Feature Resemblance: Towards a Theoretical Understanding of Analogical Reasoning in Transformers
arXiv:2603.05143v3 Announce Type: replace Abstract: Understanding reasoning in large language models is complicated by evaluations that conflate multiple reasoning types. We isolate analogical reasoning, where a model transfers an attribute between entities that share known properties, and study when such transfer can emerge from training. To make the problem analytically tractable, we study a minimal transformer-style abstraction that isolates how learned representations support analogical reasoning. Within this setting, we prove three key results. First, joint training on similarity and attr
This research provides a theoretical advancement in understanding a core cognitive function in AI, emerging amidst the rapid scaling and deployment of large language models.
A deeper theoretical understanding of analogical reasoning in transformers is crucial for improving AI capabilities, ensuring reliability, and potentially unlocking more sophisticated AI behaviors.
This research offers a foundational understanding of how specific reasoning capabilities emerge in transformer architectures, moving beyond black-box empiricism.
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- · Large Language Model Developers
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- · Black-box AI development approaches
Improved architectural designs and training methodologies for more robust and capable AI systems.
Accelerated development of AI agents capable of complex problem-solving and knowledge transfer across domains.
Potential for AI systems to autonomously discover novel solutions by drawing analogies from disparate fields.
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