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Wiki Foundation Model retrieves agent memory from linked markdown wikis

elvis

The Wiki Foundation Model (WFM) is for agents whose long-term memory is a folder of linked markdown files. It turns an LLM wiki into a graph and retrieves with message passing conditioned on the query, so the dense text on each page and the links between pages shape the result together.

The team also built a GPU-to-GPU training protocol that it says trains 10.5 times faster, and it reports strong results on five benchmarks covering agent memory and multi-hop reasoning. The work is described in the WFM paper, and academy.dair.ai hosts a chat page for the paper.

#wfm#agent-memory#llm-wiki#research

Best Taste-Bench model chooses the better direction 59.7% of the time

A paper from Microsoft and colleagues reports that the best model answers 59.7% of Taste-Bench questions correctly when choosing the better open direction in long engineering and research tasks. Forks decided by later evidence are much harder, a larger reasoning budget does not raise accuracy, and distilling a teacher’s outcome judgment improves held-out SWE-bench Pro success.