The authors designed a memory module called the Bayesian Cheatsheet, which models reasoning behaviors (concise natural-language insights) as embeddings in a Hierarchical Dirichlet Process Gaussian Mixture Model.
Bayesian memory module adapts reasoning strategies during test time
A Hierarchical Dirichlet Process Gaussian Mixture Model organizes reasoning behaviors into clusters that can be retrieved and updated on the fly, improving LLM performance on math and physics reasoning tasks.
Big Tech
Keshav Ramji · Tahira Naseem · Ramón Fernandez Astudillo
IBM Research AI
Research Digest··3 min read
The authors introduce the Bayesian Cheatsheet, a structured memory module for large language models that uses a Hierarchical Dirichlet Process Gaussian Mixture Model (HDP-GMM) to organize reasoning behaviors into an unbounded number of clusters.
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From IBM Research AI
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A Bayesian nonparametric memory that clusters and retrieves reusable reasoning behaviors improves LLM performance across math and physics benchmarks, including cold starts.
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