Humans generalize zero-shot to appearance-free action videos, and a two-pathway CNN model with coherence-gating outperforms standard video models while matching this behavior.
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The authors introduce Agentivism as a learning theory for human-AI interaction that explains how durable capability develops through selective delegation, epistemic monitoring, reconstructive internalization, and transfer under reduced support.
LLMs achieve higher accuracy than humans on compositional imagery tasks previously argued to require pictorial representations, supporting emergent propositional mental imagery in AI.
Presents a configurable variability-based framework for LLM-assisted naming of formal concepts in FCA and RCA, illustrated on a small pizzeria relational dataset.
Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
An LLM proposal loop with deterministic validation builds unit-weighted N-of-M clinical checklists that achieve AUROC comparable to flexible interpretable models on eight EHR tasks.
PAFER estimates statistical parity for differentially private decision trees using Laplacian noise, achieving low error while preserving privacy and favoring interpretable trees.
Gini index of BERTopic topic distributions on COVID-19 Reddit data shows significant global correlation with fake news fraction but ambiguous results at community level.
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Appearance-free Action Recognition: Zero-shot Generalization in Humans and a Two-Pathway Model
Humans generalize zero-shot to appearance-free action videos, and a two-pathway CNN model with coherence-gating outperforms standard video models while matching this behavior.
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Agentivism: a learning theory for the age of artificial intelligence
The authors introduce Agentivism as a learning theory for human-AI interaction that explains how durable capability develops through selective delegation, epistemic monitoring, reconstructive internalization, and transfer under reduced support.
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Artificial Phantasia: Emergent Mental Imagery in Large Language Models
LLMs achieve higher accuracy than humans on compositional imagery tasks previously argued to require pictorial representations, supporting emergent propositional mental imagery in AI.
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A Variability-Based Framework for Interpretable Naming in Formal and Relational Concept Analysis
Presents a configurable variability-based framework for LLM-assisted naming of formal concepts in FCA and RCA, illustrated on a small pizzeria relational dataset.
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Relationships Between Trust, Compliance, and Performance for Novice Programmers Using AI Code Generation
Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
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Automatic Construction of Clinical Scoring Systems with LLM Agents
An LLM proposal loop with deterministic validation builds unit-weighted N-of-M clinical checklists that achieve AUROC comparable to flexible interpretable models on eight EHR tasks.
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Privacy Constrained Fairness Estimation for Decision Trees
PAFER estimates statistical parity for differentially private decision trees using Laplacian noise, achieving low error while preserving privacy and favoring interpretable trees.
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Quantifying correlations between information overload and fake news during COVID-19 pandemic: a Reddit study with BERT model approach
Gini index of BERTopic topic distributions on COVID-19 Reddit data shows significant global correlation with fake news fraction but ambiguous results at community level.