arXiv:2605.30324v1 Announce Type: cross Abstract: We study language generation in the limit under bounded memory. In this task, a learner observes examples from an unknown target language one at a time and must eventually output only new valid examples. Prior work assumes access to the entire history, a strong assumption since realistic algorithms retain limited past information. Classical work in learning theory shows memory constraints dramatically alter learnability; we extend this to language generation. First, we study memoryless generators. Under a mild enumeration restriction, every cou
This paper presents new theoretical work building on principles of learnability in AI, specifically addressing memory constraints in language generation, a core challenge for ongoing AI development.
Understanding the limits of language generation under bounded memory is crucial for developing more efficient, scalable, and robust AI systems, influencing future architectural choices for large language models.
This research extends classical learning theory to language generation, suggesting that memory constraints fundamentally alter design principles and performance expectations for future AI systems.
- · AI researchers focusing on efficient models
- · Developers of resource-constrained AI applications
- · Cloud providers optimizing AI infrastructure
- · Developers relying solely on brute-force memory for LLMs
- · HPC infrastructure providers not adapting to efficiency demands
Research into more memory-efficient language models will accelerate.
New architectural paradigms for AI that prioritize memory optimization could emerge, leading to smaller, more deployable models.
The development of highly efficient AI agents operating on edge devices could be significantly advanced, impacting various sectors.
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Read at arXiv cs.LG