Aug 2026· Nature Communications· Vol 17· 1 citation· 62 references
Medicine
TL;DR
It is shown that AI-discovered strategies propagate and persist in human populations, producing cultural shifts when non-trivial, learnable, and advantageous.
Abstract
Intelligent machines have the potential to uncover problem-solving strategies beyond human discovery. Emerging evidence from competitive gameplay, such as Go and chess, demonstrates that AI systems are evolving from mere tools to sources of cultural innovation adopted by humans. However, the conditions under which intelligent machines transition from tools to drivers of persistent cultural change remain unclear. We identify three key dimensions that modulate machine influence on human problem-solving: the discovered strategies must be non-trivial, learnable, and offer a clear advantage. Using a cultural transmission experiment, we demonstrate that when these conditions are met, machine-discovered strategies can be transmitted, understood, and preserved by human populations, leading to enduring cultural shifts. Conversely, using agent-based simulations, we show how machine influence is constrained in the absence of these conditions. These findings provide a framework for understanding how machines can persistently expand human cognitive skills and underscore the need to consider their broader implications for human cognition and cultural evolution. AI can uncover problem-solving strategies beyond human discovery. Here, the authors show that AI-discovered strategies propagate and persist in human populations, producing cultural shifts when non-trivial, learnable, and advantageous.
How do humans and artificial agents learn to act in a world whose rules they do not yet know? We use LS20, a novel ARC-AGI-3 game, as an artificial world: a microcosm whose mechanics must be discovered through exploration and improvisation alone. We operationalise a methodological loop: observe how humans explore, adapt, and become flexible; extract principles of adaptive coupling; build artificial systems embodying those principles; and let the comparison reveal where the artificial model still diverges. By analysing the completed trajectories of 18 human players, we identify a descriptive bottleneck level for each player: a level that consumes a disproportionate share of total actions, after which many trajectories become more efficient. The clearest temporal effect is that players pause 2.1× longer after actions that change a distal reference pattern, consistent with state checking after a causal intervention. Our current artificial agent also solves LS20, but requires about 2,400 actions across 17 attempts, roughly 3.6× the mean human action count (human range 405–1111 actions). We interpret the gap as a difference in how interaction is organised under uncertainty and resource pressure: human players appear to turn visual differences into action-testable regularities, while the agent still relies on slower explicit probing. We frame these patterns through the protocognition taxonomy (Rodriguez-Vergara and Husbands, 2026) and the bodily mindedness framework (Parvizi-Wayne and Montefiore, 2026), arguing that what emerges is neither mindless flow nor reflective deliberation, but a form of skilful, flexible engagement whose computational underpinnings remain an open empirical question.
Data/Code available at: ARC-AGI-3 games: https://three.arcprize.org; project data, agent traces, scripts, and figures are available from the authors upon reasonable request.
Andrei C. Aioanei, Syeda Khushbakht Batool, Alexander Aston et al.· IEEE Symposium on Artificial...· 0 citations
This article introduces a framework for designing and running simulated experiments with LLM‐powered agents and applies the framework to the exploration–exploitation dilemma and shows that LLM‐based experiments reproduce patterns observed among human participants.
This primer draws on fieldwork in a computational biology laboratory to examine what human oversight of AI agents requires in practice and shows that effective oversight has four components: adequate knowledge of system capabilities and limitations, sufficient observation of system actions, meaningful control of system behavior, and timely intervention in system failures.
An analysis that derives task-specific algorithmic design spaces from human-designed methods, maps both human- and agent-designed methods into these spaces, and quantifies their algorithmic differences at the module level suggests that although current agents can occasionally match or surpass human SOTA performance, their algorithmic designs remain within human-derived algorithmic design spaces.
Yikang Yang, Zhengxin Yang, Luzhou Peng et al.· 0 citations
Chess has long served as a model domain for studying search, expertise, decision-making, and artificial intelligence. The emergence of large language models (LLMs) has renewed the relevance of chess as a controlled environment for investigating strategic reasoning and comparing human and artificial decision-making. We present a systematic mapping study of recent research spanning human players, classical chess engines, neural and reinforcement-learning systems, LLMs, and hybrid approaches. The final map comprises 84 core study families, classified according to agent type, strategic-reasoning stages, and evaluation dimensions. The map reveals a literature strongly concentrated on situation assessment, evaluation, and action selection, while explicit planning, explanation, metacognition, and human--AI collaboration remain less explored. LLM research places particular emphasis on state representation and generalization, whereas grounded explanation appears more frequently in hybrid approaches combining language models with engines, expert knowledge, or other external structures. Two distinctions emerge that the map aggregates rather than resolves: hybrid systems differ in where and when heterogeneous capabilities combine, and evaluations that show improved human performance do not thereby establish human--AI synergy. We propose both as extensions of the mapping framework. We argue that chess provides a useful bridge between cognitive and computational perspectives on strategic reasoning, and identify explicit planning, grounded and faithful explanation, metacognitive calibration, and human--AI complementarity as directions for future research.
Large language models (LLMs) exhibit advanced social reasoning capabilities like Theory of Mind (ToM), yet the dynamic acquisition process remains underexplored. We propose a constructive, artificial-life approach that treats LLMs as “model organisms,†investigating the emergent mechanisms of cognitive functions through biological adaptive evolution rather than static analysis, as a step toward understanding the human-AI societies now taking form. By applying a genetic algorithm to evolve LoRA adapters from a behaviorally degraded state, in which task performance is reduced to near-random levels while latent knowledge remains in the frozen weights, we analyzed this evolutionary process at both behavioral and mechanistic levels. At the behavioral level, comparing adaptations in knowledge-intensive (MMLU) and social reasoning (ToMBench) environments revealed that task characteristics dictated fitness landscape ruggedness. An asymmetric generalization was observed: while moderate adaptation to broad knowledge partially bolstered heuristic social reasoning, excessive specialization created an evolutionary trade-off constraining deep inferential capabilities. At the mechanistic level, a Sparse Autoencoder (SAE) revealed the dynamic refinement of reasoning mechanisms during ToM evolution. The evolved individual’s strategy underwent a stepwise transition from superficial linguistic cues to mental state concepts, ultimately specializing in ToM-related conceptual representations. This stepwise acquisition trajectory, alongside the compensatory reasoning observed in the knowledge-intensive environment, suggests a structural generality in the adaptive acquisition of higher-order cognitive capabilities.
Data/Code available at: https://doi.org/10.5281/zenodo.20790937
