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This paper introduces GA-AMLS, a rare-event Monte Carlo method adapted to language model activation spaces, and SPB Loss, a proper scoring rule for asymmetric penalties, demonstrating improved estimation of rare harmful outputs.
PHITSBench is an execution-scored benchmark for evaluating AI models on generating PHITS radiation-transport input files from natural language, covering editing, repair, and full generation tasks. Experiments with GPT-5.4 show that while domain knowledge improves performance, significant challenges remain in correctly configuring physical observables.
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.
Pitwall is a production system that generates faithful natural-language Formula 1 strategy briefings by decomposing claims into typed factual assertions and verifying each against a calibrated Monte Carlo race simulator, ensuring hallucination-free output.
Flux-GS enables real-time high-fidelity 3D Gaussian Splatting on mobile platforms through efficient lighting representation, attribute-conditioned enhancement, and multi-view densification strategies.
This paper introduces a model-free deep learning method for solving high-dimensional nonlinear partial differential equations with unknown coefficients, using zeroth-order derivative estimators derived from perturbed Monte Carlo trajectories. The approach avoids automatic differentiation, provides theoretical error bounds, and demonstrates competitive performance in numerical experiments.
This paper presents counterexamples showing that Monte Carlo Exploring Starts can converge to suboptimal solutions in tabular reinforcement learning, and provides a modification that guarantees convergence to optimality by scaling learning rates inversely to update frequencies.
Introduces Agentic Monte Carlo (AMC), a method to perform reinforcement learning-style optimization of black-box LLM agents using Sequential Monte Carlo, without requiring access to model parameters.
Researchers at MIT CSAIL and Harvard used a modified Battleship game to study and improve language models' question-asking abilities. By applying Monte Carlo inference strategies, they significantly boosted smaller models like Llama 4 Scout's win rate from 8% to 82% against humans, outperforming larger models at a fraction of the cost.
This paper introduces MAPLE, a tree search method that aggregates policy and value evaluations from multiple sampled world states, extending AlphaZero to imperfect-information games. Experiments on Phantom Go and Dark Hex show Elo improvements of 291 and 136 over the PIMC-based AlphaZero baseline.
A developer tests a Cold War-era AI model on satellite image datasets using Monte Carlo simulations, finding it efficient and suitable for FPGA deployment.
The article explains how to use Bayesian modeling with Gaussian processes to handle spatial data where the coordinates are observed with error, using a dataset of uranium and vanadium concentrations from Walker Lake as an example.
This paper investigates whether shallow neural network agents can master the card game Schnapsen using reinforcement learning, outperforming a supervised imitation baseline and achieving competitive results against a strong search-based opponent.
ColPackAgent is an agent framework that uses a Model Context Protocol tool server and agent skill to autonomously run hard-particle Monte Carlo simulations for colloidal packing, enabling structured workflows with human feedback or autonomous execution, with benchmarks across multiple LLMs.