arXiv:2606.01269v1 Announce Type: new Abstract: Transitive inference is the challenge of inferring that A < C from knowing only adjacent relations (A < B, B < C). It is solved by humans and animals not through logical chaining but via an analogue mental number line, whose signature is the symbolic distance effect: distant comparisons are easier than nearby ones. We ask whether Transformers acquire the same primitive, training small models exclusively on adjacent comparisons from a hidden total order and evaluating generalization to unseen distant pairs. We find that out-of-distribution general
This research provides new insights into how transformer models develop emergent cognitive abilities, specifically regarding abstract relational reasoning, which is a foundational aspect of advanced AI.
Understanding the emergent properties of AI models, particularly those related to human-like reasoning, is critical for developing more capable and reliable AI systems across various applications.
This suggests that advanced cognitive primitives like transitive inference might emerge organically in AI models trained on simpler local relationships, rather than requiring explicit programming.
- · AI researchers
- · Transformer architecture developers
- · Developers of general-purpose AI
- · AI models relying solely on explicit logical programming
- · Companies whose competitive advantage rests on brittle, non-generalizable AI mod
Further research will investigate the mechanisms behind emergent ordinal geometry in transformers and its implications for other cognitive abilities.
This understanding could lead to the development of more efficient training methods for AI that exhibit sophisticated reasoning, reducing computational costs.
AI systems demonstrating robust, emergent human-like reasoning could accelerate progress towards autonomous AI agents, impacting various white-collar workflows.
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Read at arXiv cs.AI