mcmc

This paper introduces Entropy-Guided Power Sampling (EGPS), a training-free and verifier-free sampler that improves the efficiency of power sampling for enhancing base language model reasoning. EGPS achieves up to 12.6x speedup over standard Metropolis-Hastings sampling while reaching best or tied-best accuracy on benchmarks like MATH500, HumanEval, and GPQA.

This paper proposes a four-phase method for constructing causal graphs that model LLM inference processes, using counterfactual augmentation to enable stable causal discovery and provide transparent, concept-level explainability.

bde is a Python package that brings sampling-based Bayesian Deep Learning to practitioners via the MILE method, combining JAX's speed with scikit-learn's API for tabular supervised learning tasks.