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arxiv: 2605.28405 · v1 · pith:YWNZIN7Snew · submitted 2026-05-27 · 💻 cs.AI

Measuring Progress Toward AGI: A Cognitive Framework

Ryan Burnell , Yumeya Yamamori , Orhan Firat , Kate Olszewska , Steph Hughes-Fitt , Oran Kelly , Isaac R. Galatzer-Levy , Meredith Ringel Morris
show 5 more authors
Allan Dafoe Alison M. Snyder Noah D. Goodman Matthew Botvinick Shane Legg
This is my paper

Pith reviewed 2026-06-29 11:58 UTC · model grok-4.3

classification 💻 cs.AI
keywords cognitive taxonomyAGI evaluationcognitive profilesevaluation protocolhuman cognitive facultiesAI progress measurementintelligence assessment
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The pith

A cognitive taxonomy of 10 faculties from psychology enables empirical profiles that track AI progress toward AGI.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes a framework that relates AI system capabilities directly to human cognitive abilities to replace subjective AGI claims with measurable data. It draws on psychology and cognitive science to define a taxonomy that breaks general intelligence into 10 specific faculties. An evaluation protocol then tests systems on targeted held-out tasks to produce a cognitive profile that reveals where each system stands relative to those faculties. If correct, this would let researchers and policymakers track development empirically and compare systems on a common basis rather than through vague assertions.

Core claim

We present a framework for understanding system capabilities in relation to human cognitive abilities. Drawing from decades of research in psychology, neuroscience, and cognitive science, we introduce a Cognitive Taxonomy that deconstructs general intelligence into 10 key cognitive faculties. We then propose a rigorous evaluation protocol in which a system's performance is measured across a suite of targeted, held-out cognitive tasks, generating a 'cognitive profile' that can be used to understand a system's strengths and weaknesses.

What carries the argument

The Cognitive Taxonomy of 10 faculties drawn from psychology literature, which underpins the evaluation protocol that tests systems on held-out tasks to generate comparable cognitive profiles.

If this is right

  • Different AI systems can be compared on a shared scale of cognitive strengths and weaknesses.
  • Development progress can be tracked by changes in the cognitive profile over successive versions.
  • Targeted improvements can focus on specific faculties that show gaps in the profile.
  • Governance decisions can rest on standardized empirical profiles instead of unsubstantiated claims.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the taxonomy proves incomplete for non-human forms of intelligence, profiles could systematically undervalue certain AI approaches.
  • The held-out task requirement could push benchmark creators toward more isolated tests that reduce overlap between faculties.
  • Widespread use might shift evaluation priorities from single-task leaderboards toward breadth across all 10 faculties.

Load-bearing premise

That a decomposition of intelligence into these 10 human cognitive faculties drawn from existing psychology literature supplies the right and sufficient basis for measuring progress toward AGI.

What would settle it

An AI system that demonstrates broad intelligent behavior whose capabilities cannot be accounted for or predicted by its measured performance across the 10 faculties in the taxonomy.

Figures

Figures reproduced from arXiv: 2605.28405 by Alison M. Snyder, Allan Dafoe, Isaac R. Galatzer-Levy, Kate Olszewska, Matthew Botvinick, Meredith Ringel Morris, Noah D. Goodman, Oran Kelly, Orhan Firat, Ryan Burnell, Shane Legg, Steph Hughes-Fitt, Yumeya Yamamori.

Figure 1
Figure 1. Figure 1: Overview of the 10 cognitive faculties. Faculties outlined in orange represent composite faculties. We begin with eight faculties capturing the basic building blocks of human cognition: Perception: The ability to extract and process sensory information from the environment. Generation: The ability to produce outputs such as speech, text, motor movements, and computer control actions. Attention: The ability… view at source ↗
Figure 2
Figure 2. Figure 2: Cognitive profiles for three hypothetical systems. Panel A: A hypothetical system that shows significant cognitive weaknesses relative to the human sample. Panel B: A hypothetical system that outperforms the human sample median across all cognitive faculties. Panel C: A hypothetical system that outperforms the human sample maximum across all cognitive faculties. The hypothetical human sample scores are sta… view at source ↗
read the original abstract

Despite widespread discussion of AGI, there is no clear framework for measuring progress toward it. This ambiguity fuels subjective claims, makes it difficult to track progress, and risks hindering responsible governance. As a starting point to address this gap, we present a framework for understanding system capabilities in relation to human cognitive abilities. Drawing from decades of research in psychology, neuroscience, and cognitive science, we introduce a Cognitive Taxonomy that deconstructs general intelligence into 10 key cognitive faculties. We then propose a rigorous evaluation protocol in which a system's performance is measured across a suite of targeted, held-out cognitive tasks, generating a 'cognitive profile' that can be used to understand a system's strengths and weaknesses. We hope this framework will provide a practical roadmap and an initial step toward more rigorous, empirical evaluation of AGI.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims there is no clear framework for measuring AGI progress and proposes a Cognitive Framework that introduces a Cognitive Taxonomy deconstructing general intelligence into 10 key faculties drawn from psychology, neuroscience, and cognitive science literature, paired with a rigorous evaluation protocol that measures system performance on targeted held-out tasks to produce a 'cognitive profile' for tracking strengths, weaknesses, and progress toward AGI.

