aiqbench analysis
This arXiv preprint studies how to make pretrained Transformers cheaper at long-context inference by replacing most of their attention memory with a fixed-size recurrent state. Its main finding is that long-context retrieval is concentrated in a few layers, which lets the method place test-time-training memory only there and share one recurrent state across layers. The reported gains (2.3-3.1x recall, up to 4.3x lower latency) come from LLaMA-3.1-8B and Mistral-7B and have not been independently reproduced, so readers should weigh them as preliminary.
Anchor: Augmented Cross-Layer Recurrent State for Long-Context Retrieval in Test-Time Training
arXiv:2611.04327v1 Announce Type: new Abstract: Parameter-efficient linearization turns a pretrained autoregressive Transformer into an efficient model by hybridizing sliding-window attention (SWA) with a constant-size recurrent memory. But does every layer's recurrent state contribute to long-context retrieval? Investigating an SWA and Test-Time Training (TTT) hybrid, we find that retrieval is sparse across depth, concentrated in a few anchor layers, and that the remaining states hold similar information expressed through layer-specific transformations. We exploit both observations in Anchor: TTT memory placed only at the anchor layers, and a cross-layer recurrent state written once and read by many layers, each through its own learned read map that realizes a layer-specific stretch and rotation, along with demonstrated generalization to other hybrid attentions. On Llama-3.1-8B and Mistral-7B, Anchor delivers 2.3-3.1x higher recall and 6.7-8.9 points higher MMLU accuracy than LoLCATs with a 10x smaller recurrent state, retains 91-95% of the teacher's MMLU accuracy, and cuts end-to-end latency by up to 4.3x relative to softmax attention.
Abstract:Parameter-efficient linearization turns a pretrained autoregressive Transformer into an efficient model by hybridizing sliding-window attention (SWA) with a constant-size recurrent memory. But does every layer's recurrent state contribute to long-context retrieval? Investigating an SWA and Test-Time Training (TTT) hybrid, we find that retrieval is sparse across depth, concentrated in a few anchor layers, and that the remaining states hold similar information expressed through layer-specific transformations. We exploit both observations in Anchor: TTT memory placed only at the anchor layers, and a cross-layer recurrent state written once and read by many layers, each through its own learned read map that realizes a layer-specific stretch and rotation, along with demonstrated generalization to other hybrid attentions. On Llama-3.1-8B and Mistral-7B, Anchor delivers 2.3-3.1x higher recall and 6.7-8.9 points higher MMLU accuracy than LoLCATs with a 10x smaller recurrent state, retains 91-95% of the teacher's MMLU accuracy, and cuts end-to-end latency by up to 4.3x relative to softmax attention.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2611.04327 [cs.LG]
(or arXiv:2611.04327v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.04327 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 252 words