Repeated Shared Access Enables Grokking, but Edit Propagation Depends on an Addressable Memory
arXiv:2606.20737v2 Announce Type: replace Abstract: We study factual edit propagation in a controlled synthetic knowledge-graph QA setting using a 2x2 grid that crosses loop recurrence with shared-memory access: a dense transformer (Dense), a looped transformer (Loop), a dense backbone with shared memory (Dense+Mem), and a looped backbone with shared memory (loop-memory coupling, LMC). The two factors dissociate. For learning, both routes to repeated shared access -- looped recomputation and repeated memory rereading -- cross the out-of-distribution (OOD) grokking barrier that Dense fails, so
This research provides deeper technical insight into how specific architectural choices, like repeated shared access and addressable memory, impact advanced AI learning phenomena like grokking and factual edit propagation.
Understanding the mechanisms behind grokking and memory in transformers is crucial for developing more robust, adaptable, and efficient AI models, directly influencing the capabilities of agentic systems.
The explicit dissociation of learning mechanisms (grokking) from memory propagation offers clearer pathways for designing next-generation AI architectures that can handle complex knowledge updates and out-of-distribution generalization.
- · AI researchers
- · Deep learning framework developers
- · AI model developers
- · Generative AI companies
- · AI models lacking sophisticated memory architectures
- · Stagnant AI research paradigms
Improved understanding of transformer capabilities and limitations in knowledge representation and learning.
Accelerated development of more advanced and generalizable AI agents capable of continuous learning and factual correction.
Enhanced AI applications across various domains, potentially leading to more reliable autonomous systems and intelligent assistants.
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Read at arXiv cs.AI