mlsanityData to 7 Oct 2026

Process-Constituted Intelligence: A Shared Criterion for Humans and Machines

arXiv 2608.16213 ↗First posted 17 Aug 2026cs.AI cs.ETcited by 0

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Abstract (arXiv)

Intelligence is constituted by textitprocess (iterative activity through which output emerges), not in the output itself. Generative AI (GenAI) is trained on textittraces (textual and visual residues of human cognitive processes), reproducing samples from a distribution of those traces. Its outputs resemble reasoning, problem-solving, and creativity, yet the activity that produces such outputs in humans remains largely absent. Current GenAI is, therefore, weakly equivalent to the cognition it imitates, matching outputs while process stays absent or opaque. The cognitive sciences have long distinguished between weak and strong equivalence. Here, we define textitstrong equivalence across seven process features, assessable against human and machine cognition. Our process-based account addresses a symmetric risk: GenAI tools that outsource a person's generative processes may leave critical capacities unbuilt. We specify design principles for GenAI that instantiate more process and preserve rather than erode human judgment and creativity, and outline process audits that make strong equivalence testable.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 70 references that are in the map. First posted on arXiv 17 Aug 2026.

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References · 26

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  5. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  6. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  7. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  8. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
  9. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,081
  10. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 668
  11. 2025Reasoning Models Don't Always Say What They Thinkcited by 388
  12. 2025Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safetycited by 222
14 more
  1. 2023Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMscited by 103
  2. 2025Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Taskcited by 103
  3. 2025Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivitycited by 93
  4. 2025When Chain of Thought is Necessary, Language Models Struggle to Evade Monitorscited by 81
  5. 2025Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?cited by 74
  6. 2024Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performancecited by 58
  7. 2025Stop Overvaluing Multi-Agent Debate -- We Must Rethink Evaluation and Embrace Model Heterogeneitycited by 43
  8. 2025Can LLM Agents Really Debate? A Controlled Study of Multi-Agent Debate in Logical Reasoningcited by 27
  9. 2021Technological Approach to Mind Everywhere (TAME): an experimentally-grounded framework for understanding diverse bodies and mindscited by 25
  10. 2024Six Fallacies in Substituting Large Language Models for Human Participantscited by 24
  11. 2026How AI Impacts Skill Formationcited by 20
  12. 2025Teaching Models to Verbalize Reward Hacking in Chain-of-Thought Reasoningcited by 19
  13. 2025Large Language Models Do Not Simulate Human Psychologycited by 17
  14. 2025Levels of Analysis for Large Language Modelscited by 9

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