shidingz/EDV: Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experience learning methods mostly rely on single-agent loops, where the same agent executes tasks, summarizes outcomes, and determin...
Pillar = mean of 2 scaled values = 2.5.
Awaiting first reading — these signals apply to this agent and will be ingested on the next tier tick: PyPI monthly installs, SO questions (7d), Product Hunt upvotes, Docker Hub pulls, Crates.io downloads (90d), Tech-news mentions (30d)
Not applicable — this agent doesn't have the prerequisite (no GitHub repo, no HF mirror, etc.) for these signals to ever apply: HF downloads (30d), npm weekly installs
[](https://agenttape.com/agents/shidingz-edv)
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