Multimodal LLMs reliably solve many CAPTCHA tasks but can be defended by adding fine-grained localization and implicit counting that drops state-of-the-art success from over 95% to 0%.
LLM-Based Class Diagram Derivation from User Stories with Chain-of-Thought Promptings
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We use a result on mixed Tate motives due to Goncharov (arXiv:alg-geom/9601021) to show that the symbol of an arbitrary one-loop 2m-gon integral in 2m dimensions may be read off directly from its Feynman parameterization. The algorithm proceeds via recursion in m seeded by the well-known box integrals in four dimensions. As a simple application of this method we write down the symbol of a three-mass hexagon integral in six dimensions.
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roles
background 3representative citing papers
Large-scale review mining of 1M+ comments from 171 Gen-AI apps using an LLM framework reveals top topics plus three opportunities and three challenges for developers.
COMPASS formalizes HPC configuration questions as ML tasks on traces, quantifies recommendation trustworthiness, and delivers 65.93% lower average job turnaround time plus 80.93% lower node usage versus prior methods in simulator tests.
The survey organizes mechanistic interpretability techniques into a Locate-Steer-Improve framework to enable actionable improvements in LLM alignment, capability, and efficiency.
An online study of 70 students found that gender, race, and self-efficacy predict distinct ChatGPT query patterns during essay writing, with patterns linked to enjoyment and perceived ownership of the final essay.
Preference-based prompting raises LLM adherence to object-oriented design principles in UML generation but leaves substantial output variance and model-specific differences intact.
Survey and interview study finds neurodivergent computing students prefer structured collaborative active learning with small teams and explicit roles.
citing papers explorer
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COGNITION: From Evaluation to Defense against Multimodal LLM CAPTCHA Solvers
Multimodal LLMs reliably solve many CAPTCHA tasks but can be defended by adding fine-grained localization and implicit counting that drops state-of-the-art success from over 95% to 0%.
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Understanding the Challenges and Opportunities of Generative AI Apps: An Empirical Study
Large-scale review mining of 1M+ comments from 171 Gen-AI apps using an LLM framework reveals top topics plus three opportunities and three challenges for developers.
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COMPASS: A Unified Decision-Intelligence System for Navigating Performance Trade-off in HPC
COMPASS formalizes HPC configuration questions as ML tasks on traces, quantifies recommendation trustworthiness, and delivers 65.93% lower average job turnaround time plus 80.93% lower node usage versus prior methods in simulator tests.
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Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models
The survey organizes mechanistic interpretability techniques into a Locate-Steer-Improve framework to enable actionable improvements in LLM alignment, capability, and efficiency.
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An Empirical Study to Understand How Students Use ChatGPT for Writing Essays
An online study of 70 students found that gender, race, and self-efficacy predict distinct ChatGPT query patterns during essay writing, with patterns linked to enjoyment and perceived ownership of the final essay.
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Reliability of Large Language Models for Design Synthesis: An Empirical Study of Variance, Prompt Sensitivity, and Method Scaffolding
Preference-based prompting raises LLM adherence to object-oriented design principles in UML generation but leaves substantial output variance and model-specific differences intact.
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"I can't read your mind": A Study of Neurodivergent Computing Students' Experiences with Collaborative Active Learning
Survey and interview study finds neurodivergent computing students prefer structured collaborative active learning with small teams and explicit roles.