# Benchmark Radar：AI 基准与评测的活数据库和搜索引擎

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-10 08:00
- AIHOT 分数：54
- AIHOT 链接：https://aihot.news/items/cmu0pwl7k05z1rox21y34k399
- 原文链接：https://arxiv.org/abs/2609.11115

## AI 摘要

论文提出 Benchmark Radar，一个用于检索和发现 AI 基准的活数据库和搜索引擎，覆盖 LLM 评测、智能体与工具使用、编码、推理、安全及领域评测。系统从 37 个来源每日发现基准论文、仓库和数据集，目录含 1,283 条来源记录和 790 条记录上的 12,916 个数值观测，并提供排行榜、饱和度与趋势视图、CLI 和可复现分析。

## 正文

Benchmark researchers and developers of large language models (LLMs) and other AI systems need to find relevant evaluations, locate their benchmark datasets and code, and understand the settings behind reported scores. We present Benchmark Radar, a living database and search engine for retrieval and discovery of AI benchmarks, covering LLM evaluation, agentic and tool-use benchmarks, coding, reasoning, safety, and domain-specific evaluations. The system combines daily discovery of benchmark papers, repositories, datasets, and releases with a searchable benchmark catalog, mentions in model cards and technical reports, and score histories.

It retains source identities and citations so readers can inspect candidate benchmarks and their evaluation evidence. Daily discovery draws on 37 sources: 13 direct connectors and 24 first-party research and engineering feeds. The catalog contains 1,283 source records drawn from 4 benchmark catalogs and 12,916 numeric observations on 790 records. We describe collection and retrieval, audit the full catalog, and examine benchmark saturation, adoption trends, and the limits of score comparisons. A worked example walks through a complete prior-art search, showing how to query the catalog and inspect benchmark evidence when designing a new evaluation.

We release the web dashboard with a benchmark leaderboard, a Pareto frontier view of score against measured use, saturation and trend views, daily feeds, downloadable evidence, a command-line interface (CLI) for offline queries, and reproducible analysis.