arXiv AI
Generalizing the Turing Test to Interactive Agents
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arXiv AI
Aug 11
A Rigorous Turing Test: a Foundation for Evaluating Artificial General Intelligence
arXiv:2501. 17629v2 Announce Type: replace-cross Abstract: Several studies claim that large language models have passed the Turing Test and hence can "think", yet none follow Turing's original instructions precisely.
arXiv AI
Aug 7
Turing's Frist Imitation Game: Design Concepts and a Human-Approximates-Machine Reading
arXiv:2608. 05558v1 Announce Type: cross Abstract: This paper examines Turing's 1948 report, "Intelligent Machinery", as an important conceptual source for the later imitation games.
arXiv AI
Sep 10
Turing's First Imitation Game: Design Concepts and a Human-Approximates-Machine Reading
The paper analyzes Turing’s 1948 report "Intelligent Machinery" as a foundational source for later imitation games, highlighting key design concepts such as the possibility of machine errors, the exclusion of irrelevant physical traits, the role of a human judge, and Turing’s view that intellectual activity is largely search. It argues that limiting the human contestant to a weak chess player heightens the importance of intellectual search, making human behavior more comparable to machine behavior. This reframes the 1948 game as a human‑approximates‑machine scenario, suggesting that imitation games can probe when human intelligence becomes machine‑like under specific task constraints.
arXiv AI
Aug 25
The Imitator Game: Benchmarking Robot Imitative Ability Beyond Action Prediction
arXiv:2608.22301v1 Announce Type: cross Abstract: Humans imitate at the level of intent: given a demonstration, we infer its goal and carry it out with whatever tools, objects, and layouts are at han...
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.
Hugging Face Trending Papers
Jun 22
On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners
Large Language Models (LLMs) are frequently portrayed as general-purpose solvers capable of solving arbitrary tasks. We argue that this view overlooks a fundamental constraint: language is a compressed and capacity-limited interface for conveying task information.