arXiv:2604.23862v2 Announce Type: replace Abstract: We investigate whether the Feed-Forward Network (FFN) sublayer in a decoder-only transformer can be replaced by an explicit learned memory graph while preserving the surrounding autoregressive architecture. The proposed Graph Memory Transformer (GMT) keeps causal self-attention intact, but replaces the usual per-token FFN transformation with a memory cell that routes token representations over a learned bank of centroids connected by a learned directed transition matrix. In the base GMT v7 instantiation studied here, each of 16 transformer bl
Ongoing research into more efficient and capable transformer architectures is a continuous process, driven by the limitations of current models in specific tasks and the desire for novel memory mechanisms.
This research explores a fundamental architectural change in transformers, potentially leading to more efficient, scalable, and powerful AI models, impacting a wide array of applications.
The proposed Graph Memory Transformer (GMT) deviates from the standard Feed-Forward Network within a transformer, introducing an explicit learned memory graph to route token representations, which could enhance long-range dependencies and memory capacity.
- · AI researchers
- · NLP applications
- · Generative AI
- · Edge AI developers
- · Traditional FFN-dependent architectures
- · Compute-constrained AI deployments
Improved efficiency and performance in complex sequence-to-sequence tasks.
Reduced training costs and deployment requirements for advanced AI models.
Acceleration of research into novel memory-augmented neural networks and potential paradigm shifts in AI architecture design.
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Read at arXiv cs.LG