Monte Carlo Cone Sampling

Procedure for sampling 64 random nonnegative combinations of cone basis vectors to evaluate the full cone distribution

Neighborhood — ranked by edge-count

paper

framework

Concept Cones
uses
The central framework this paper extends from refusal to propositional truth; identifies multi-dimensional subspaces that causally mediate target behaviors

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Monte Carlo Methodmethod0.783
Computational algorithm mentioned as an example of diverse problem-solving strategies.
Monte-Carlo reinforcement learningmethod0.743
Reinforcement learning methods that update parameters at the end of an episode based on sampled returns.
Thompson Samplingmethod0.737
A Bayesian exploration strategy that samples from the posterior distribution over model parameters to decide actions.
Monte Carlo Tree Searchmethod0.733
Search algorithm used in AlphaGo and proposed for combining with LLMs.
Cross-Modal Samplingmethod0.729
Technique used to demonstrate that the self-prior captures visual–proprioceptive associations by recovering visual appearance from proprioception alone
Autoregressive Samplingmethod0.726
The mechanism by which LLMs generate text: drawing a token from the next-token distribution and appending it to context repeatedly
Rejection samplingmethod0.723
A technique to filter model outputs; Redwood Research's project mentioned.
Monte Carlo Integration for EImethod0.719
Technique to estimate the continuous EI formula by sampling, used in neural network EI calculation.