Prime Intellect

Over two weeks, Prime Agent orchestrated a swarm of over 2,000 agents to rewrite itself in Rust.

With 10,000+ sandboxes, 200B+ GLM-5.3 tokens, and 16,000 agent-to-agent messages, this was our largest test yet of Prime Agent’s multi-agent capabilities and our infrastructure powering these agent swarms.

Prime Agent now reaches usable input ~13× faster and uses 83% less startup memory than before.

Inference scaling works, but every agent eventually faces the same problem: its context window fills up.

In this essay, we argue the only scalable fix leads straight to swarms.

Over two weeks, Prime Agent orchestrated a swarm of over 2,000 agents to rewrite itself in Rust.

With 10,000+ sandboxes, 200B+ GLM-5.3 tokens, and 16,000 agent-to-agent messages, this was our largest test yet of Prime Agent’s multi-agent capabilities and our infrastructure powering these agent swarms.

Prime Agent now reaches usable input ~13× faster and uses 83% less startup memory than before.

Over two weeks, Prime Agent orchestrated a swarm of over 2,000 agents to rewrite itself in Rust.

With 10,000+ sandboxes, 200B+ GLM-5.3 tokens, and 16,000 agent-to-agent messages, this was our largest test yet of Prime Agent’s multi-agent capabilities and our infrastructure powering these agent swarms.

Prime Agent now reaches usable input ~13× faster and uses 83% less startup memory than before.

Over two weeks, Prime Agent orchestrated a swarm of over 2,000 agents to rewrite itself in Rust.

With 10,000+ sandboxes, 200B+ GLM-5.3 tokens, and 16,000 agent-to-agent messages, this was our largest test yet of Prime Agent’s multi-agent capabilities and our infrastructure powering these agent swarms.

Prime Agent now reaches usable input ~13× faster and uses 83% less startup memory than before.

Over two weeks, Prime Agent orchestrated a swarm of over 2,000 agents to rewrite itself in Rust.

With 10,000+ sandboxes, 200B+ GLM-5.3 tokens, and 16,000 agent-to-agent messages, this was our largest test yet of Prime Agent’s multi-agent capabilities and our infrastructure powering these agent swarms.

Prime Agent now reaches usable input ~13× faster and uses 83% less startup memory than before.

Over two weeks, Prime Agent orchestrated a swarm of over 2,000 agents to rewrite itself in Rust.

With 10,000+ sandboxes, 200B+ GLM-5.3 tokens, and 16,000 agent-to-agent messages, this was our largest test yet of Prime Agent’s multi-agent capabilities and our infrastructure powering these agent swarms.

Prime Agent now reaches usable input ~13× faster and uses 83% less startup memory than before.

Inference scaling works, but every agent eventually faces the same problem: its context window fills up.

In this essay, we argue the only scalable fix leads straight to swarms.

Open superintelligence accelerates open scientific research.

The Alzheimer’s Translation Challenge will share a 150M cell atlas on the Alzehimer's Disease workbench and Prima Mente's modeling platform.

Prime Intellect is lending infrastructure support to researchers to build models based on this dataset. The best hypotheses will be tested in Prima Mente's wet lab.

Today we're launching the Alzheimer's Translation Challenge, a global AI competition to discover new therapeutic hypotheses for Alzheimer's disease.

At its core is a new 150M cell atlas of neurons, astrocytes, and microglia across different genetic backgrounds under combinatorial perturbations, with multi-modal readouts.

The data will be made available through the AD workbench, Hugging Face and Prima Mente's modeling platform.

The challenge is led by @PrimaMente and @AlzData in collaboration with @nvidia, @huggingface, @nebiusai, Talisman Therapeutics, @UltimaGenomics, @cellanome, @PrimeIntellect, @GoodfireAI, and @boltz_bio.

In 1906, Alois Alzheimer described a new disease of the brain. After 120 years of progress, we still don't fully understand what drives this disease. We want to change that.

Participants will use the dataset to build AI models, assessed through a series of evals from general benchmarks to more complex tasks.

Top teams will be shortlisted, and their experimental hypotheses tested in Prima Mente's wet lab. Teams win when their hypotheses demonstrate the predicted molecular and functional effect.

Frontier Time Series Language Models struggle with precise anomaly localization over long contexts, when useful signals might be hidden in hours of recordings.

In our new paper we trained a tiny Recursive Language Model to SoTA performance on long context time series.

We introduce AnomalyXL, an infinitely scalable synthetic time-series generator with programmatic ground truth answers.

Training our RLM formulation on this RL environment transfers to real world anomaly detection in Software Observability, ECG and Sleep data.

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