arXiv Machine Learning July 14, 2026 By Bla\v{z} P\v{s}eni\v{c}nik, Borko Bo\v{s}kovi\'c, Jan Popi\'c, Janez Brest

Prioritizing Search Space Regions in the Low Autocorrelation Binary Sequences Problem

Read the original on arXiv Machine Learning →

arXiv:2607. 09688v1 Announce Type: new Abstract: Low autocorrelation binary sequences problem (LABS) is a hard combinatorial optimization challenge with important applications in communications, signal processing, and satellite navigation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

reinforcement-learning

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ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization

arXiv:2603. 21180v4 Announce Type: replace Abstract: Sequential experimental design under expensive, gradient-free objectives is a central challenge in computational statistics: evaluation budgets are tightly constrained and information must be extracted efficiently from each observation.

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Efficiency Matters in Autonomous Research

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Spotlight: Synergizing Seed Exploration and Spot GPUs for DiT RL Post-Training

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llms diffusion computer-vision reinforcement-learning

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Bilevel Autoresearch: Meta-Autoresearching Itself

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reinforcement-learning fine-tuning benchmarks

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Target-Aware Bandit Allocation for Scalable Surrogate Optimization in Chemical Space

arXiv:2606. 26657v1 Announce Type: new Abstract: Identifying high-utility candidates from massive discrete spaces under expensive evaluations is a recurring challenge across the sciences, with structure-based drug discovery as a prominent example.

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