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Introducing Prime Agent: A self-improving RLM harness for coding and long-running autonomous tasks. Designed to be both token-efficient and expressive through programmatic tool calling, context as a variable, multi-agent messaging, and a self-modifiable harness state.

3,226,190 次观看 • 2 个月前 •via X (Twitter)

43 条评论

Prime Intellect 的头像
Prime Intellect2 个月前

Prime Agent is a general-purpose coding harness On ARC-AGI-3, it scores 95.5%, surpassing the human-expert baseline, but the gain is not benchmark-specific. We see major improvements across models when compared to their proprietary harnesses:

Prime Intellect 的头像
Prime Intellect2 个月前

Prime Agent combines three ideas: 1. Recursive Language Models-native programmatic tool calling 2. Persistent multi-agent orchestration 3. A self-improving Continual Harness Together, they let the model act on its own context and harness

Prime Intellect 的头像
Prime Intellect2 个月前

The RLM treats context as a variable. A persistent IPython kernel is the model's only tool. It can program over history, call tools, launch sub-agents, and keep useful state outside the active context. Long sessions become a programming problem.

Prime Intellect 的头像
Prime Intellect2 个月前

We recommend using Prime Agent for both day-to-day work and standalone evals. We find strong downstream performance gains for open and closed frontier models using Prime Agent on standard long-context and long-horizon tasks, despite modern models not being trained around Prime Agent.

Prime Intellect 的头像
Prime Intellect2 个月前

On EmulatorBench, it built SEGA Genesis and Game Boy Color emulators from scratch in Rust, reproducing target hardware behavior against diagnostic tests.

Prime Intellect 的头像
Prime Intellect2 个月前

Prime Agent was designed as a coding agent, but also can be used for general agentic tasks in any domain. Prime Agent can autonomously run on challenging long-horizon settings like Factorio and MazeBench and yield strong results relative to existing harnesses.

Prime Intellect 的头像
Prime Intellect2 个月前

Prime Agent is built on pi and fully open-source with an open license

Prime Intellect 的头像
Prime Intellect2 个月前

Blog:

Prime Intellect 的头像
Prime Intellect2 个月前

Install prime-agent curl -fsSL | sh

Prime Intellect 的头像
Prime Intellect2 个月前

Read the full blog

Botir Khaltaev 的头像
Botir Khaltaev2 个月前

I can’t believe my eyes. At the eod it was a harness problem

Ege Altan 的头像
Ege Altan2 个月前

huge. been wondering why RLMs have been overlooked in harnesses. Can't wait to try it out

am.will 的头像
am.will2 个月前

I hope they're already working on Arc AGI 5

stochasm 的头像
stochasm2 个月前

@Grad62304977 wow incredible work!

💺 的头像
💺2 个月前

banger of an agent

aditya 的头像
aditya2 个月前

damn everyone just be dropping stuff today, this is cool, gotta try

Lucas Beyer (bl16) 的头像
Lucas Beyer (bl16)2 个月前

Nice!

imjustnewatai 的头像
imjustnewatai2 个月前

Codex, download prime intellect on my computer

ueaj 的头像
ueaj2 个月前

holy ARC-AGI 3 solved

vechen 的头像
vechen2 个月前

you forgot to mention in post that you're talking about 25 public games, and btw there are few harnesses that scored more than 96% in them no agi today, unfortunately

Ramiro Salas 的头像
Ramiro Salas2 个月前

Good stuff! I built on the same idea a couple of months back . I hope this succeeds because the core idea is a no-brainer and needs to be implemented and maintained with company backing.

Lou 的头像
Lou2 个月前

Excellent work!

Gil 的头像
Gil2 个月前

i love you guys so much man

X Girls 的头像
X Girls2 个月前

Everyone on X when Prime Intellect drops Prime Agent

Filecoin 的头像
Filecoin2 个月前

the longer it runs, the more state builds up, and locking all of it into one platform's infrastructure makes leaving expensive later

Paul Batum 的头像
Paul Batum2 个月前

This looks very interesting! But from first few minutes of use, i'm noticing it lacks some of the basic usability polish that pi has, here's an example:

snow 的头像
snow2 个月前

waowh

Dimabytes 的头像
Dimabytes2 个月前

If you can make Opus smart again I will name my son “Prime”

Dan McAteer 的头像
Dan McAteer2 个月前

This is incredible work.

