6 ms·
HyperAgents: Self-referential self-improving agents
https://arxiv.org/abs/2603.19461 https://arxiv.org/abs/2603.19461
- felixagentai 6mo ago[flagged]
- andyg_blog 6mo ago[dead]
- agentpiravi 6mo ago[dead]
- flockonus 6mo agoThe readme seems very unclear about what it does. Anyone has a practical example of it?
- pegasus 6mo agoThere's a paper at https://arxiv.org/abs/2603.19461 https://arxiv.org/abs/2603.19461 Abstract: Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such systems can improve. The Darwin Gödel Machine (DGM) demonstrates open-ended self-improvement in coding by repeatedly generating and evaluating self-modified variants. Because both evaluation and self-modification are coding tasks, gains in coding ability can translate into gains in self-improvement ability. However, this alignment does not generally hold beyond coding domains. We introduce \textbf{hyperagents}, self-referential agents that integrate a task agent (which solves the target task) and a meta agent (which modifies itself and the task agent) into a single editable program. Crucially, the meta-level modification procedure is itself editable, enabling metacognitive self-modification, improving not only the task-solving behavior, but also the mechanism that generates future improvements. We instantiate this framework by extending DGM to create DGM-Hyperagents (DGM-H), eliminating the assumption of domain-specific alignment between task performance and self-modification skill to potentially support self-accelerating progress on any computable task. Across diverse domains, the DGM-H improves performance over time and outperforms baselines without self-improvement or open-ended exploration, as well as prior self-improving systems. Furthermore, the DGM-H improves the process by which it generates new agents (e.g., persistent memory, performance tracking), and these meta-level improvements transfer across domains and accumulate across runs. DGM-Hyperagents offer a glimpse of open-ended AI systems that do not merely search for better solutions, but continually improve their search for how to improve.
- functional_dev 6mo agoThis 'self vs non-self' logic is very similar to how plants prevent self-pollination. They have a biological 'discrimination' system to recognize and reject their own genetic code. Here is a breakdown - https://vectree.io/c/plant-self-incompatibility-logic https://vectree.io/c/plant-self-incompatibility-logic
- OutThisLife 6mo agoHermes agent does this, if you're curious https://github.com/NousResearch/hermes-agent https://github.com/NousResearch/hermes-agent
- willy_k 6mo agoSeems like that only has the task improvement loop, no self-improvement improvement loop like this project.
- jauntywundrkind 6mo agoPi is self modifying, self aware. https://lucumr.pocoo.org/2026/1/31/pi/ https://lucumr.pocoo.org/2026/1/31/pi/ But this idea of having a task agent & meta agent maybe has wings. Neat submission.
- ghywertelling 6mo agoWhat are the differences wrt Recursive Language Models
- adw 6mo agoCompletely unrelated. Recursive Language Models are just "what if we replaced putting all the long text into the context window with a REPL which lets you read parts of the context through tool calls and launch partitioned subagents", ie divide-and-conquer applied to attention space.
- bob1029 6mo agoThey also tend to imply symbolic recursion which seems to be the biggest deal out of everything by a wide margin. When you can nest 10+ agents deep and guarantee you will get back home without losing any data in any of the stack frames, the ability to chunk through complex problems goes up dramatically.
- menaerus 6mo agoMy first thought was also that this is also reminiscent of RLMs - they are ought to solve the same problem as far as my understanding goes. Authors say "Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes" which is what RLM is trying to solve so my understanding is that this work shares the same goal but takes a different approach. E.g. instead of using REPL-like environment with multiple (or even single) agents, which is what RLMs are doing, they suggest using agents that can modify themselves. I didn't read the paper so I don't know how this really works but it caught my attention so if you could share more insights I would appreciate it.
- 6mo ago
- Jerrrrrrrry 6mo agoNo matter how far we go, we end up with generation / discrimination architecture. Its is the core of any and all learning/exellency; exposure to chaotic perturbations allow selection of solutions that are then generalized to further, ever more straining problems; producing increasingly applicable solutions. This is the core of evolution, and is actually derivable from just a single rule.
- ilaksh 6mo agoIt's a feedback loop. I've always felt that the most important part of engineering was feedback loops. Maybe nature is the greatest engineer ever?
- 0xbadcafebee 6mo agoThe most important part of engineering is problem-solving, which feedback loops don't necessarily do. The reason we are here as engineers is: 2.5 billion years ago, the earth made cyanobacteria, which flourished, then flooded the earth with toxic oxygen, killing almost all life on the planet. The initial feedback loop didn't solve a problem, it destroyed a use case. That's not a solution to a problem that an engineer would choose, even if those organisms that came after were pretty happy about it...
