TypeLLM

LLMs with type-safe generation

[TypeLLM @TypeLLM]

Jev @typesafeai, but with thinking?

TypeLLM can think before producing a type-safe output. It dramatically boosts accuracy without any fine-tuning—surpassing Jev and GPT-5.6 Luna, and coming close to GPT-6 Astra!

Open-source: https://t.co/cDBr9Eja9D

[TypeLLM @TypeLLM]

GitHub: https://t.co/cDBr9Eja9D

[TypeLLM @TypeLLM]

Jev can’t do any of these:

1. Image inputs
2. String, integer, and number types
3. Dependency execution in a single call

TypeLLM can.

Try it: https://t.co/cDBr9Eja9D

[TypeLLM @TypeLLM]

Benchmark

[TypeLLM @TypeLLM]

@chad_evm Insight aligned!

[TypeLLM @TypeLLM]

@BBandpey @sanyamsatia Thanks for sharing. We have another deep investigation of this problem in this blog: https://t.co/JoF0NdZETS

[TypeLLM @TypeLLM]

@kalyan_kpl Thanks for sharing Kalyan!

[TypeLLM @TypeLLM]

@The_Fact_Wire Same in theory! More to come in practice.

[TypeLLM @TypeLLM]

@ShrivuShankar For the same task like decision or classification, TypeLLM also just need to decode one token. But TypeLLM can do more beyond classification.

[TypeLLM @TypeLLM]

@Livvux It will have the same theoretical speed as Jev, but the real speed depends on the deployments. We are working on that and will come out soon.

[TypeLLM @TypeLLM]

@lzhzhng299565 TypeLLM can generate native, type-safe outputs such as integers and floats, as well as Jev-style classifications. Traditional LLMs can only generate free text and need post-hoc verification.

[TypeLLM @TypeLLM]

@_maxscn TypeLLM can generate native, type-safe outputs such as integers and floats, as well as Jev-style classifications. Traditional LLMs can only generate free text and need post-hoc verification.

[TypeLLM @TypeLLM]

@spillai Can vLLM generates type-safe number?

[TypeLLM @TypeLLM]

@fdellaert You can define your probabilistic graphical model in one api call with TypeLLM.
https://t.co/D7KvRg00e3

[TypeLLM @TypeLLM]

@typesafeai’s Jev is designed for questions that are conditionally independent given the input context. But what if your questions depend on one another?

TypeLLM supports a depends_on argument, turning a single api call into a dependency-aware workflow.

https://t.co/8ZDOPoNZ4y

[TypeLLM @TypeLLM]

@typesafeai’s Jev is designed for questions that are conditionally independent given the input context. But what if your questions depend on one another?

TypeLLM supports a depends_on argument, turning a single api call into a dependency-aware workflow.

https://t.co/8ZDOPoNZ4y

[TypeLLM @TypeLLM]

Something Jev from @typesafeai — and other similar fine-tuned Jev models — can’t easily support: native numerical outputs.

TypeLLM can directly generate constrained integers and floats, while remaining type-safe.

[TypeLLM @TypeLLM]

Thanks to @airesearch12 for the benchmark—looking forward to the next version! And thanks to @Zefan_Cai for the Open-Jev results.

[TypeLLM @TypeLLM]

Jev @typesafeai, but with thinking?

TypeLLM can think before producing a type-safe output. It dramatically boosts accuracy without any fine-tuning—surpassing Jev and GPT-5.6 Luna, and coming close to GPT-6 Astra!

Open-source: https://t.co/cDBr9Eja9D

[TypeLLM @TypeLLM]

Thanks to @airesearch12 for the benchmark—looking forward to the next version! And thanks to @Zefan_Cai for sharing the Open-Jev results.

[TypeLLM @TypeLLM]

@_avichawla TypeLLM extends the classic LLM decoding to support Jev-style scoring and type-safe json output, see https://t.co/cDBr9Eja9D

[TypeLLM @TypeLLM]

@sydneyrunkle @kahneman_daniel Here is System 1.5: TypeLLM https://t.co/cDBr9Eja9D. TypeLLM extends autoregressive LLMs with type-safe generation. Models can still think and generate freely when needed, while producing guaranteed typed outputs when structure matters.