arXiv:2605.28304v1 Announce Type: new Abstract: Composing autoregressive models remains a core challenge in understanding how large language models can combine behaviors or skills learned across tasks. We introduce a new and principled composition strategy for autoregressive systems, inspired by composition methods developed for diffusion models. Under a factorized-conditionals assumption, we show that the resulting composition is projective: each component model preserves control over its own designated subspace of the output distribution avoiding interference between models. This property is
This research addresses a fundamental challenge in current large language models, indicating ongoing efforts to improve their capabilities in combining learned behaviors.
A principled method for compositional generalization could significantly enhance the reliability and application breadth of advanced AI models, impacting numerous industries and research directions.
The ability to predictably compose autoregressive models without interference could lead to more modular and robust AI systems capable of complex reasoning and task execution.
- · AI researchers
- · Large language model developers
- · AI-driven product companies
- · Developers relying solely on brute-force scaling
- · AI applications requiring extensive manual fine-tuning for combined tasks
Improved performance and decreased complexity in training large, multi-task AI models.
Faster development cycles for AI agents and more sophisticated AI-powered software.
Acceleration in the development of general-purpose AI, as models become more capable of combining disparate skills seamlessly.
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Read at arXiv cs.LG