AI agents that have to survive in changing worlds

My research hypothesis is that continual learning could be a path toward superintelligence: systems that keep acquiring skills, testing ideas, and building on experience. Learning from an evolving stream of interaction could let capabilities compound beyond what a fixed training dataset provides. This is a research direction, not a demonstrated result. See Silver and Sutton’s Welcome to the Era of Experience, a position paper on learning from experience.

One potential bottleneck lies in the train-then-deploy pattern seen in systems from OpenAI and Anthropic. When deployed model weights remain fixed, context, tools, and external memory can still enable useful adaptation, but new experience does not automatically update those weights. A key question is when retrieved experience is enough, and when durable changes to learned capabilities are more effective. Durable system-level learning can also live in memory and tools; weight updates are not the only route. Anthropic’s Effective context engineering for AI agents describes approaches to managing context over long-running tasks.

My concern is a possible ceiling on efficient, cumulative adaptation: repeatedly retrieving experience or running separate training cycles may add cost and delay. Continual learning could reduce that bottleneck by integrating new experience while retaining earlier skills. This is a hypothesis to test, including against strong memory and replay baselines. Forgetting, unreliable feedback, and safe adaptation remain hard problems. For the retention challenge, see Kirkpatrick and colleagues’ Overcoming catastrophic forgetting in neural networks.

There is no demonstrated intelligence ceiling for either company, and their approaches keep evolving. OpenAI has reported gains from reinforcement learning and inference-time compute in Learning to reason with LLMs; Anthropic has described memory and tools for long-running agents. These may complement continual learning. Continual learning alone is not known to be sufficient for superintelligence.

The prototype is implemented; the proposed continual-learning evaluation remains uncompleted. The immediate goal is to test learning and retention honestly before making broader claims. Review the proposed evaluation and research progress.

Changing worlds, interacting agents

The proposal is to create simulations where agents must survive as resources, hazards, and other agents change. Competition, cooperation, resource management, and adaptation could all matter. Worlds with millions of interacting agents are a long-term ambition, not an implemented capability. Start with small, controlled populations and establish what can be learned and measured before increasing scale.

Why synthetic survival environments?

Finite operating power, irreversible lifetime wear, integrity damage, unstable zones and system faults provide artificial pressures to investigate. Agents could face resource allocation, coordination and changing conditions without copying biological needs. These proposed systems are not all implemented, and optimizing operational survival does not by itself establish transferable intelligence. Useful controlled tests matter more than visual realism.

Learn across worlds without losing earlier skills

Train across a sequence of environments with changing conditions, then revisit earlier worlds to test whether prior capabilities remain. Compare continual adaptation with sequential training and replay under matched resource budgets. A longer survival time in the latest world would not, by itself, show that the agent retained what it learned before.

Proposed tests

Keep survival as the central objective; compare survival-only rewards with survival plus auxiliary learning signals. Measure retention of earlier abilities separately from learning new tasks and continued learnability. Evaluate frozen checkpoints against archived opponents so changing competitors are not mistaken for forgetting; compare conditions with memory available and reset. These are proposed tests, not a finalized protocol. Retaining past skills and preserving the ability to learn new ones are separate questions; see Loss of plasticity in deep continual learning.

Test the reward, not just the agent

Survival may encourage useful behavior, but an agent might also learn to hide, avoid exploration, exploit a simulator, or act aggressively when the reward favors it. Compare reward designs and inspect behavior alongside survival, adaptation, retention, and resource costs. Survival alone is not evidence of general intelligence, safe behavior, or a path guaranteed to reach superintelligence.

Prior work motivates both the opportunity and the caution: multiagent autocurricula demonstrate evolving strategies in a particular simulated setting, while Neural MMO 2.0 describes limits of earlier survival objectives and introduces a broader task system. Neither establishes survival as the best reward or validates this project’s hypothesis.

The survival objective is confined to simulated agents inside research environments; it is not a real-world self-preservation objective.

The arena results measure winning in one task; retention across tasks remains untested. A lightweight survival testbed and a local 3D base world are available; broader survival systems and large-scale populations remain future work. Reward design and evaluation still need controlled tests.

The small gridworld diagnostic is a first check of learning and forgetting, not the final research scope.