arXiv:2602.23164v2 Announce Type: replace Abstract: Foundation models must handle multiple generative processes, yet mechanistic interpretability largely studies capabilities in isolation; it remains unclear how a single transformer organizes multiple, potentially conflicting "world models". Previous experiments on Othello playing neural-networks test world-model learning but focus on a single game with a single set of rules. We introduce MetaOthello, a controlled suite of Othello variants with shared syntax but different rules or tokenizations, and train small GPTs on mixed-variant data to st
The increasing complexity and multimodal nature of foundation models necessitates deeper understanding of how they manage diverse 'world models' to improve their robustness and generalization across varied tasks.
Understanding how transformers handle multiple, potentially conflicting 'world models' is critical for developing more capable, reliable, and flexible AI, directly impacting the path to more advanced AI agents.
This research provides a controlled methodology to study the internal organization of different generative processes within a single transformer, moving beyond isolated capabilities.
- · AI researchers
- · Foundation model developers
- · Developers of AI agents
- · AI models with poor generalization
- · Single-task AI systems
Improved mechanistic interpretability of complex AI systems, specifically regarding how models juggle multiple internal representations.
More reliable and adaptable AI agents capable of performing a wider array of tasks under vastly different conditions by integrating multiple 'world models'.
Accelerated development of artificial general intelligence (AGI) as models gain a more sophisticated understanding and adaptation to diverse environments and rule sets.
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