Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement

arXiv:2609.13406v1 Announce Type: new Abstract: When we speak of recursive self-improvement (RSI), are we speaking of a phenomenon, a mechanism, or a prospect? Towards autonomous and evolving intelligence, RSI is being claimed at many scales, while no single framework that formally describes these emerging instances exists. Its counterpart in the classical realm, iterative policy improvement, is characterized by generalized policy iteration (GPI), a framework of broad applicability with well-understood theoretical properties, but only where the update principle and the evaluation base lie outside the agent. In this paper, we propose Generalized Agent Iteration (GAI), a formal framework that describes iterative policy improvement and RSI as two cases of a single learning paradigm. GAI defines the agent as a configuration of modifiable components within a system and models the learning process as a cycle of agent evaluation and agent improvement. Two pivotal dials then distinguish the instances: whether the improving mechanism is part of the agent and whether the standard it is measured against is grounded outside it. The former dial delineates the boundary between GPI and RSI, and the latter determines a system's polarity as anchored, goal drift, or fully self-referential. Moreover, we use these coordinates to place existing systems on the same two axes and make the defects of recursive self-improvement statable one condition at a time. We see this paper as a first step toward exploring a formal characterization of RSI that rests on the classical account, makes existing syst

Proposes a framework for understanding RSI, but lacks concrete implications for human flourishing.

The paper proposes a formal framework to unify iterative policy improvement and recursive self-improvement, potentially advancing understanding and design of autonomous intelligence. However, it does not claim or demonstrate direct societal effects or transformative impacts. The significance lies in theoretical clarity rather than immediate or guaranteed societal outcomes.

This is a theoretical framework paper, not an empirical result or deployed capability. It offers conceptual tools for analyzing RSI risks like goal drift, which is mildly relevant to AI safety but makes no claim about real-world systems or outcomes yet.

This paper proposes a formal framework, Generalized Agent Iteration (GAI), to understand and analyze recursive self-improvement (RSI). While it aims to provide a basis for understanding future systems, the paper itself is theoretical and does not describe any immediate societal impact. It is a foundational academic contribution without direct implications for human flourishing at this stage.

The paper presents a formal framework for understanding recursive self-improvement. It does not make substantive claims about AI's effect on society.

SOURCE     arXiv cs.AI (tier 1)
   ↓
DOCUMENT   8ee5214e-f60c-4ce3-80d6-1d72c2f56697
           https://arxiv.org/abs/2609.13406
   ↓
EVIDENCE   1 extracted excerpt
   ↓
MODEL RUN  5 runs, methodology 1.0.1
   ↓
SCORE      -17  (Mixed or uncertain)
   ↓
CONFIDENCE 75%