CART: Context-Anchored Recurrent Transformer -- A Parameter-Efficient Architecture with Learned Stability
arXiv:2606.01495v1 Announce Type: cross Abstract: We present CART (Context-Anchored Recurrent Transformer), a parameter-efficient language model that reuses a single shared core block R times across depth. Unlike prior looped transformers that recompute key-value tensors at every iteration, CART computes K and V once from a multi-layer prelude and has the recurrent core cross-attend to those frozen tensors via multi-head latent attention. A learned Linear Time-Invariant (LTI) gate keeps the recurrence stable: its spectral radius settles in a narrow band (rho in [0.79, 0.83]) across all 36 full
The continuous push for more efficient and scalable AI models drives the exploration of novel architectures like recurrent transformers that address computational costs.
This development proposes a method to significantly reduce parameters and computational overhead in large language models, making advanced AI more accessible and sustainable.
The paradigm of LLM architecture shifts towards more efficient recurrent designs leveraging techniques like multi-layer preludes and learned stability, moving away from purely deep and massive feedforward networks.
- · AI researchers and developers
- · Companies with limited compute budgets
- · Edge AI applications
- · Energy-constrained data centers
- · Manufacturers of excessive AI compute
- · Companies reliant on brute-force scaling
- · Legacy AI model architectures
Reduced training and inference costs for large language models.
Democratization of advanced AI capabilities, potentially leading to more widespread deployment and innovation.
Accelerated AI development in resource-constrained environments, fostering new applications and competitive landscapes.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL