构建可组合的跨平台搜索框架，提升检索系统复现性与性能
SearchGym: A Modular Infrastructure for Cross-Platform Benchmarking and Hybrid Search Orchestration
- 将数据、向量、应用分离为可组合的模块，支持灵活配置
- 在LitSearch上实现70%的Top-100检索准确率，验证有效性
- 适合研究检索优化与跨领域信息获取的开发者和研究人员
检索增强生成（RAG）快速发展，但实验原型与生产系统间仍存在鸿沟。本文提出SearchGym，一个用于跨平台基准测试与混合搜索编排的模块化基础设施。不同于传统模型中心框架，SearchGym将数据表示、嵌入策略与检索逻辑解耦为有状态抽象：Dataset、VectorSet与App。这一设计支持组合式配置代数，使系统可通过分层配置合成，并确保完全可复现。我们分析了混合检索管道中的“Top-k认知”现象，发现语义排序与结构化过滤的最优顺序高度依赖过滤强度。在专家标注的LitSearch基准上，SearchGym实现了70%的Top-100检索率。结果揭示了通用性与可优化性之间的设计张力，表明工程优化可能成为揭示异构领域信息检索因果机制的工具。SearchGym开源代码见：https://github.com/JeromeTH/search-gym
原文摘要 · Abstract (English)
The rapid growth of Retrieval-Augmented Generation (RAG) has created a proliferation of toolkits, yet a fundamental gap remains between experimental prototypes and robust, production-ready systems. We present SearchGym, a modular infrastructure designed for cross-platform benchmarking and hybrid search orchestration. Unlike existing model-centric frameworks, SearchGym decouples data representation, embedding strategies, and retrieval logic into stateful abstractions: Dataset, VectorSet, and App. This separation enables a Compositional Config Algebra, allowing designers to synthesize entire systems from hierarchical configurations while ensuring perfect reproducibility. Moreover, we analyze the "Top-$k$ Cognizance" in hybrid retrieval pipelines, demonstrating that the optimal sequence of semantic ranking and structured filtering is highly dependent on filter strength. Evaluated on the LitSearch expert-annotated benchmark, SearchGym achieves a 70% Top-100 retrieval rate. SearchGym reveals a design tension between generalizability and optimizability, presenting the potential where engineering optimization may serve as a tool for uncovering the causal mechanisms inherent in information retrieval across heterogeneous domains. An open-source implementation of SearchGym is available at: https://github.com/JeromeTH/search-gym
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