mlsanityData to 14 Oct 2026

Taming the Centaur(s) with LAPITHS: a framework for a theoretically grounded interpretation of AI performances

arXiv 2604.27927 ↗First posted 30 Apr 2026cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

We introduce a framework called LAPITHS (Language model Analysis through Paradigm grounded Interpretations of Theses about Human likenesS) and use it to show that several major claims advanced by models such as CENTAUR, proposed as an artificial Unified Model of Cognition, are not theoretically or empirically justified. LAPITHS provides a principled reference point for counteracting the current behaviouristic tendency in AI research to interpret the human level performances of transformer based language models as evidence of human like underlying computation and, by extension, as signs of cognitive abilities. The novelty of LAPITHS lies in making explicit the arguments grounded in two quantitative assessments: (i) the Minimal Cognitive Grid, a theoretically motivated method for estimating the cognitive plausibility of artificial systems, and (ii) a behavioural comparison showing that results similar to those reported for CENTAUR like models can be reproduced by other systems that do not satisfy the structural constraints typically associated with cognitive plausibility, and whose outputs do not provide independent explanatory insight into human cognition.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 86 references that are in the map. First posted on arXiv 30 Apr 2026.

Builds on

17 of 86 references.

References · 17

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  5. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  6. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  7. 2018World Modelscited by 1,513
  8. 2025Large Language Diffusion Modelscited by 912
  9. 2022Using cognitive psychology to understand GPT-3cited by 398
  10. 2025Memory in the Age of AI Agentscited by 303
  11. 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactionscited by 254
  12. 2025Epistemological Fault Lines Between Human and Artificial Intelligencecited by 11
5 more
  1. 2025Large Language Model probabilities cannot distinguish between possible and impossible languagecited by 5
  2. 2025World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Childcited by 4
  3. 2025How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMscited by 3
  4. 2025Not Even Wrong: On the Limits of Prediction as Explanation in Cognitive Sciencecited by 3
  5. 2025Addressing Longstanding Challenges in Cognitive Science with Language Modelscited by 3

Built on by

Not cited yet.