Researchers have identified a phenomenon termed "world-model collapse" in long-horizon language agents, drawing an analogy to phase transitions like water boiling. This collapse occurs when a small change in parameters, such as state load or horizon length, causes a sudden degradation in the agent's internal world model. Analysis of a deterministic task family revealed a phase diagram with a solved plateau, a transition band, and a collapse floor, indicating that world-state fidelity degrades before action validity. The study suggests that while stronger models may shift the critical boundary, they do not eliminate this qualitative transition, identifying world-model collapse as a significant bottleneck for agents operating over extended horizons. AI
IMPACT Identifies a critical bottleneck in long-horizon AI agents, suggesting current architectures may not scale effectively for complex, extended tasks.
RANK_REASON Academic paper detailing a new phenomenon in AI agent behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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