The recall failures are structural, not bugs
This is the single most important fact about pure SSMs and it deserves to be said clearly. SSMs with a fixed-size hidden state cannot do perfect recall over arbitrary context. Not won't, can't. The information has been compressed. The 2025 NeurIPS spotlight "Achilles' Heel of Mamba" proved this rigorously for the associative-recall family of tasks: pure SSMs fail in ways that more data, more parameters, and better training cannot fix.
This is why every successful production SSM in 2026 is a hybrid. The empirical answer the field landed on is to keep enough attention layers in the stack to provide "precision recall checkpoints" — usually 1 attention layer per 7–9 SSM layers — and let the SSM layers do the bulk efficiency work. It's not glamorous; it works.
Training stability is narrower than Transformers
Anyone who has trained a Mamba model will tell you: the optimal learning rate window is narrower than for Transformers. A 2025 study found that on recall-style probes, Mamba's accuracy can jump from near-zero to near-optimal within a fairly tight LR range — choose poorly and you waste a training run. Initialization choices matter more. Schedule shape matters more. The grace period for hyperparameter mistakes is shorter.
For research labs this is fine — they have the budget to sweep. For production teams spending real money on each training run, this fragility is a tax. The mature recipes for Mamba (NVIDIA's Nemotron training book, AI21's Jamba writeups) help, but "Mamba is finicky" is still true in a way that "Transformers are finicky" is no longer true.
Ecosystem maturity gap
Hugging Face Transformers supports Mamba and Mamba-2 natively, vLLM has day-1 Bamba support, and Together AI's stack runs StripedHyena. But the depth of optimization isn't comparable to Transformer. Quantization recipes are less battle-tested. Speculative decoding integrations are rougher. The community knowledge — "oh, that's the loss spike at step 50K, here's the fix" — accumulates more slowly. This is changing fast, but in 2026 it's still the single biggest practical reason to default to Transformer when in doubt.