arXiv:2606.31399v1 Announce Type: new Abstract: Water looks unchanged as it warms, then at a critical point it boils. We ask whether long-horizon language agents show an analogous transition in their implicit world models. In some parameter settings, changing state load by a small amount, or adding a single step of horizon, leaves behavior nearly unchanged; near a critical boundary, the same small change causes a sudden world collapse. We study this effect in a deterministic task family with exact per-step gold state. A large grid search over state cardinality, dependency density, horizon, bra
This research is emerging as AI model complexity and autonomous agentic systems are rapidly advancing, requiring better understanding of their stability and failure modes.
Understanding 'world-model collapse' is critical for developing robust, reliable, and trustworthy AI agents, especially for high-stakes applications and long-horizon tasks.
This paper provides a new conceptual framework and empirical evidence for a critical instability point in AI models, shifting the focus towards phase transitions in AI behavior.
- · AI Safety Researchers
- · Developers of robust AI agents
- · Industries deploying autonomous systems
- · Developers of unstable large language models
- · Theories lacking critical instability understanding
Further research will focus on identifying critical boundaries and mitigating 'world-model collapse' in AI systems.
New architectural designs or training methodologies will emerge to enhance the stability and predictability of long-horizon AI agents.
The development of highly reliable autonomous AI systems will accelerate, enabling their widespread deployment in complex, real-world environments.
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Read at arXiv cs.AI