# Measuring Progress Toward AGI: A Cognitive Framework

arXiv: https://arxiv.org/abs/2605.28405 · HTML: https://mlsanity.com/paper/2605.28405/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 171 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 17 of 17)

- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Perception Test: A Diagnostic Benchmark for Multimodal Video Models](https://mlsanity.com/paper/2305.13786/) (2023)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systems](https://mlsanity.com/paper/2505.11831/) (2025)
- [Language models show human-like content effects on reasoning tasks](https://mlsanity.com/paper/2207.07051/) (2022)
- [GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks](https://mlsanity.com/paper/2510.04374/) (2025)
- [Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks](https://mlsanity.com/paper/2305.10160/) (2023)
- [Levels of AGI for Operationalizing Progress on the Path to AGI](https://mlsanity.com/paper/2311.02462/) (2023)
- [SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge](https://mlsanity.com/paper/2509.07968/) (2025)
- [Meta-learning in natural and artificial intelligence](https://mlsanity.com/paper/2011.13464/) (2020)
- [The AGI Containment Problem](https://mlsanity.com/paper/1604.00545/) (2016)
- [A Definition of AGI](https://mlsanity.com/paper/2510.18212/) (2025)
- [Large Language Models Lack Understanding of Character Composition of Words](https://mlsanity.com/paper/2405.11357/) (2024)
- [Remote Labor Index: Measuring AI Automation of Remote Work](https://mlsanity.com/paper/2510.26787/) (2025)
- [The FACTS Leaderboard: A Comprehensive Benchmark for Large Language Model Factuality](https://mlsanity.com/paper/2512.10791/) (2025)
- [Evaluating Alignment of Behavioral Dispositions in LLMs](https://mlsanity.com/paper/2602.11328/) (2026)
- [Capabilities Ain't All You Need: Measuring Propensities in AI](https://mlsanity.com/paper/2602.18182/) (2026)

## Built on by (most cited first, 6 of 6)

- [CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model](https://mlsanity.com/paper/2606.06099/) (2026)
- [Epistemic Stance Flexibility Probing: Measuring Prompt-Conditioned Register Shift in Large Language Models](https://mlsanity.com/paper/2607.12739/) (2026)
- [The Autonomous Agency Scale: A Behavioral Framework for Measuring Self-Directed Behavior in AI Systems](https://mlsanity.com/paper/2607.17947/) (2026)
- [Gubernaut: A Deterministic Homeostatic Controller for Affect-Regulated LLM Agents, Validated Across Independent Model Families](https://mlsanity.com/paper/2607.24339/) (2026)
- [Using profiles of cognitive capability to assess AI suitability for workplace tasks](https://mlsanity.com/paper/2608.25623/) (2026)
- [Thought without systematicity? Evaluating reasoning models on rule induction tasks](https://mlsanity.com/paper/2609.13948/) (2026)

## Abstract (arXiv)

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.