Mem-Gallery Model Card
这是可索引项目报告证据页：它保留 Mem-Gallery 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。
Mem-Gallery Model Card
| Field | Value |
|---|---|
| Repository | YuanchenBei/Mem-Gallery |
| Category | Long-Term Memory Benchmark Suite |
| Stars / forks snapshot | 36 / 2 |
| Language | Python |
| License | MIT |
| Raw capture | raw-github/yuanchenbei_mem-gallery.md |
| Updated by | hourly public metadata update, 2026-05-29 16:12 +0800 |
1. Role in Self Evolve
Mem-Gallery provides a benchmark suite for long-term memory capability in LLM-based agents and assistants. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.
2. Working Principle
assemble memory-intensive tasks and temporal-context datasets -> run agents with different memory strategies -> score recall/consistency/retrieval behavior -> compare long-term memory robustness across setups
3. Evidence Path
web-observed GitHub page showed 36 stars, 2 forks, 12 commits, MIT license, and README sections for benchmark datasets and evaluation pipelines. 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 Long-Term Memory Benchmark Suite: 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.