HyperAgents: Self-referential self-improving agents

HyperAgents: Self-referential self-improving agents
github.com

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.

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.

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.