text classification is equivalente to decision. This is exactly the same thing Jev does.

If it has four legs, a tail and barks why not call it a dog?

	▲	seemaze 7 minutes ago \| parent \| next [-]
		This dog only barks when given biscuits
	▲	gchamonlive an hour ago \| parent \| prev [-]
		Because this specific dog only barks in structured text

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ricardobeat an hour ago | parent | prev | next [-]

It is not. In a benchmark with actual decisions - navigation, traffic, waypoints - laya does slightly better than a small classifier, with very low correlation to state changes.

	▲	cobanov an hour ago \| parent [-]
		[dead]

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abirch an hour ago | parent | prev [-]

Jev does it more efficiently because it doesn't use an LLM https://typesafe.ai/blog/introducing-system-one-models-and-j...

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rockinghigh 16 minutes ago | parent [-]

Their marketing language is misleading. They must still use some transformer language model backbone to encode the text input (BERT or decoder-only LLM). The biggest difference is the output, instead of auto-regressively generating tokens, they produce probabilities over a bounded set of decisions (more flexible classification).

	▲	kgeist 8 minutes ago \| parent [-]
		Stock LLMs already internally produce probabilities before selecting the next token, they're just currently predominantly post-trained for chat/tool calls, not for selecting options A/B/C in the first token.