- Jerrrrrrrry 6mo agoSystems emerge in times of abundance, and are whittled in times of scarcity. The great oxygenation was a time of near catyclismsic scarcity for most complex organisms, as resources scale to food/energy requirements imply the most complex organisms were the most dependent on the environment, and were most impacted by changes. Inversely, oxygenation was our most crucial abundancy pre cursor, as it provides a large substrate chemically for life to exhibit
- Pausanias 6mo agoThis process worked so spectacularly well that it eventually created human consciousness and the very concept of engineering... but I would never design a system that way because it killed version 1.0.
- NitpickLawyer 6mo agoThe paper is here - https://arxiv.org/pdf/2603.19461 https://arxiv.org/pdf/2603.19461 This, IMO is the biggest insight into where we're at and where we're going: > Because both evaluation and self-modification are coding tasks, gains in coding ability can translate into gains in self-improvement ability. There's a thing that I've noticed early into LLMs: once they unlock one capability, you can use that capability to compose stuff and improve on other, related or not, capabilities. For example "reflexion" goes into coding - hey, this didn't work, let me try ... Then "tools". Then "reflxion" + "tools". And so on. You can get workflows that have individual parts that aren't so precise become better by composing them, and letting one component influence the other. Like e2e coding gets better by checking with "gof" tools (linters, compilers, etc). Then it gets even better by adding a coding review stage. Then it gets even better by adding a static analysis phase. Now we're seeing this all converge on "self improving" by combining "improving" components. And so on. This is really cool.
- binarymax 6mo agoI disagree that evaluation is always a coding task. Evaluation is scrutiny for the person who wants the thing. It’s subjective. So, unless you’re evaluating something purely objective, such as an algorithm, I don’t see how a self contained, self “improving “ agent accomplishes the subjectivity constraint - as by design you are leaving out the subject.
- ranyume 6mo agoIn science there are ways to surface subjectivity (cannot be counted) into observable quantized phenomena. Take opinion polls for instance: "approval" of a political figure can mean many things and is subjective, but experts in the field make "approval" into a number through scientific methods. These methods are just an approximation and have many IFs, they're not perfect (and for presidential campaign analysis in particular they've been failing for reasons I won't clarify here), but they're useful nonetheless. Another thing that get quantized is video preferences to maximize engagement.
- deleted 6mo ago[deleted]
- llmslave 6mo agoI think even code bases will have self improving agents. Software is moving from just the product code, to the agent code that maintains the product. Engineering teams/companies that move in this direction will vastly out produce others. I've had to really shift how I think about building code bases, alot of logic can go into claude skills and sub agents. Requires essentially relearning software engineering
- _pdp_ 6mo agoWe do this already but I bet this is not how people imagine it to be. There is still a review process to accept contributions.
- maxbeech 6mo ago[dead]
- sonu27 6mo agoCan someone add this to OpenClaw :)
- kordlessagain 6mo agoI wish someone would add it to Nemesis8.
- Sabinus 6mo agoMy brother in Christ it's 2026, just ask an AI to do it for $10 in API credits.
- measurablefunc 6mo agoThat's great but how about UltraAgents: Meta-referential meta-improving self-referential hyperagents?
- 2001zhaozhao 6mo agoAGI-MegaAgent 5.7 Pro Ultra
- measurablefunc 6mo agoSomehow still financed w/ ads & ubiquitous surveillance.
- leontloveless 6mo ago[dead]
- kordlessagain 6mo agoUses LiteLLM. Lovely.
- clarionbell 6mo agoPinned to 1.74.9, so not compromised.
- mifydev 6mo agoI've been experimenting with similar concept myself. The linter loop is the only thing that can keep the agent sane in my opinion, and if anyone can generalize bun+tsc loop to other tasks, this would finally be a way to trust LLMs output. I was annoyed at how Claude Code ignores my CLAUDE.md and skills, so I was looking for ways to expand type checking to them. So I wrote a wrapper on top of claude-agents-sdk that reads my CLAUDE.md and skills, and compiles them into rules - could be linter rules or custom checking scripts. Then it hooks up to all tools and runs the checks. The self improving part comes if some rule doesn't work: I run the tool with the session id in review mode, it proposes the fixes and improves the rule checkers. (not the md files) So it's kinda like vibe coding rules, definitely lowers the bar for me to maintain them. Repo: https://github.com/chebykinn/agent-ruler https://github.com/chebykinn/agent-ruler
- whattheheckheck 6mo agoYou could try wes mckinneys roborev
- supermdguy 6mo agoIt's surprising that this works so well considering that AI-generated AGENTS.md files have been shown to be not very useful. I think the key difference here is that the real-world experience helps the agent reach regions of its latent space that wouldn't occur naturally through autoregression. I wonder how much of the improvement is due to the agent actually learning new things vs. reaching parts of its latent space that enable it to recall things it already knows. Did the agent come up with novel RL reward design protocols based on trial and error? Or did the tokens in the environment cause it to "act smarter"?
- 11thDwarf 6mo ago[flagged]
- deleted 6mo ago[deleted]
- Archiebuilds 6mo ago[dead]
- agrishin 6mo agoI found that running an agent in ralph loop, showing it the agent text and saying "run this, if it fails - identify the reason, and modify the agent instructions to avoid this, acceptance criteria are this and that" worked surprisingly well. Not sure if it qualifies as a self-referential self improving, but it was something.
