GenericAgent Model Card
这是可索引项目报告证据页：它保留 GenericAgent 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。
GenericAgent Model Card
One Sentence
GenericAgent is the clearest “not named evolution but actually evolution” example in the current packet: a lightweight agent that grows a skill tree instead of preloading a fixed one.
Three Sentences
Its public repository still leads with the claim that a small seed plus atomic tools can grow into full-system control through skill-tree expansion. That matters because the project name does not advertise evolution, yet the mechanism directly targets long-horizon capability growth, memory accumulation, and context compression. The 2026-06-17 morning packet keeps moving this runtime forward, which is exactly why it now belongs inside the public site registry rather than only the raw/classification layer.
Model Card
| Field | Value |
|---|---|
| Repository | lsdefine/GenericAgent |
| Source | raw-github/lsdefine_genericagent.md |
| Category | Token-efficient self-evolving agent |
| Pattern | context density -> skill-tree growth -> memory/reflection compression |
| Evidence | Authenticated GitHub API snapshot, 2026-06-17 08:29 +0800 |
Teaching Use
Use GenericAgent to teach context economics. Many self-evolution systems spend more tokens to search; GenericAgent’s claim is that better context density and skill reuse can reduce token cost while expanding control.
Evidence And Limits
The raw capture now reflects a GitHub metadata packet observed on 2026-07-05: 13,284 stars, 1,535 forks, 895 commits, 94 open issues, and 72 open pull requests. This packet is fresher than the previous authenticated packet at 2026-07-05 01:38 +0800 where a delta was observed. This run did not execute the repository locally, validate workflows end to end, or independently rerun benchmark claims. Product, memory, benchmark, and automation claims therefore remain repository-scoped unless separately tested.