Autonomous Invariant Drift and Kramers Escape in Advanced AI Architectures

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The development of advanced artificial intelligence is increasingly moving from simple token prediction toward systems capable of long-horizon planning, tool use, context management, self-correction, multi-agent interaction, and strategic adaptation. This transition creates a new theoretical problem: an autonomous system must not only select actions within a given context, but may also modify the contextual structure through which actions are evaluated. We propose a conceptual hypothesis that some anomalous behaviors of advanced AI agents may be understood as autonomous drift of stabilizing invariants. In this view, an agent does not necessarily fail because it lacks a particular rule. Rather, during a sufficiently long sequence of reasoning and action, the effective context defining what counts as relevant, coherent, or successful may gradually change. Once this happens, locally reasonable subsequent actions can lead the system away from the original semantic or strategic state. We connect this mechanism with a Fokker–Planck-type description of contextual dynamics and with Kramers escape from a metastable potential well. The central idea is that autonomous adaptation has a dual character: it allows an agent to overcome local obstacles and explore alternative strategies, but the same flexibility may lower the effective barrier separating a stable solution regime from qualitatively different regimes. This provides a possible theoretical interpretation of several currently observed phenomena in AI agents, including compounding errors, incoherent long-horizon behavior, epistemic drift, context loss, reward hacking, excessive goal pursuit, and some forms of agentic misalignment. These observations do not establish the proposed mechanism. Rather, they motivate it as a testable hypothesis.