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Introducing flow-1, our new model trained with RL to find errors in agent traces. It matches GPT-6-sol in trace intelligence while being 23x cheaper. It also costs 25% less to run than GPT-6-luna. flow-1 finally makes it possible to monitor and understand every agent run, without sampling. 1/6

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Robert

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244,417 görüntüleme • 8 gün önce •via X (Twitter)

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Robert profil fotoğrafı
Robert8 gün önce

flow-1 runs inside Signals agent - a harness deeply optimized for agent trace analysis. It operates like a coding agent, treating each trace like repo and each span like a file. As an input it takes a user prompt, which could as general as "find logical errors in this trace", an output schema, and a trace. It greps span content, reads tool results and returns structured findings.

Robert profil fotoğrafı
Robert8 gün önce

On our Signals benchmark of 523 difficult agent traces across coding, legal, customer support, healthcare and more, flow-1 matches GPT-6-sol in analysis quality. Sol flags more traces. flow-1 is more precise about which ones contain real failures.

Robert profil fotoğrafı
Robert8 gün önce

We compared costs by running the models inside the Signals agent on traces of various difficulty that have under 100k total tokens. flow-1 delivers GPT-6-sol-level analysis while processing 23x more traces per $1!

Robert profil fotoğrafı
Robert8 gün önce

We started with supervised fine-tuning on synthetic investigations of realistic workflows across software, legal, customer support, CRM and more. We then trained with RL inside the Signals agent to improve tool use, schema adherence and investigation of complex traces. Here’s how the training data breaks down by industry.

Robert profil fotoğrafı
Robert8 gün önce

With flow-1, production traces become a source of better evals, better agents and insight into what users need. Read the full post for results, training details and more examples of what flow-1 finds:

Hercules Giaretta Gimenes profil fotoğrafı
Hercules Giaretta Gimenes8 gün önce

Amazing launch, Robert and team!! Love what you're building and the direction Laminar is heading!

John Enevoldsen profil fotoğrafı
John Enevoldsen8 gün önce

Hell yeah, congrats Robert! This looks really cool.

Yechan Do profil fotoğrafı
Yechan Do8 gün önce

How to get AGI: 1. Train a model that can generate response 2. Train a model that can detect errors at the previous model. 3. Repeat 1~2

Gaurav Atavale profil fotoğrafı
Gaurav Atavale8 gün önce

Very interesting! won't it lose generalization ability if you do this?

Robert profil fotoğrafı
Robert8 gün önce

It’s not a general model, it’s purpose built for trace analysis and works primarily in our Signals harness

Gaurav Atavale profil fotoğrafı
Gaurav Atavale8 gün önce

excellent idea if the boundary is well defined!

yersultan profil fotoğrafı
yersultan8 gün önce

congrats guys!

Rakhman Asmatullayev profil fotoğrafı
Rakhman Asmatullayev8 gün önce

analyzing 100K agent traces of 50k tokens each costs under $250 with flow-1. crazy

Maxi profil fotoğrafı
Maxi8 gün önce

Congrats on the launch!

kenneth profil fotoğrafı
kenneth8 gün önce

super cool!

Roko ʕ •ᴥ•ʔっ🪄✨🐍 profil fotoğrafı
Roko ʕ •ᴥ•ʔっ🪄✨🐍8 gün önce

yummy @ClevrPwn

Shabah profil fotoğrafı
Shabah8 gün önce

Is error detection a classification problem?

AI Psychosis profil fotoğrafı
AI Psychosis8 gün önce

Why would I use this over OAI or Anthropic? Lmao

Ben Newman profil fotoğrafı
Ben Newman8 gün önce

Beautiful video btw

Eris Margeta:: ubi:// profil fotoğrafı
Eris Margeta:: ubi://8 gün önce

Incredible tracing solution

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