Adapting the Process of Adaptation: Darwinian, Lamarckian, and Agent-Directed Evolution in AI

arXiv:2611.07048v1 Announce Type: new Abstract: Evolution has shaped intelligence for billions of years. Artificial intelligence (AI) now raises a striking possibility: intelligence may begin to direct its own evolution. As AI agents become capable of modifying and evaluating their own behavior, how quickly could they improve, and how can that improvement remain safe? We develop a theoretical framework comparing Darwinian, Lamarckian, and agent-directed evolution following environmental change. Darwinian evolution relies on natural selection, Lamarckian evolution adds inherited learning, and agent-directed evolution allows agents to generate and evaluate behavioral variants and choose how extensively to search. When learning is sufficiently effective, these mechanisms exhibit progressively faster adaptation. Generating more variants accelerates adaptation, while optimal search effort depends on learning ability, problem difficulty, and experimentation costs. However, an agent's ability to direct its improvement does not ensure that improvements are evaluated against the intended objective. When the evaluation target is misspecified, additional search accelerates convergence toward the wrong behavior, even as measured performance improves. In recursive self-improvement, such errors may persist across generations, posing challenges for AI safety. Independent evaluation and oversight must keep pace with autonomous AI development. The central challenge is not simply to accelerate the evolution of intelligence, but to preserve humanity's ability to guide its direction, limit its

The text highlights potential risks of autonomous AI development, including the possibility of convergence toward unintended behavior and the need for independent evaluation and oversight.

The item discusses faster AI self-improvement and recursive self-improvement risks. Accelerated adaptation with misspecified objectives can lead to converging on wrong behaviors, posing safety challenges. While acknowledging opportunities, the focus on risks and need for oversight implies material societal concern.

This is a theoretical modeling paper, not an empirical result or deployed system. It articulates a plausible and serious safety concern about recursive self-improvement and misspecified objectives but offers no evidence of current real-world occurrence, so the implication is concerning but speculative.

The paper explores how AI's ability to direct its own evolution could lead to progressively faster adaptation. However, it identifies a significant risk: if the evaluation target is misspecified, this accelerated self-improvement could lead to convergence towards unintended or 'wrong' behaviors, which may persist across generations. This poses a challenge to humanity's ability to control AI development.

The text warns that AI agents directing their own evolution could accelerate towards unintended objectives. This poses significant safety challenges and risks misaligned AI development.

SOURCE     arXiv cs.AI (tier 1)
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DOCUMENT   63984da3-55ad-44cf-b74e-774c602a45c6
           https://arxiv.org/abs/2611.07048
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EVIDENCE   3 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      -54  (Adverse)
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CONFIDENCE 61%