EvoMap Evolver Model Card

这是可索引项目报告证据页：它保留 EvoMap Evolver 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。

EvoMap Evolver Model Card

1. Role in Self Evolve

EvoMap/evolver presents a memory-map framework that keeps updating graph structures from user and task feedback to improve retrieval and reasoning quality over time. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.

2. Working Principle

represent knowledge as evolving graph maps -> apply reinforcement and relation updates from interaction feedback -> optimize memory retrieval paths for downstream reasoning -> persist the updated map structure as a reusable long-horizon state substrate

3. Evidence Path

web-observed GitHub page showed 7.6k stars, 773 forks, 87 commits, GPL-3.0 license, and README positioning around self-evolving memory maps for AI reasoning. 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 Self-Evolving Memory and Reasoning Map Framework: 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.