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UC Berkeley researchers propose a hybrid system treating cognitive algorithm discovery as a program refinement problem, where LLM agents revise human-authored probabilistic programs based on behavioral data mismatches, consistently improving model fit in a problem-solving paradigm.
This paper shows that Large Language Bayes (LLB), which averages posteriors from language-model-generated probabilistic programs using weights from an exponentiated evidence bound, is not reparameterisation-invariant. Equivalent program writings can receive substantially different weights (up to 31.9× discrepancy that can even reverse sign), inverting Bayes factors and inducing error in model posteriors controlled by the spread of bound shortfalls via a sharp Hilbert-distance bound.
This paper introduces PPDL, a probabilistic language for programming LLM-based flows that enables developers to quantify and propagate uncertainty throughout applications, with experimental and case studies on theorem proving.
Jacquard is a programming language designed for AI-written, human-reviewed code, with built-in effect tracking, probabilistic simulation, and canonical identity to help humans trust AI-generated programs.
NoiseLang is a probabilistic programming language where every value is a distribution. It compiles to efficient Monte Carlo simulations using a JIT compiler and supports conditional inference.
This paper introduces NeSyCat Torch, a differentiable tensor implementation of categorical semantics for neurosymbolic learning, unifying classical, fuzzy, and probabilistic semantics under a monadic framework and demonstrating superior speed and accuracy on MNIST addition compared to existing systems like LTN and DeepProbLog.