arXiv:2602.13273v1 Announce Type: new Abstract: Large language model (LLM) merging has become a key technique in modern LLM development pipelines, enabling the integration of multiple task- or domain-specific expert models without retraining. However, as the number of experts grows, existing merging implementations treat model parameters as unstructured files and execute merges in a stateless, one-shot manner, leading to excessive disk I/O, redundant parameter scans, and poor scalability. In this paper, we present textbf{MergePipe}, a parameter management system for […]

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NASA’s Transiting Exoplanet Survey Satellite (TESS) has identified thousands of exoplanet candidates, yet many remain unconfirmed due to the limitations of manual vetting processes. This paper presents ExoNet, a multimodal deep learning framework that integrates phase-folded global and local light curve representations with stellar parameters using a late-fusion architecture combining 1D Convolutional Neural Networks and Multi-Head Attention. Trained on labeled Kepler data, ExoNet achieves strong classification performance and demonstrates effective generalization to TESS data. Applied to 200 unconfirmed […]

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The most important effect of AI is the one we can’t see.

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Navigating complex environments poses challenges for multi-agent systems, requiring efficient extraction of insights from limited information. In this paper, we introduce the Blackbox Oracle Information Learning (BOIL) process, a scalable solution for extracting valuable insights from the environment structure. Leveraging the Pagerank algorithm and common information maximization, BOIL facilitates the extraction of information to guide long-term agent behavior applicable to problems such as coverage, patrolling, and stochastic reachability. Through experiments, we demonstrate the efficacy of BOIL in generating […]

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arXiv:2601.21789v1 Announce Type: cross Abstract: We introduce ECSEL, an explainable classification method that learns formal expressions in the form of signomial equations, motivated by the observation that many symbolic regression benchmarks admit compact signomial structure. ECSEL directly constructs a structural, closed-form expression that serves as both a classifier and an explanation. On standard symbolic regression benchmarks, our method recovers a larger fraction of target equations than competing state-of-the-art approaches while requiring substantially less computation. Leveraging this efficiency, ECSEL […]

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arXiv:2602.09273v1 Announce Type: new Abstract: We study approximation algorithms for Maximum Constraint Satisfaction Problems (Max-CSPs) under differential privacy (DP) where the constraints are considered sensitive data. Information-theoretically, we aim to classify the best approximation ratios possible for a given privacy budget $varepsilon$. In the high-privacy regime ($varepsilon ll 1$), we show that any $varepsilon$-DP algorithm cannot beat a random assignment by more than $O(varepsilon)$ in the approximation ratio. We devise a polynomial-time algorithm which matches this barrier under […]

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In this project, we study the optimization methods impacts on deep learning tasks, where we particularly focus on adaptive learning rate optimizers (e.g., AdaGrad, RMSProp, and Adam) and describe them, stating their strengths, weaknesses, and scenarios where they excel or underperform. We employ an experimental approach to analyze their performance, generalization, computational efficiency, and hyperparameter sensitivity. The study compares the performance of adaptive optimizers against a traditional method (SGD) and a non-tuning machine learning model (LDA). Our empirical […]

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Spiking neural networks (SNNs) employing unsupervised learning methods inspired by neural plasticity are expected to be a new framework for artificial intelligence. In this study, we investigated the effect of multiple types of neural plasticity, such as spike-time-dependent plasticity (STDP) and synaptic scaling, on the learning in a winner-take-all (WTA) network composed of spiking neurons. We implemented a WTA network with multiple types of neural plasticity using Python. The MNIST and the Fashion-MNIST datasets were used for training […]

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