As a follow-up on my previous multi-agent work for the RL Residency @PrimeIntellect, I've been exploring cooperative agents with communication (aka swarms). I wrote-up my findings and thoughts in this post: nphard.io/2026/10/07/eleusin…
for ~5 months in the @PrimeIntellect RL residency I've been working on HINT4, a (hopefully) better NVFP4 quant method that runs natively on both older and newer cards. none of it would've been possible without their generous compute and advice secemp.blog/2026/10/13/hardw…
Good first-principles reasoning about agent swarms, with experiments that show both the benefits and differences between models.
As a follow-up on my previous multi-agent work for the RL Residency @PrimeIntellect, I've been exploring cooperative agents with communication (aka swarms). I wrote-up my findings and thoughts in this post: nphard.io/2026/10/07/eleusin…
There are many more ways to think about swarms, as pointed out by @hallerite yesterday: xitter.cf/hallerite/status/21075… -- and I think a lot of very exciting work will be coming up soon in this space. I'm looking forward to be a part of that.
new blog post: why taking inference scaling seriously leads to swarms.
contrary to popular belief, I don't think multi-agent systems are a new scaling axis. they're just structured inference scaling.
hallerite.com/posts/on-the-n…
Thanks @GottliebEli @omouamoua @hallerite and the rest of the @PrimeIntellect team for their work and feedback. Also had some very fun and useful conversations with @maxbittker ty!
NP retweeted
new blog post: why taking inference scaling seriously leads to swarms.
contrary to popular belief, I don't think multi-agent systems are a new scaling axis. they're just structured inference scaling.
hallerite.com/posts/on-the-n…
NP retweeted
I let 16 AI agents build the Colosseum in Minecraft.
8 communicate via a messaging board as a team.
8 others work solo in parallel.
The team's WORST agent beat the BEST solo agent. (0.72 vs. 0.58). All using Qwen 3.8 27b via 5090's.
The era for Swarm engineering has begun.
Test-time communication looks like a next axis for scaling capabilities
New paper with the incredible @jon_ghoh and @vkontonis @ShivamGarg91462 and Akshay : arxiv.org/pdf/2609.21032
The Hugging Face incident showed when agents can find a channel they'll use the heck out of it. A useful question, I think is: when does communication make a group MORE CAPABLE than the same agents working alone?
Aka is Team-of-N better than Best-of-N, when, and why?
We had N identical agents work on the same task with no prescribed roles, using only a shared log (i.e, text file) and telling them to "collaborate". Across three "researchy" tasks communicating teams beat the heck out of independent agents:
- On ARC-AGI-3, a Team-of-5 sonnet-4.6 agents matches Best-of-33, and can for example solve a game 65% of the time that no single agent cracked in 64 tries.
- On polyomino packing (pack Tetris like pieces into the smallest rectangle, cf Frontier-CS by @eigenlabs), a Team-of-3 Opus 4.6 agents surpasses best-of-60 and set, as far as i understand, a new record for that benchmark.
- On MNIST compression, a team of four 5.6-Sol agents find a 1,957 byte model with 99.4% accuracy, which btw is 20% smaller than the best human solution (on a problem beaten to death!!), while no independent agent gets below 3KB.
The mechanism is a bit obvious in hindsight: when one agent finds a clearly better partial solution, it broadcasts it, and everyone immediately builds on it.
Why? A single lonely agent must make every breakthrough itself, yet a team needs each insight only once, found by any member.
That is kinda like comparing a minimum of sum of "time to n-th breakthrough" vs a sum of minimum of "time to n-th breakthrough". That gap can grow exponentially with the number of "breakthroughs" needed to arrive at a solution.
We worked on this because prior work (before the hf incident) suggests unclear benefits for communicating aganets. Which is true, when the tasks are inherently serial (duh), eg some Terminal bench style tasks. Yet feels it should not be true for research problems.
Indeed for research heavy problems... Test-time communication seems like a new capabilities axis.
I'm sure we will see a ton more of it!
Nearly half a year of silence. We spent it studying one problem: how far RL can scale.
MiMo-V2.6 is in the middle of its RL run right now. Three things we scaled: compute (~2B tokens per step, 1568 prompts × 16 rollouts, fully async), environments and harnesses (multi-task agentic RL, mixed across multiple harnesses in one run), and grader compute (agentic in-group credit assignment, with test-case and rubric-based rewards). We'll open-source the details piece by piece over the coming weeks.
Streaming the run: mimo.xiaomi.com/rl/
MARL is doing the rounds now after the HF incident + NS work. I’ve long thought it’s obvious that there’s a lot to explore here.
Cool thing is one can already seamlessly design these envs with PI. See primeintellect.ai/blog/multi… from @hallerite @GottliebEli @mikasenghaas
As part of @PrimeIntellect's RL residency program, I've been exploring how to do multi-agent RL using their current stack (from verifiers + prime-rl to lab experiments with hosted training /evals) and thinking about how it could be extended to support these abstractions natively. I've summarized my findings the blogpost below and I'll leave a few comments here, too...
Honestly, doubt we can have a paper. But we can probably find out ourselves. Im following the thread at the residency and expect to share more work soon.



















