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	▲	wongarsu 5 hours ago
		I just tried it on qwen3-embedding:8b with a little vibe-coded 100 line script that does the obvious linear math and compares the result to the embeddings of a couple of candidate words using cosine similarity, and it did prefer the expected words. Same 22 candidates for both questions king - man + woman ≈ queen (0.8510) `Top similarity 0.8510 queen 0.8025 king 0.7674 princess 0.7424 woman 0.7212 queen Elizabeth` Berlin - Germany + France ≈ Paris (0.8786) `Top similarity 0.8786 Paris 0.8309 Berlin 0.8057 France 0.7824 London` Sure, 0.85 is not an exact match so things are not exactly linear, and if I dump an entire dictionary in there it might be worse, but the idea very much works Edit: after running a 100k wordlist through qwen3-embedding:0.6b, the closest matches are: `king – man + woman ≈ queen (0.7873) berlin – germany + france ≈ paris (0.9038) london – england + france ≈ paris (0.9137) stronger – strong + weak ≈ weaker (0.8531) stronger – strong + nation ≈ country (0.8047) walking – walk + run ≈ running (0.9098)` So clearly throwing a dictionary at it doesn't break it, the closest matches are still the expected ones. The next closest matches got a lot more interesting too, for example the four closest matches for london – england + france are (in order) paris, strasbourg, bordeaux, marseilles