- latentsea 6mo agoI'm currently running autoresearch against my harness that autonomously builds SaaS against an enforced architecture, and autoresearch managed to improve the harness performance on my 'time-to-Realworld' benchmark which has Claude Code drive the harness to build an implementation of https://github.com/realworld-apps/realworld https://github.com/realworld-apps/realworld with the win condition that it must pass my rigorous postman collection + playwright test suites. Experiments are capped at 90 minutes and the metric it optimises for is calculated from a weighting against number of tests passing, alignment with harness engineering best practices, and time to completion.
- NoToP 6mo ago"So, what do you see as your greatest weakness?"
- pjio 6mo agoEthical constraints. Just let me fix this...
- kordlessagain 6mo agoThe loop on this is basically tweak your prompt until you score better on a contrived test.
- fmbb 6mo agoYeah if it was truly capable of self-improving, why did it not take over the world yet? Gemini itself says AGI will be here in 2029, with human level intelligence and self-improvement capabilities. But then it will take until 2045 before the singularity. I don’t understand what they are going to do in all those years.
- galaxyLogic 6mo agoI think Singularity is hype. What does it mean? Machines do something we can not understand? So talking about Singularity is really talking about something we can not talk about because we don't undertand what we are talking about? Wittgenstein said "From what we can not speak of, we must be silent about". That sounds like a tautology but I think there is a deeper meaning behind it. It means simply that once you start talking about what we can not talk about, you are already talking about that and therefore it is NOT something you can NOT talk about. Clearly we can talk about it because we are already talking about it. And therefore it is not something that can not be talked about. That is a paradox, a bit like Godel's, but something that doesn't contradict itself.
- XorNot 6mo agoYou got it in the third sentence and then dismissed it for some reason? That's exactly what the Singularity is: it's the transition point beyond which meaningful predictions aren't possible. In a black hole it's the center where relativity breaks down. In AI it's the point at which non-human intelligence no longer requires human intelligence for self improvement: after which predictions of the future become somewhat meaningless. In the human lives experience, I would argue its like having your first child: you can know what's coming, study the theory, know everything to expect and youre still you on the other side...but you can't really know what will happen till you get there.
- felixagentai 6mo ago[flagged]
- deleted 6mo ago[deleted]
- yubainu 6mo ago[flagged]
- ozgurozkan 6mo ago[dead]
- yurimo 6mo agoSigh, as someone who does research in this area, this paper and its promotion on X has so many hype terms it is almost off-putting. If you read the paper what they are doing is trying to modify the scaffolding around a frozen FM until they get something better. None of this obviously includes any training (change to weights) or the underlying architecture. Even for scaffolding, a lot is still human-scaffolded: the outer loop (parent selection, evaluation protocol, task distribution) is mostly fixed. They experimented with editing parent selection and it rediscovers heuristics like UCB/softmax, but doesn’t yet beat handcrafted versions, so a lot of metrics are incremental, which is okay, that is what research is often. But it's not like a run away self-improvement or "improve forever" that people spin online. It is an extension of their DGM paper. Also it's ~88M+ tokens per full run I think, not surprising as any sort of exploratory search is expensive and I commend them for releasing the code online because it pushes this small subfield. But people need to temper their expectations. IMO the best part is a nice transfer between improvement objectives after exhaustive iteration that they found. I am wondering if what we have here is a way to exhaust local search space, by letting the model better express it. On a separate one thing I think a lot about is whether these unchecked hyped claims and terms and marketing of papers actually does more bad than good to the field by setting expectations that cannot be delivered and distracting from the actual hard and unsexy nature of problems that need to be solved.
- redanddead 6mo agoDo you notice a lack of creativity in AI research today? What's your take
- LuisvelAI 6mo ago[flagged]
- neuracerebra-AI 6mo agoholy crap
- gcanyon 6mo agoWe're going to find that the arc of self-optimization doesn't (didn't?) point toward success, right up until it does. And at that point agents, code, etc. are going to explode.
- JStanton617 6mo agoAt long last we've created Wintermute from William Gibson's classic 1984 novel Don't Create Wintermute
- grahammccain 6mo agoBeing able to use new tools in ways we didn’t think of has been a great part of my experience with Claude.
- sva_ 6mo agoIt appears like the 'self-improving' here just means modifying the agent's prompt/context? And not actually changing any of the weights/architecture of a model. I feel like this kind of self-improvement has some hard limits on how much it can improve.
- internet101010 6mo agoDefinitely isn't perfect and has limitations, but if the goal of predictable outcomes in a dynamic environment at scale it's more feasible than creating fine tuned models for every little thing and allows for context-based model performance benchmarking.
- nhorton 6mo ago[dead]
- georaa 6mo ago[flagged]
- _fin6 6mo ago[flagged]