Continuity Benchmarks Model Card

这是可索引项目报告证据页：它保留 Continuity Benchmarks 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。

Continuity Benchmarks Model Card

1. Role in Self Evolve

continuity-benchmarks provides reproducible execution-intent memory benchmarks for long-horizon AI coding agents with LongMemEval-S and ID-RAG matrix evaluation. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.

2. Working Principle

agent action intent -> retrieval keyed by execution intent vs prompt intent -> benchmark runners score recall/alignment -> report deltas and confidence for memory strategy selection

3. Evidence Path

web-observed GitHub page showed 3 stars, 0 forks, 27 commits, MIT license, and benchmark runners comparing retrieval strategies for coding-agent memory. 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 Execution-Intent Memory Benchmark Harness: 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.