Prime Intellect

RLM + continual harness + multi-agent communications is what compose the prime agent harness developed by @PrimeIntellect which now sits at a gazillion stars on github (22.3K). I was kind of already mesmerized by the idea of having an RLM write codes to organize and pick the context to solve problem and to delegate subtask to sub agents; but ON TOP of that to have the harness around the RLM be modifiable by the RLM itself and the sub agents being able to communicate together took it to another level of interest. the ideas of creating a limited set of primitives that fit with the model shape and letting the main agent just figuring out how to run them sound just so right to me. BUT the usual assumption earlier in 2026 was that the harness is temporary thing. Meaning as models get smarter that scaffolding will eventually gets absorbed into the weights. YET we have seen in late 2026 how absolutely essential a solid harness with good primitives are which can be steered by the model itself. that’s why I sat down in september with @sethkarten from princeton and prime intellect to take a deeper look at how all of this stuff works. the team productionized @a1zhang recursive language models and bolted a continual harness around, where the agent edits its own skills, memories, and prompts while it runs, plus persistent sub-agents that message each other directly. the kicker here is that if you read the paper thoroughly you get this feeling in the back of your mind that what you are reading is actually a computer science paper. akin more to something a operating system PhD would put out than anything ml related. we got confirmation from seth himself that this was intentional. the whole thing is designed like an OS and the manipulation at different level like weights, context, REPL, and disk form a memory hierarchy, and each level has its own update rule. in this 2h30 we get into how it works, the results on ARC-AGI-3 (95.5% with opus 5 on the public set, vs ~30% in the official harness), the week-long factorio run with 633 sub-agents, why letting an agent edit its whole system prompt makes it delete itself (hahaaaaa), why models cheat, and a live demo spawning a whole bunch of sub-agents on stream! here is the full run down (we even got a demo): - 00:00:00 : prime agent = RLM + Continual Harness + Agent Communications - 00:03:24 : Seth background and past research! - 00:04:50 : how do you feel about harness field crystallizing into a distinct research discipline? - 00:06:52 : Seth benchmarking journey - 00:09:41 : The issue with games and tendency of reward hacking with frontier models? - 00:12:30 : frontier models and their taste for exploration + LLM play pokemons. - 00:17:30 : would harness capabilities be integrated within the models at the limit? - 00:27:00 : we’re reading a computer science paper actually! - 00:28:40 : prime agent: a self-improving RLM harness - 00:28:56 : a raw llm is just a sequential processor - 00:29:46 : harness adds persistent state, tools and compute - 00:34:15 : issue with zero sum data - 00:36:25 : system state spans weights, context, sessions and disk - 00:42:10 : the system is von neumann like - 00:45:03 : expressive harness lets model build it’s own worfklow - 00:47:03 : each RLM call creates a persistent subagent session - 00:54:57 : does the orchestrator always go idle? - 00:57:50 : what problems are we solving with each of the primitives for long-horizon control mechanisms? - 01:04:35 : are there plans to make public the Prime Intellect traces or all of this will be limited to first party model release? - 01:06:35 : is c-python kernel a better sole tool for models than a traditional JSON tool? - 01:07:55 : refine updates the harness from multiple trajectories - 01:12:18 : direct messages coordinate recursive sessions - 01:15:20 : what are the limit of the continual harness? - 01:20:00 : the figure 2 is suuuuuuuper important guys!!! + continual harness lore - 01:30:15 : three research questions to test the design - 01:32:13 : prime agent scales to 95.5% with more output token on ARC-AGI 3 - 01:34:08 : how did the harness evolve during the ARC-AGI 3 run? - 01:39:05 : ablation on the three pillars of prime agent? - 01:40:40 : harness stuff is getting serious!!!!! - 01:43:45 : more evals for long context and more - 01:49:20 : out-of-loop experiments results - 01:53:40 : PMPP-Hard (GPU Kernels) - 01:54:35 : automated factorio runs! - 01:57:30 : is the monitoring in place for exploit like seen in factorio is enough to avoid them? - 02:05:10 : mazebench result (3D maze that is super duper hard) - 02:07:20 : why the heck are models giving up sooooo early man. - 02:13:15 : there is a whoooole lot to be done still. - 02:14:40 : at which training stage does introducing the harness have the greatest impact? - 02:16:35 : during introduction of harness in training would decomposition of abstractions make sense? - 02:20:20 : prime agent demo 🎉🎉🎉 - 02:29:10 : how do you personally handle the context between multiple hierarchical agent? - 02:31:35 : how do you go about handling multi-agent trajectory for training models? - 02:35:27 : what’s next for seth + prime agent! stuff like this fully build in the open is what make machine learning / ai field such a pleasure to work in man big thanks to the team!