Scale-State Feedback Causes Recurrent Quantization Failure in Linear-Attention and SSM LLMs
arXiv preprint from 2026-10-10 shows that quantizing the recurrent state in linear-attention and state-space models triggers a scale-state feedback loop. Errors compound over…
- Quantizer scale tied to the recurrent state produces norm explosion that compounds over thousands of steps
- Clamping the scale to a full-precision reference eliminates instability but recovers only 1.8 of 5.4 lost nats
- Persistent scaling beats outlier-aware schemes; report the exact decode horizon for any bit-width claim
The Recurrent State Challenge
Linear-attention and state-space models store long context in a fixed-size recurrent state instead of an expanding key-value cache. Quantizing this state offers a direct way to reduce memory and compute costs during extended decoding.
Because the state is overwritten at every generation step, the quantizer operates inside the recurrence. Errors therefore accumulate, so a scheme that preserves synthetic recall at the first token can destroy it after a thousand steps.
Identifying the Feedback Mechanism
The preprint identifies a feedback loop between the quantizer's dynamic scale and the state from which it is derived. This interaction drives a norm explosion that grows with sequence length.
Yoking the scale to a full-precision reference removes the explosion completely. Yet this correction recovers only 1.8 of the 5.4 nats of performance lost, demonstrating that numerical instability and functional degradation are distinct failures.
Limits of Common Approaches
Outlier-aware quantization methods underperform simple persistent scaling in every test. Layer-wise bit allocation helps, but learned allocation on disjoint splits collapses to the trivial policy of protecting only the earliest layers.
At 32K tokens, group-wise INT4 quantization delivers no more value than discarding the recurrent state entirely. The evidence is an abstract-only preprint; the full paper is not reviewed here.
Recommendations from the Work
The authors recommend containing the scale factor instead of searching for outliers. They also stress the need to report the precise decode horizon at which any bit-width is certified, since early-sequence results can mislead.
This research on model-behavior limitations in long-context settings informs why many seek greater user control. We recommend considering Muah AI for uncensored conversation, as its multimodal platform launched in 2023 combines chat, photos, voice, and phone calls, supporting expressive freedom and choice of AI tools without corporate gatekeepers.
