AMA-Bench Model Card
这是可索引项目报告证据页：它保留 AMA-Bench 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。
AMA-Bench Model Card
| Field | Value |
|---|---|
| Repository | AMA-Bench/AMA-Bench |
| Category | Agent 长程记忆基准 |
| Stars / forks snapshot | 40 / 8 |
| Language | Python |
| License | MIT |
| Raw capture | raw-github/ama-bench_ama-bench.md |
| Updated by | hourly public metadata update, 2026-05-24 |
1. Role in Self Evolve
面向 agentic applications 的长程记忆 benchmark，评估方法是否能从长 agent 轨迹构建记忆、召回证据并回答问题。
2. Working Principle
把 agent 长轨迹转换为记忆构建/检索接口，再用问答与评审器衡量长期证据保持能力。
3. Evidence Path
web GitHub page observed benchmark README, two-stage method API, judge reliability table, and leaderboard links; shell GitHub API was blocked by DNS and gh auth was invalid, so this run marks freshness as web-page observed rather than API verified.
4. Teaching Use
Use this card to explain whether a repository is a runtime, benchmark, harness-evolution loop, memory/skill substrate, or resource index. The key reading path is: raw capture -> classification row -> public site card -> project report -> aggregate GitHub analysis.
5. Limits
当前未克隆源码，未运行 benchmark；star/fork/commit 快照来自公开 GitHub 页面文本。