Why is Jev so much faster than LLMs?
I came across a reverse-engineering article about Jev, and what I found particularly interesting was its inference architecture.
A normal LLM is fundamentally autoregressive:
input → token₁ → token₂ → token₃ → ... → tokenₙ
Even if all you need is:
is_this_safe: bool
route_to: one_of[agent_a, agent_b, agent_c]
priority: 1..5
you're still using a model whose fundamental interface is "generate text."
Jev takes a very different approach.
According to the reverse-engineering analysis, the architecture may look roughly like this:
┌─ Noul → P(true)
│
state encoder ───┼─ Choice → P(option₁...optionₙ)
│
└─ Score → distribution / expectation
The key idea is that the state can be encoded once, while multiple independent decisions can be evaluated against the same representation.
So instead of:
LLM → generate JSON → parse → validate → execute
you can do:
state → decision model → typed result → execute
And because the output isn't a sequence of tokens that needs to be generated one by one, the amount of sequential decoding work can potentially be reduced significantly.
This is also what allows Jev to evaluate multiple questions against the same state in a single request.
What's interesting is that the optimization isn't necessarily:
make the LLM smaller
but rather:
remove unnecessary sequential computation from the interface.
LLMs are optimized for generating arbitrary sequences.
Jev, on the other hand, is optimized for answering bounded questions that software can act on directly.
If this architecture is close to what TypeSafe is actually doing internally, I think that's much more interesting than simply calling Jev a faster LLM.
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