action-space partitioning

This work addresses the prohibitive computational cost of surrogate model inference across ultra-large discrete chemical spaces, which hinders efficient identification of high-value molecules. The authors propose BOBa, a novel framework that, for the first time, integrates a multi-armed bandit mechanism into trillion-scale virtual screening. By partitioning the chemical library into target-aware subspaces modeled as “arms” and employing an uncertainty-based upper confidence bound strategy, BOBa dynamically allocates computational resources to perform surrogate inference and evaluation only in high-potential regions. Validated on real synthesizable molecular libraries, the method significantly reduces computational overhead while maintaining strong screening performance, achieving a tunable trade-off between exploration and exploitation as well as between cost and efficacy, thereby establishing a new paradigm for efficient optimization over massive chemical libraries.

This work addresses the high cost of small-scale pilot experiments required to fit scaling laws in large-scale model training. Framing the problem as a budget-aware sequential experimental design task, the authors propose an uncertainty-aware active selection strategy that dynamically chooses the most informative experiments from a heterogeneous-cost pool for extrapolation to the target regime. By integrating sequential experimental design, uncertainty quantification, and active learning, the method achieves fitting accuracy comparable to that of exhaustive experimentation using only approximately 10% of the total training budget across diverse scaling law tasks, substantially outperforming conventional experimental design baselines.

Existing benchmarks for operations research (OR) optimization overlook the realistic characteristics of industrial settings—specifically, the multi-stage task lifecycle and persistent, multi-artifact workspaces—rendering them inadequate for reliably evaluating large language model (LLM) agents in practical optimization workflows. To address this gap, this work proposes OR-Space, a novel benchmark that introduces, for the first time, a full-lifecycle evaluation paradigm with persistent, multi-artifact workspaces. By integrating business documents, structured data, code, and solver outputs into an executable workspace, OR-Space defines three core tasks—Build, Revise, and Explain—to comprehensively assess agent capabilities across modeling, refinement, and explanation phases. Leveraging multi-source heterogeneous artifact integration, task-specific evaluators, and cross-file evidence tracing, the benchmark enables end-to-end evaluation, establishing a systematic foundation for assessing the reliability, failure modes, and production readiness of LLMs in industrial OR applications.

This study addresses the challenge that existing factor screening designs struggle to simultaneously achieve high screening efficiency and adequate space-filling properties, thereby limiting the accuracy of subsequent surrogate modeling. To overcome this limitation, the authors propose a novel class of one-factor-at-a-time (OFAT) designs that systematically incorporates space-filling characteristics into the OFAT framework for the first time. While preserving the inherent efficiency of OFAT in identifying active factors, the proposed approach significantly enhances coverage of the input space. By refining the MOFAT family of designs through optimization-based space-filling criteria, the method demonstrates superior performance in both factor identification accuracy and space-filling quality across multiple numerical experiments, effectively balancing the dual objectives of efficient screening and high-fidelity modeling.

Multi-scale visualization—particularly under extreme scale-to-object ratios—presents significant design challenges. This paper formally defines the problem and constructs an eight-dimensional design space encompassing three spatial dimensions. Through systematic encoding analysis of 54 academic and industrial case studies, we distill five recurrent design strategies and identify structural gaps in cross-dimensional strategy combinations. The proposed framework is both descriptive and generative: it systematically exposes limitations of current approaches while providing reusable design principles and optimization pathways for large-ratio visualization. By unifying conceptual modeling with empirical evidence, our work advances systematic design capability in multi-scale visual analytics.

本文提出一种基于置信区域的筛选框架，用于解决模拟系统可接受性问题，保证高概率筛选出所有或每个可接受系统，并支持并行化。

This work addresses the issue of strategic manipulation in linear bandits, where arms may misreport their feature vectors to increase selection probability. To counter this, the authors propose the MESHA algorithm, which integrates uniform sampling with a round-wise “grim trigger condition” (GTC) to effectively suppress strategic behavior and eliminate severely distorted reports. The study establishes, for the first time, reliability guarantees for best-arm identification (BAI) under strategic linear settings, proving theoretically that conventional optimal-design-based sampling methods can entirely overlook the true best arm in such scenarios. Through Nash equilibrium analysis and derivation of an upper bound on the failure probability under fixed-budget constraints, the paper demonstrates that MESHA remains effective across all Nash equilibria. Empirical results confirm its significant superiority over existing baseline methods.

该研究通过消除几何学框架探讨局部最优对象能否由共享部署规则实现，分析信息、架构等因素对缺陷修复的影响。

This work addresses the high cost of ground-truth evaluation in chemical and materials design, where existing machine learning surrogate models often lack reliability guarantees. Departing from conventional reliance on prediction accuracy metrics such as R²—which can paradoxically increase the risk of worst-case selections—the study proposes “rank preservation” as a core criterion for surrogate validation. It formally introduces the concept of “selection tax” and derives its theoretical upper and lower bounds. A safety certification framework for surrogates is established through selection-aware auditing, rank correlation analysis, and multi-task ground-truth validation. Experiments demonstrate that the proposed audit statistics achieve Spearman correlations of 0.80–0.99 with actual search performance, substantially outperforming R² (as low as 0.33). Certified screening strategies based on this framework reduce evaluation costs by up to 25-fold.

This work addresses a fundamental limitation in conventional Bayesian experimental design, which relies on prior-to-posterior uncertainty reduction and yields an intractable objective that is doubly hard to evaluate and poorly aligned with downstream tasks. By reframing the problem through decision theory, the authors formulate it as optimizing the expected future loss (EFL) of downstream actions, thereby reducing the objective to a singly intractable form that obviates explicit posterior or marginal likelihood computation. They introduce a stochastic gradient method that jointly optimizes both the experimental design and the action policy, requiring only samples from the joint parameter–data model and evaluations of the loss function. This approach naturally accommodates implicit modeling and task-specific customization, demonstrating marked improvements over existing methods in both optimization efficiency and task adaptability.