Significance. If adopted and empirically validated, the framework could supply a structured, literature-grounded approach to AGI evaluation that reduces subjective claims and supports governance; the explicit use of held-out tasks and the proposal of cognitive profiles are constructive elements that could enable falsifiable comparisons across systems.

major comments (2)
  1. [Cognitive Taxonomy] Cognitive Taxonomy section: the manuscript asserts that the 10 faculties provide a sufficient basis for measuring AGI progress but supplies no argument or evidence that this decomposition is exhaustive, that the faculties are independent, or that it is preferable to non-human-centric alternatives (e.g., scaling-law clusters or emergent capability taxonomies). This choice is load-bearing for the central claim that the resulting profiles constitute a useful measure of AGI progress.
  2. [Evaluation Protocol] Evaluation Protocol section: the protocol is described as 'rigorous' yet the text contains no validation experiments, inter-task correlation analysis, or sensitivity checks demonstrating that the held-out tasks yield stable, taxonomy-independent profiles. Without such evidence the claim that the protocol enables reliable empirical tracking of AGI progress remains untested.
minor comments (1)
  1. [Abstract] Abstract: the phrasing 'we hope this framework will provide a practical roadmap' could be tightened to clarify that the contribution is a proposal rather than a demonstrated method.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the intended scope of our proposed framework. We address each major comment below, indicating planned revisions to avoid overstating claims while preserving the manuscript's focus as an initial step toward structured AGI evaluation.

read point-by-point responses

  1. Referee: [Cognitive Taxonomy] Cognitive Taxonomy section: the manuscript asserts that the 10 faculties provide a sufficient basis for measuring AGI progress but supplies no argument or evidence that this decomposition is exhaustive, that the faculties are independent, or that it is preferable to non-human-centric alternatives (e.g., scaling-law clusters or emergent capability taxonomies). This choice is load-bearing for the central claim that the resulting profiles constitute a useful measure of AGI progress.

    Authors: We agree that the manuscript should more explicitly bound the claims of the Cognitive Taxonomy. The 10 faculties are selected from established literature in psychology, neuroscience, and cognitive science to provide a human-centric starting point for evaluation, not as an exhaustive or mutually independent decomposition. We make no assertion of superiority to alternative approaches such as scaling-law clusters. In the revised manuscript, we will add explicit language in the Cognitive Taxonomy section stating these limitations and framing the taxonomy as a practical, literature-grounded proposal rather than a definitive basis. revision: yes

  2. Referee: [Evaluation Protocol] Evaluation Protocol section: the protocol is described as 'rigorous' yet the text contains no validation experiments, inter-task correlation analysis, or sensitivity checks demonstrating that the held-out tasks yield stable, taxonomy-independent profiles. Without such evidence the claim that the protocol enables reliable empirical tracking of AGI progress remains untested.

    Authors: We acknowledge that the evaluation protocol is a design proposal without empirical validation or sensitivity analysis in the current manuscript. The description as 'rigorous' refers to its use of targeted, held-out tasks rather than demonstrated reliability. We will revise the Evaluation Protocol section to refer to a 'proposed evaluation protocol' and include a forward-looking discussion noting that empirical validation, including inter-task correlations and stability checks, is required in future work to support reliable tracking of progress. revision: yes

Circularity Check

0 steps flagged

No circularity: proposal draws taxonomy and protocol from external literature without self-referential reductions

full rationale

The paper presents a framework proposal that deconstructs intelligence into 10 faculties drawn from existing psychology/neuroscience literature and outlines a held-out task evaluation protocol. No equations, fitted parameters, or 'predictions' appear that reduce by construction to the paper's own inputs. The taxonomy is explicitly sourced from decades of independent prior research rather than defined in terms of the proposed profile or any self-citation chain. No uniqueness theorems, ansatzes, or renamings of known results are invoked in a load-bearing way. The central claim is therefore a self-contained definitional proposal, not a derivation that collapses into its premises.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper rests on the domain assumption that human cognitive faculties are the appropriate reference class for AGI measurement and introduces no free parameters, new physical entities, or formal derivations.

axioms (1)
  • domain assumption Human cognitive abilities, as studied in psychology and neuroscience, supply the correct decomposition for measuring general intelligence in artificial systems.
    Invoked in the abstract when the authors state they draw from decades of research in psychology, neuroscience, and cognitive science to define the 10 faculties.

pith-pipeline@v0.9.1-grok · 5710 in / 1228 out tokens · 28575 ms · 2026-06-29T11:58:11.824100+00:00 · methodology

discussion (0)

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