AI Agent Benchmark Model Card
这是可索引项目报告证据页：它保留 AI Agent Benchmark Model Card 的源材料入口、机制线索和限制提醒；正文仍需 reader/editor 与 academic public-copy review 后才能当作最终结论引用。
AI Agent Benchmark Model Card
| Field | Value |
|---|---|
| Repository | murataslan1/ai-agent-benchmark |
| Category | Multi-Domain Agent Benchmark Pack |
| Stars / forks snapshot | 24 / 4 |
| Language | Markdown |
| License | MIT |
| Raw capture | raw-github/murataslan1_ai-agent-benchmark.md |
| Updated by | hourly public metadata update, 2026-05-27 22:00 +0800 |
1. Role in Self Evolve
ai-agent-benchmark is a compact benchmark repository that evaluates AI agents across coding, math, memory, translation, and safety-oriented task slices. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.
2. Working Principle
define multi-domain task suites -> evaluate coding/math/memory/translation and safety behavior -> score cross-model outcomes -> expose benchmark schema for reproducible comparisons
3. Evidence Path
web-observed GitHub page showed 24 stars, 4 forks, 3 commits, MIT license, and README claims for 73 benchmark tasks spanning code, memory, translation, and safety categories. Shell GitHub API access remained blocked by DNS and local gh auth was invalid, so this card treats the snapshot as web-observed rather than API-verified.
4. Teaching Use
Use this card to explain Multi-Domain Agent Benchmark Pack: it shows how harness/runtime/benchmark layers convert agent behavior into reproducible and auditable engineering workflows.
5. Limits
The repository was not cloned in this iteration; no benchmark run, plugin install, workflow execution, or agent loop experiment was executed. Counts and claims are visible public-page/search signals unless independently revalidated later.