arXiv AI By Sharon Temtsin, Diane Proudfoot, David Kaber, Christoph Bartneck

A Rigorous Turing Test: a Foundation for Evaluating Artificial General Intelligence

Read the original on arXiv AI →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI
Aug 3

Shall We Play a Game? Language Models for Open-ended Wargames

arXiv:2509. 17192v3 Announce Type: replace Abstract: LLM-based social simulations can make a generated transcript look like a single behavioral signal, but the model behind that transcript may be doing several different jobs: choosing what an actor says or does, deciding what happens after an action, or both.

By Glenn Matlin, Isaac Song, Yixiong Hao, Parv Mahajan, Evan Montoya, Ryan Bard, Stuart R. Topp, Anthony Wen-Ming Zang, Mohammed Rehan Parwani, Soham Shetty, Mark Riedl
arXiv AI
6d ago

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.

By Lance Ying, Katherine M. Collins, Lionel Wong, Ilia Sucholutsky, Ryan Liu, Adrian Weller, Tianmin Shu, Thomas L. Griffiths, Joshua B. Tenenbaum