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LLM-Guided Bayesian Learning Models Human Inductive Inquiry

Wasu Top Piriyakulkij, Sam Acquaviva, Cassidy Langenfeld, Joshua Tenenbaum, Kevin Ellis· September 3, 2026 View original

Key takeaways

  • Humans may learn by inferring "mental programs" combining language and code.
  • LLM-guided Bayesian learning can efficiently model human inductive reasoning.
  • The model reproduces human cognitive biases like anchoring and garden-pathing.
  • This approach offers a path to more data-efficient and human-like AI.

Who benefits

AI DevelopmentEdTechCognitive ScienceRobotics

Summary

Researchers introduce a computational model that combines natural language and source code as "mental programs" to explain human inductive learning and active inquiry. Using LLM-guided Bayesian learning, the model efficiently reproduces human cognitive biases like anchoring and garden-pathing, outperforming pure LLMs and classic Bayesian models.

A new computational model has been developed to shed light on how humans acquire and maintain abstract knowledge from limited and noisy experiences. This model addresses three key requirements: data and compute efficiency, the ability to represent uncertainty for intelligent inquiry, and flexibility to represent a vast range of concepts. It achieves this by encoding symbolic knowledge as "mental programs" that integrate natural language with source code. The model employs LLM-guided Bayesian learning algorithms to sequentially infer these mental programs. Across various behavioral studies, it successfully replicates quantitative aspects of human inductive learning and active inquiry, including phenomena like anchoring and garden-pathing effects. Unlike pure LLMs or traditional Bayesian models, this approach demonstrates superior performance in reproducing human behavior without incurring exorbitant computational costs, suggesting that humans might continuously expand knowledge by revising language-like and program-like hypotheses through approximate Bayesian updates, facilitated by a neural mechanism.

Why it matters

This research offers a novel perspective on human learning and reasoning, potentially informing the development of more human-like and efficient AI systems capable of robust, data-efficient knowledge acquisition.

How to implement this in your domain

  1. 1Explore integrating "mental program" representations (combining language and code) into AI systems for more flexible knowledge representation.
  2. 2Investigate LLM-guided Bayesian learning algorithms for developing more data-efficient and human-like AI learning agents.
  3. 3Apply insights from human cognitive biases (e.g., anchoring) to design more robust and less susceptible AI decision-making processes.
  4. 4Consider hybrid AI architectures that leverage both neural networks (LLMs) for inference and symbolic representations for knowledge acquisition.

Original post by Wasu Top Piriyakulkij, Sam Acquaviva, Cassidy Langenfeld, Joshua Tenenbaum, Kevin Ellis

"arXiv:2609.01815v1 Announce Type: new Abstract: How humans grow and maintain abstract knowledge from the sparse, streaming noisy data of experience is a longstanding challenge in cognitive science. Any computational account must satisfy at least three desiderata: It must be (1) d…"

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Originally posted by Wasu Top Piriyakulkij, Sam Acquaviva, Cassidy Langenfeld, Joshua Tenenbaum, Kevin Ellis on X · view source

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