arXiv:2605.31497v1 Announce Type: new Abstract: Large language models are able to compose skills in order to perform complex tasks, many of which might not have been seen during training. The details of how exactly this composition occurs remain elusive. In this paper, we study a mechanism for compositional generalization in transformers by considering a simple controlled setting involving variable assignment and modular addition. By partitioning our training data into disjoint sets, we observe that small transformers are able to generalize to previously unseen combinations of variables and nu
The paper was just published, representing a new finding in the ongoing research into large language model capabilities and their foundational mechanisms.
Understanding how LLMs achieve compositional generalization is critical for developing more robust, reliable, and truly intelligent AI systems beyond current associative pattern matching.
This research provides a mechanistic understanding of how large language models can combine skills, offering insights that could lead to more predictable and capable AI architectures.
- · AI researchers
- · AI developers
- · Deep learning platforms
- · AI models lacking compositional generalization
- · Black-box AI development approaches
Improved understanding of transformer mechanisms for compositional tasks.
Development of more robust and generalizable AI models, particularly for complex reasoning and multi-step tasks.
Accelerated progress towards more agentic and human-like AI systems by enhancing their ability to combine learned knowledge effectively.
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Read at arXiv cs.LG