[Paper] RMAAT: Astrocyte-Inspired Memory Compression and Replay for Efficient Long-Context Transformers

Source: arXiv - 2601.00426v1

Overview

The paper introduces RMAAT, a Transformer variant that borrows ideas from astrocytes—the brain’s support cells—to tackle the notorious quadratic cost of self‑attention on long sequences. By embedding a lightweight “memory‑compression” loop and a replay‑based training scheme, the authors achieve competitive accuracy on long‑context benchmarks while slashing compute and GPU memory usage.

Key Contributions

• Astrocyte‑inspired memory tokens that persist across segmented inputs, acting as a compressed summary of earlier context.
• Retention factor derived from simulated long‑term plasticity (LTP) to adaptively compress or expand the memory tokens on the fly.
• Linear‑complexity intra‑segment attention modeled after short‑term plasticity (STP), eliminating the quadratic blow‑up inside each chunk.
• Astrocytic Memory Replay Backpropagation (AMRB), a training algorithm that reuses stored memory states to reduce back‑propagation memory footprints.
• Empirical validation on the Long Range Arena (LRA) showing RMAAT matches or exceeds state‑of‑the‑art accuracy with up to ~40 % lower FLOPs and ~30 % less GPU memory.

Methodology

1. Segmented processing – Input sequences are split into fixed‑size chunks. Each chunk is processed by a standard Transformer block, but instead of discarding the hidden states after the chunk, a small set of memory tokens is updated and carried forward.
2. Memory compression via retention factor – After each segment, the memory tokens are passed through a learned gating mechanism that mimics astrocytic LTP: important information is retained (high retention), while redundant bits are compressed (low retention). This keeps the memory size constant regardless of total sequence length.
3. Linear attention inside chunks – Within a segment, attention is approximated with a kernel‑based linear transformer (e.g., Performer‑style) that reflects astrocytic STP dynamics, giving O(N) cost per chunk rather than O(N²).
4. AMRB training – During back‑propagation, the algorithm replays the stored memory states instead of keeping the full computational graph for all previous chunks. This replay mimics biological memory consolidation and dramatically reduces the activation memory needed for long sequences.

The overall pipeline can be visualized as a recurrent loop: Chunk → Linear Attention → Memory Update (compress) → Pass to next Chunk, with AMRB handling gradient flow.

Results & Findings

• Accuracy: RMAAT is on par with or slightly better than existing efficient Transformers across all LRA tasks.
• Compute & Memory: The linear‑attention per‑segment and the compressed memory reduce both FLOPs and peak GPU memory, enabling sequences up to 8 K tokens on a single 16 GB GPU (where vanilla Transformers would OOM).
• Ablation: Removing the retention factor or the AMRB replay leads to a 5‑10 % drop in accuracy and a noticeable rise in memory consumption, confirming the importance of both astrocytic mechanisms.

Practical Implications

• Long‑document processing – RMAAT can be dropped into pipelines for legal contracts, codebases, or scientific papers where context beyond a few thousand tokens matters, without needing multi‑GPU sharding.
• Edge & mobile inference – The constant‑size memory and linear attention make it feasible to run on devices with limited RAM, opening doors for on‑device summarization or transcription.
• Training efficiency – AMRB’s replay strategy reduces the memory overhead of gradient checkpointing, allowing larger batch sizes or longer sequences during pre‑training, which translates to faster convergence and lower cloud costs.
• Neuro‑inspired design – The work demonstrates a concrete way to translate biological plasticity concepts into software primitives, encouraging further exploration of brain‑derived optimizations for AI models.

Limitations & Future Work

• Memory token capacity – The fixed number of memory tokens may become a bottleneck for extremely long or highly heterogeneous documents; scaling this adaptively is left for future research.
• Astrocyte model abstraction – The retention factor and STP approximations are simplified; richer biologically‑grounded dynamics could yield even better compression but would increase implementation complexity.
• Benchmark scope – Evaluation is limited to LRA; testing on real‑world corpora (e.g., OpenWebText, code repositories) and downstream tasks like QA or translation would solidify the claims.
• Hardware acceleration – While the algorithm is linear, existing deep‑learning libraries are still optimized for quadratic attention; dedicated kernels or compiler support could unlock further speedups.

Overall, RMAAT offers a compelling blend of neuroscience inspiration and engineering pragmatism, pointing toward a new class of memory‑efficient Transformers for the era of ever‑longer context windows.

Authors

• Md Zesun Ahmed Mia
• Malyaban Bal
• Abhronil Sengupta

Paper Information

• arXiv ID: 2601.00426v1
• Categories: cs.NE, cs.AI, cs.ET, cs.LG
• Published: January 1, 2026
• PDF: Download PDF