arXiv AI
Taming the Centaur(s) with LAPITHS: a framework for a theoretically grounded interpretation of AI performances
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arXiv AI
Sep 1
Do Large Language Models Possess a Theory of Mind? A Comparative Evaluation Using the Strange Stories Paradigm
arXiv:2603.18007v2 Announce Type: replace-cross Abstract: The study explores whether current Large Language Models (LLMs) exhibit Theory of Mind (ToM) capabilities -- specifically, the ability to inf...
arXiv AI
Sep 21
CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition
CogGym is a scalable, unified framework that standardizes diverse cognitive experiments into a task‑agnostic Experiment Markup Language (EML) for systematic comparison of human and AI behavior. The initial release curates 258 experiments from 100 papers focused on human commonsense reasoning and evaluates 50 large language models, revealing a scaling trend where larger models better reproduce human judgments but still lag far behind human split‑half reliability. The framework aims to continually incorporate new cognitive science experiments to track where model behavior aligns with or diverges from human cognition as models evolve.
arXiv Computation and Language
Sep 22
Is Imagination Derived from Hallucination? A Cross-Taxonomy Evaluation of Imagination and Hallucination in Large Language Models
arXiv:2609.22152v1 Announce Type: new Abstract: Imagination performs as a high-level function of large language models (LLMs) which determines the potential of how an LLM creates unseen or creative c...
arXiv AI
Jun 30
Situation Perception: A Necessary Primitive to Artificial Superintelligence
arXiv:2606. 30481v1 Announce Type: cross Abstract: Current large language models are extraordinary statistical engines.
arXiv AI
Aug 13
On Benchmarking Human-Like Intelligence in Machines
arXiv:2502. 20502v2 Announce Type: replace Abstract: Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition.
arXiv AI
Sep 10
Sparks of In Silico Cognitive Science: Theories from Simulated Data Can Generalize to Humans
arXiv:2609.08003v1 Announce Type: new Abstract: Behavioral foundation models have been proposed as stand-ins for human participants across settings, but it is unclear whether theories discovered on t...