent to DAGs, a strong inductive bias d…

Downstream: efficient cross-platform algorithms for fixed-capacity stream downsampling
Connor Yang, Joey Wagner, Emily Dolson, Luis Zaman, Matthew Andres Moreno
https://arxiv.org/abs/2506.12975

Downstream: efficient cross-platform algorithms for fixed-capacity stream downsampling
Due to ongoing accrual over long durations, a defining characteristic of real-world data streams is the requirement for rolling, often real-time, mechanisms to coarsen or summarize stream history. One common data structure for this purpose is the ring buffer, which maintains a running downsample comprising most recent stream data. In some downsampling scenarios, however, it can instead be necessary to maintain data items spanning the entirety of elapsed stream history. Fortunately, approaches g…

Measurement-based quantum computation with variable-range interacting systems
Debkanta Ghosh, Keshav Das Agarwal, Pritam Halder, Aditi Sen De
https://arxiv.org/abs/2506.11909

Measurement-based quantum computation with variable-range interacting systems
We demonstrate that weighted graph states (WGS) generated via variable-range interacting Ising spin systems where the interaction strength decays with distance as a power law, characterized by the fall-off rate, can successfully implement single- and two-qubit gates with fidelity exceeding classical limits by performing suitable measurements. In the regime of truly long-range interactions (small fall-off rate), optimizing over local unitary operations, while retaining the local measurement sche…

Early Modern Women: Figures, Labors, Afterlives (RSA 2026) #acrel https://call-for-papers.sas.upenn.edu/cfp/2025/06/12/early-modern-women-figur…

Foreclosure, a Special Issue of Key Words: A Journal of Cultural Materialism #acrel https://call-for-papers.sas.upenn.edu/…

Pulse vol 13 (2026) CFP - Zines and STS: The Remix #acrel https://call-for-papers.sas.upenn.edu/cfp/2025/06/12/pulse-vol-13-2026-cfp-zines-and-sts-the-…

NeMLA 2026 Panel Session: The Murder and Its Afterlife: Regenerating the Wicked Witch #acrel https://call-for-papers.sas.upenn.edu/