Shout-out to anthropic for having their models have such a strongly distinct writing style and personality that you can recognize their work instantly! It's quite nice to have such an immediate signal that if I were to proceed, I would spend orders of magnitude more time and effort reading the the text than the person claiming author credit spent writing or even reading it themselves.
Show HN: DeepSeek-V4 Latent Reasoning – moving "thinking" into latent space
21–30 of 30 posts
Re: Show HN: DeepSeek-V4 Latent Reasoning – moving "thinking" into latent space
#22Am I missing something or the evals do not compare it to the baseline deepseek-v4-flash? Without a baseline comparison, it is hard to tell what works well and what doesn't
Re: Show HN: DeepSeek-V4 Latent Reasoning – moving "thinking" into latent space
#23I've been hearing forever that latent reasoning is the next step. But none of the frontier labs seem to incorporate it. Any idea why? It doesn't sound so technically challenging. Sure you loose some interpretability but surely everyone would sacrifice that for a step-up in quality. Or at least offer a latent-reasoning-version of the model.
I wanted a knob to keep thinking going until it was sure it was done.
Re: Show HN: DeepSeek-V4 Latent Reasoning – moving "thinking" into latent space
#24AI2027.com puts this around early 2027 and treats the loss of legible chain-of-thought as a major negative turning point for alignment.
Re: Show HN: DeepSeek-V4 Latent Reasoning – moving "thinking" into latent space
#25Cool idea. I didn't understand what causes the bad response on the first query. Does it mean the first response in every new conversation, or just the first served response after startup?
Re: Show HN: DeepSeek-V4 Latent Reasoning – moving "thinking" into latent space
#26I've been hearing forever that latent reasoning is the next step. But none of the frontier labs seem to incorporate it. Any idea why? It doesn't sound so technically challenging. Sure you loose some interpretability but surely everyone would sacrifice that for a step-up in quality. Or at least offer a latent-reasoning-version of the model.
you lose CoT monitorability which is a big issue since models have become quite powerful and also often deceptive but i do think that efficiency pressure will keep nudging us toward latent reasoning. looped language models are an active research area. but imagine not being able to monitor mythos' thoughts as it's working through a national security need...
Re: Show HN: DeepSeek-V4 Latent Reasoning – moving "thinking" into latent space
#27LinkedIn recently added a “seems like AI slop” button. I.e.: independent of downvoting/not interested/flagging as spam/ToS violation, you can say “this is AI slop”. Maybe we need something like it here
Re: Show HN: DeepSeek-V4 Latent Reasoning – moving "thinking" into latent space
#28Re: Show HN: DeepSeek-V4 Latent Reasoning – moving "thinking" into latent space
#29Shout-out to anthropic for having their models have such a strongly distinct writing style and personality that you can recognize their work instantly! It's quite nice to have such an immediate signal that if I were to proceed, I would spend orders of magnitude more time and effort reading the the text than the person claiming author credit spent writing or even reading it themselves.
Re: Show HN: DeepSeek-V4 Latent Reasoning – moving "thinking" into latent space
#30Earlier quoted context omitted.
you lose CoT monitorability which is a big issue since models have become quite powerful and also often deceptive but i do think that efficiency pressure will keep nudging us toward latent reasoning. looped language models are an active research area. but imagine not being able to monitor mythos' thoughts as it's working through a national security need...
LLMs already think in latent space. The generated reasoning tokens are only the surface of what's happening internally. An LLM may write one thing in the traces but decide differently in the latent space. The whole token-based "reasoning" thing was just a clever hack to extend the existing architecture without completely redoing it. In one of Anthropic's recent papers, they added an additional subnetwork trained to m…
Therefore output tokens are decodeable, but are trained compressed. So they are approximations of faster thinking.
Interpretability is a mixed bag even with trained tools on top of existing models.