MAB
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Related Articles from SNS
Blockage-Aware Non-stationary Dynamic Bandit for User Association in mmWave V2X Networks
arXiv:2606.08118v1 Announce Type: new Abstract: In millimeter-wave (mmWave) vehicular networks, dense base station (BS) deployments expand the user association (UA) decision space while dynamic blockages cause link quality fluctuations, posing critical challenges for effective mobility management. Traditional Multi-Armed Bandit (MAB) frameworks assume stationary reward distributions and fail to handle the rapid context-reward mapping shifts caused by vehicle mobility and transient blockages.
Don't Ask the LLM to Track Freshness: A Deterministic Recipe for Memory Conflict Resolution
arXiv:2606.01435v1 Announce Type: new Abstract: LLM-based memory systems increasingly maintain facts that evolve over time, where a recurring failure is conflict resolution: when a fact has multiple contradictory values, which should the agent return? MemoryAgentBench (MAB; Hu et al., 2026) makes this explicit in its FactConsolidation task: facts are numbered, the counterfactual has the higher serial, and agents are told newer facts have larger serials. Yet every published system...
Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits
arXiv:2606.08977v1 Announce Type: new Abstract: Motivated by the recency effect in online learning, we study algorithms for single-pass *sliding-window streaming multi-armed bandits (MABs)* In this setting, we are given $n$ arms with unknown sub-Gaussian reward distributions and a parameter $W$. The arms arrive in a single-pass stream, and only the most recent $W$ arms are considered valid.
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. We propose \textbf{ALMAB-DC}, a GP-based sequential design framework combining active learning, multi-armed bandits (MAB), and distributed asynchronous computing for expensive black-box experimentation. A...
Rapid Determination of Drug-to-Antibody Ratios in Antibody Drug Conjugates Using Ultrafast Microdroplet Digestion Technology
Accurate determination of drug to antibody ratios (DARs) is essential for the development, quality control, and performance evaluation of antibody drug conjugates (ADCs); yet conventional analytical approaches often require extensive sample preparation, long analysis time, and substantial sample consumption. The peak distribution of intact ADCs is highly complex due to inherent glycosylation heterogeneity and variable drug conjugation. By applying enzymatic digestion, ADC can be converted...
Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries
Announce Type: new Abstract: Personalized decision-making in multi-objective bandits requires learning user-specific trade-offs among competing objectives. Since arm utility depends on both unknown rewards and unknown preferences, existing methods infer preferences only from utility feedback, entangling preference learning with reward exploration. In practice, however, users often reveal their priorities through proactive conversational queries (e.g., "cheap and clean hotel"), yet this...