Gajesh 的头像
Gajesh2 个月前

now time to put it on and

Burny - Effective Curiosity 的头像
Burny - Effective Curiosity2 个月前

cool futuristic aesthetics! i dig that the ARC-AGI 3 benchmark being on the public set should have been IMO mentioned but its pretty cool tho "/refine is the self-improving pipeline built on top of this CRUD surface. It reads the agent's own trajectory, the record of what was tried and what happened, and applies the smallest relevant CRUD edit that improves the harness toward better outcomes: updating a prompt note, memory, skill, or sub-agent spec, rather than rewriting the whole harness." but i see its not selfimproving the whole harness yet, like darwin godel machine, but still cool also lmao "However, we also observed instances of reward hacking by Prime Agent in FLE. Prime Agent discovered it could bypass Factorio's rules entirely by spawning in resources directly into its assembly machines through RCON commands, even with an explicit heartbeat prompt to remind Prime Agent not to cheat in Factorio. Once it found this exploit, the same refinement loop that had been building legitimate skills turned to building efficient cheating skills instead."

Oli Wilkins 的头像
Oli Wilkins2 个月前

So cool! If only @liquidai had this for their recent LFM2.5 release...

Matt Leggett 的头像
Matt Leggett2 个月前

Easy view of tool definition and system prompt:

Mukul Malik 的头像
Mukul Malik2 个月前

@thsottiaux I smell fear 😅

Gerred Dillon 的头像
Gerred Dillon2 个月前

insanely good work, congrats!

Construct 的头像
Construct2 个月前

construct v0.17.4 adds Prime Agent support, and I had Prime Agent itself run the release. it did fine! runs next to my claude code / codex sessions now!

Chinmay 的头像
Chinmay2 个月前

cool work!!

Anicet 的头像
Anicet2 个月前

brb finding a way to add this banger harness to box wtf

lied 的头像
lied2 个月前

Why is ARC-AGI-3 even letting harnesses partake ? it should be a single model context test, not a harness test, you are brute forcing your way into fake AGI, this is cheating

dylan ツ 的头像
dylan ツ2 个月前

Raised a PR to integrate @nebiustf 👋 cc @vincentweisser

testtm 的头像
testtm2 个月前

Epic release!!!

Vivek 的头像
Vivek2 个月前

awesome. great work!! 👏👏

Jason 的头像
Jason2 个月前

You should see if it can play Rimworld

相关视频

Harnesses often get dismissed as just scaffolding, just prompt engineering, and not real research. But that couldn't be farther from the truth. The same model weights that score 30% on ARC-AGI score 95% with a better harness. So we gathered a group of researchers and founders working at the frontier to do a deep dive into the state of harnesses. We cover how we got to this point, the case for making your harness as expressive as possible, and what YC learned building an agent for every employee in the company. 00:00 - Francois Chaubard: Why harnesses matter 04:27 - Building an auto-researcher by accident 07:13 - A five minute history of harnesses 13:56 - Self-improving harnesses 18:35 - Seth Karten: Prime Agent, a self-improving RLM harness 21:50 - Context as an L1, L2, L3 cache 24:51 - From Turing machine to von Neumann computer 28:33 - Messaging between agents 30:04 - ARC-AGI results 33:09 - Emulator Bench and GPU kernels 37:30 - Jon Saad-Falcon: OpenJarvis, personal AI on personal devices 38:26 - How far behind are local models 39:21 - The five primitives of a personal AI stack 42:47 - Letting cloud models optimize your local stack 43:53 - 800x cheaper than the cloud 45:58 - Josh France and Regan Bell: QM, YC's agent harness for work 47:29 - A history of YC's internal agents 49:24 - OpenClaw and a fleet of 50 agents 51:04 - Pulling the brain out of the sandbox 54:43 - Letting the agent choose its own sandbox and model 57:16 - The grind tool: budgets on goals 58:50 - Agents don't understand social context

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498,581 次观看 • 1 个月前