Objective

The study aimed to accelerate giant impact simulations in planetary formation using machine learning (ML) to predict collisional outcomes in multiplanet systems.

Method

The researchers developed an ML model trained on over 500,000 \(N\)-body simulations of three-planet systems. The approach was designed to classify which two planets would collide and to predict the state of the post-collision planets. Validation of the ML model was conducted against both ML and non-ML baselines, utilizing shadow integrations to assess accuracy.

Results

The ML collision classifier achieved a 66.5% accuracy in correctly identifying colliding planet pairs, with a prediction scatter of approximately 10%. The orbital outcome regressor outperformed baseline models in predicting post-collision states, achieving predictions near chaos-imposed accuracy limits. The ML-based emulator performed simulations up to four orders of magnitude faster than traditional methods, generating realistic planetary systems.

Significance

The findings could facilitate new analyses of the planet formation process that were previously computationally prohibitive. Although the emulator produced slightly less dynamically excited systems than traditional \(N\)-body simulations, it holds potential for extensive parameter space exploration. The study acknowledges the model's limitations and emphasizes the need for cautious application in future simulations. The authors have made their training code and model API publicly available.

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Objective

The study aims to evaluate the effectiveness of cross-validation in enhancing the accuracy of spectral representations derived from Stochastic Analytic Continuation (SAC) by identifying the best-fitting parameters and models.

Method

A k-fold cross-validation approach is implemented, where the dataset of imaginary-time correlation functions is divided into k training sets and one validation set. The goodness-of-fit is measured using the chi-squared (\(\chi^2\)) statistic, which serves as the loss function during model training. This cross-validation scheme is integrated into the SAC workflow to compare spectral functions generated by different parameterizations.

Results

The findings reveal that cross-validation significantly improves the identification of optimal parameters and spectral shapes in SAC. It establishes an optimal sampling temperature (\(\Theta\)) for spectral function estimates and effectively differentiates between various models, such as equal amplitude and variable amplitude parameterizations.

Significance

The study highlights the importance of cross-validation in model selection for spectral analysis within quantum many-body physics. Its application is crucial for accurately distinguishing spectral features of quantum phases, thereby enhancing the reliability of numerical analytic continuation methods, including SAC, in future research endeavors.

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Objective

To develop a surprise-adaptive agent that can adaptively switch between surprise-minimization and surprise-maximization objectives in unsupervised reinforcement learning (RL) and to demonstrate its effectiveness across different entropy regimes without relying on extrinsic rewards.

Method

The study employs a multi-armed bandit approach to select the optimal surprise objective based on the agent's current entropy conditions. The agent estimates the marginal state distribution using maximum likelihood estimation to calculate entropy for decision-making. An intrinsic feedback mechanism rewards the agent for its ability to control environmental entropy.

Results

The surprise-adaptive agent outperforms single-objective agents by effectively adapting to changing environmental conditions. In benchmark tasks, it achieves better overall task returns compared to both surprise-minimizing and surprise-maximizing agents, leveraging the strengths of both objectives.

Significance

This research demonstrates that combining adaptive intrinsic motivation strategies can significantly enhance an agent's ability to learn complex tasks and develop emergent behaviors. The findings indicate a promising direction for future research into intelligent agents capable of navigating diverse environments without dependence on extrinsic rewards.

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Objective

The study aims to present a hybrid self-ensembling approach named PEDAL, which enhances traditional Greedy Decoding and Self-Consistency (SC) methods to improve accuracy and cost efficiency in large language model (LLM) reasoning tasks.

Method

The study employs an experimental approach using two publicly available datasets: SVAMP (elementary-level math word problems) and the AI2 Reasoning Challenge (ARC) for multiple-choice question-answering. The methodology involves generating candidate responses from a language model (LLM) using diverse prompts based on selected exemplars, utilizing Greedy Decoding for response generation. The USC consensus method is used for aggregating responses, selecting the most consistent output from multiple candidates. Performance metrics include accuracy and token counts, with experiments run multiple times for reproducibility.

Results

Key findings indicate that PEDAL outperforms Greedy Decoding in accuracy (77.89% vs. 76.00% on SVAMP) and demonstrates cost efficiency by generating fewer output tokens compared to USC, while maintaining competitive accuracy. On the ARC dataset, USC achieved the highest accuracy (84.35%), with PEDAL showing marginal improvements over Greedy Decoding (83.77% vs. 83.38%). The results highlight the effectiveness of diverse prompt strategies in optimizing LLM performance.

Significance

The research is significant as it addresses the limitations of existing SC methods, particularly regarding fixed answer sets and high inference costs. By introducing PEDAL

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