@omarsar0: A great paper from Google on maintaining agent skills through persistent knowledge.
Summary
A Google paper discusses maintaining agent skills by separating raw execution traces from persistent knowledge in a wiki-like structure.
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Cached at: 08/30/26, 04:03 AM
A great paper from Google on maintaining agent skills through persistent knowledge.
DAIR.AI (@dair_ai): Banger paper from Google.
If you maintain a skill library for your agents, you might want to check this out.
(bookmark it)
This work separates three things that skill-evolution systems usually collapse into one. Raw execution traces, a persistent wiki of accumulated knowledge,
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@Teknium: Sounds a bit like Hermes' self improvement
A paper from Google discusses separating skill-evolution systems into components like raw execution traces and a persistent wiki of knowledge for AI agents, related to self-improvement concepts.
@omarsar0: Interesting paper on agent memory stored as a linked markdown wiki. Lots of great ideas and insights if you work with L…
The paper proposes Wiki Foundation Model (WFM), a novel foundation model for scalable, agent-native representation and retrieval using LLM Wiki, which couples dense text with link structure, achieving strong performance on benchmarks and 10.5x training acceleration.
@hwchase17: https://x.com/hwchase17/status/2071963622298050997
The article discusses the emerging pattern of 'wiki memory' for AI agents, where raw source data is intelligently compressed into a persistent, structured knowledge layer that agents can use efficiently. It compares this to basic RAG and gives examples like DeepWiki and LLM Wiki.