Taming the Centaur(s) with LAPITHS: a framework for a theoretically grounded interpretation of AI performances
- URL: http://arxiv.org/abs/2604.27927v1
- Date: Thu, 30 Apr 2026 14:29:50 GMT
- Title: Taming the Centaur(s) with LAPITHS: a framework for a theoretically grounded interpretation of AI performances
- Abstract summary: We introduce a framework called LAPITHS (Language model Analysis through Paradigm grounded Interpretations of Theses about Human likenesS)<n>We 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Generative Interpretability via Scalable Neuro-Symbolic Models [2.1550607598300617]
We argue for a shift toward generative interpretability.<n>We show the merits of generative interpretability as comparison to other interpretability research paradigms.
arXiv Detail & Related papers (2026-09-11T20:54:08Z) - Cognitive Profiling of LRMs' Reasoning Traces Using Bloom's Taxonomy [51.08110837378928]
We introduce a framework for automatic annotation of reasoning steps through the lens of Bloom's taxonomy.<n>We perform a large-scale analysis across models and datasets, revealing both similarities and differences in thinking patterns across models and tasks.<n>Our findings establish a fine-grained framework for analyzing thinking patterns in LRMs and provide actionable insights for enhancing reasoning quality.
arXiv Detail & Related papers (2026-08-24T12:55:51Z) - The Universal Landscape of Human Reasoning [60.72403709545137]
We introduce Information Flow Tracking (IF-Track) to quantify information entropy and gain at each reasoning step.<n>We show that IF-Track captures essential reasoning features, identifies systematic error patterns, and characterizes individual differences.<n>This approach establishes a quantitative bridge between theory and measurement, offering mechanistic insights into the architecture of reasoning.
arXiv Detail & Related papers (2025-10-24T16:26:36Z) - Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models [93.1043186636177]
We explore the hypothesis that people use a combination of distributed and symbolic representations to construct bespoke mental models tailored to novel situations.<n>We propose a computational implementation of this idea -- a Model Synthesis Architecture''<n>We evaluate our MSA as a model of human judgments on a novel reasoning dataset.
arXiv Detail & Related papers (2025-07-16T18:01:03Z) - Bhatt Conjectures: On Necessary-But-Not-Sufficient Benchmark Tautology for Human Like Reasoning [0.0]
Bhatt Conjectures framework introduces rigorous, hierarchical benchmarks for evaluating AI reasoning and understanding.<n>Agentreasoning-sdk demonstrates practical implementation, revealing that current AI models struggle with complex reasoning tasks.
arXiv Detail & Related papers (2025-06-13T02:41:18Z) - I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data? [76.15163242945813]
Large language models (LLMs) have led many to conclude that they exhibit a form of intelligence.<n>We introduce a novel generative model that generates tokens on the basis of human-interpretable concepts represented as latent discrete variables.
arXiv Detail & Related papers (2025-03-12T01:21:17Z) - Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
Benchmarks are plagued by various biases, artifacts, or leakage.<n>Models may behave unreliably due to poorly explored failure modes.<n> causality offers an ideal framework to systematically address these challenges.
arXiv Detail & Related papers (2025-02-07T17:01:37Z) - On the Reasoning Capacity of AI Models and How to Quantify It [0.0]
Large Language Models (LLMs) have intensified the debate surrounding the fundamental nature of their reasoning capabilities.<n>While achieving high performance on benchmarks such as GPQA and MMLU, these models exhibit limitations in more complex reasoning tasks.<n>We propose a novel phenomenological approach that goes beyond traditional accuracy metrics to probe the underlying mechanisms of model behavior.
arXiv Detail & Related papers (2025-01-23T16:58:18Z) - A Novel Neural-symbolic System under Statistical Relational Learning [47.30190559449236]
We propose a neural-symbolic framework based on statistical relational learning, referred to as NSF-SRL.<n>Results of symbolic reasoning are utilized to refine and correct the predictions made by deep learning models, while deep learning models enhance the efficiency of the symbolic reasoning process.<n>We believe that this approach sets a new standard for neural-symbolic systems and will drive future research in the field of general artificial intelligence.
arXiv Detail & Related papers (2023-09-16T09:15:37Z) - Learning a Structural Causal Model for Intuition Reasoning in
Conversation [20.243323155177766]
Reasoning, a crucial aspect of NLP research, has not been adequately addressed by prevailing models.
We develop a conversation cognitive model ( CCM) that explains how each utterance receives and activates channels of information.
By leveraging variational inference, it explores substitutes for implicit causes, addresses the issue of their unobservability, and reconstructs the causal representations of utterances through the evidence lower bounds.
arXiv Detail & Related papers (2023-05-28T13:54:09Z) - Logical Satisfiability of Counterfactuals for Faithful Explanations in
NLI [60.142926537264714]
We introduce the methodology of Faithfulness-through-Counterfactuals.
It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation.
It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
arXiv Detail & Related papers (2022-05-25T03:40:59Z) - Understanding Interpretability by generalized distillation in Supervised
Classification [3.5473853445215897]
Recent interpretation strategies focus on human understanding of the underlying decision mechanisms of the complex Machine Learning models.
We propose an interpretation-by-distillation formulation that is defined relative to other ML models.
We evaluate our proposed framework on the MNIST, Fashion-MNIST and Stanford40 datasets.
arXiv Detail & Related papers (2020-12-05T17:42:50Z) - A Meta-Bayesian Model of Intentional Visual Search [0.0]
We propose a computational model of visual search that incorporates Bayesian interpretations of the neural mechanisms that underlie categorical perception and saccade planning.
To enable meaningful comparisons between simulated and human behaviours, we employ a gaze-contingent paradigm that required participants to classify occluded MNIST digits through a window that followed their gaze.
Our model is able to recapitulate human behavioural metrics such as classification accuracy while retaining a high degree of interpretability, which we demonstrate by recovering subject-specific parameters from observed human behaviour.
arXiv Detail & Related papers (2020-06-05T16:10:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.