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arXiv AI

Analogy as Nonparametric Bayesian Inference over Relational Systems

arXiv:2006. 04156v2 Announce Type: replace Abstract: Our inferences in the real world are rarely na\"ive - we acquire experiences through our lifetime that can help us more quickly understand the structure of something new.

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arXiv AI
Sep 3

Induction and Inquiry via Probabilistic Reasoning over Language and Code

The paper introduces a computational model that encodes symbolic knowledge as mental programs combining natural language and source code, and uses LLM-guided Bayesian learning to sequentially infer these programs. It demonstrates that this approach satisfies data‑efficiency, uncertainty handling, and flexibility, reproducing human inductive learning and active inquiry behaviors such as anchoring and garden‑pathing. In contrast, pure LLMs and classic Bayesian models either fail the task, do not match human behavior, or require prohibitive computational resources.

By Wasu Top Piriyakulkij, Sam Acquaviva, Cassidy Langenfeld, Joshua Tenenbaum, Kevin Ellis
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arXiv AI
Jul 14

People use fast and flat simulation to reason about new games

arXiv:2510. 11503v2 Announce Type: replace-cross Abstract: Games have long been a microcosm for studying planning and reasoning in both natural and artificial intelligence (AI), often focusing on expert-level or even super-human play.

By Katherine M. Collins, Cedegao E. Zhang, Lionel Wong, Mauricio Barba da Costa, Graham Todd, Adrian Weller, Samuel J. Cheyette, Thomas L. Griffiths, Joshua B. Tenenbaum
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arXiv Machine Learning
Jun 19

Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence

arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.

By Jinseok Chung, Minkyoung Song, Hyunji Jung, Namhoon Lee
llmsbenchmarkssafety
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arXiv Machine Learning
Sep 14

Diffusion Models and Concept Formation

The paper argues that diffusion models, originally developed for image synthesis, implicitly perform concept formation similar to the Cobweb cognitive model. Both models build hierarchical density structures using Gaussian prototypes, treat categorization as score‑following to reduce uncertainty, and exhibit a basic level of abstraction. The authors demonstrate this correspondence by extracting a diffusion hierarchy from MNIST and Fashion‑MNIST data and comparing its basic level to that of Cobweb, suggesting diffusion models can serve as a continuous, scalable instantiation of concept formation.

By Zekun Wang, Karthik Singaravadivelan, Christopher J. MacLellan
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arXiv AI
Jul 1

BayesBench: Evaluating LLM Belief Trajectories Under Multi-Turn Evidence Accumulation

arXiv:2606. 30850v1 Announce Type: new Abstract: Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic uncertainty about their environment.

By Ankur Samanta, Akshayaa Magesh, Tal Lancewicki, Ayush Jain, Youliang Yu, Paul Sajda, Kaveh Hassani, Aditya Modi, Daniel R. Jiang, Yonathan Efroni
llms
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