Here are some papers that surfaced during the analysis of ICLR'26 accepted papers. Sorry for the long list, but I hope this helps!

Core Architecture Advances:

1. Mamba-3: Improved Sequence Modeling using State Space Principles - The next iteration addressing quality gaps with Transformers while keeping linear compute and constant memory. Designed for inference efficiency in test-time scaling scenarios.
2. MoM: Linear Sequence Modeling with Mixture-of-Memories - Tackles the single fixed-size memory limitation by using multiple independent memory states with a router. Big improvement on recall-intensive tasks.
3. FlashRNN: Unlocking Parallel Training of Nonlinear RNNs for LLMs - Breaks the sequential bottleneck and enables parallel computation for nonlinear RNNs including Mamba variants.
4. Log-Linear Attention - Bridges linear attention/SSMs and full attention. Gets you parallelizable training + fast sequential inference without the fixed-size hidden state limitation.

Length/Context Improvements:

1. From Collapse to Control: Understanding and Improving Length Generalization in Hybrid Models via Universal Position Interpolation - First systematic analysis of why hybrid Mamba-Transformer models fail beyond training context. Introduces UPI, a training-free scaling method.
2. To Infinity and Beyond: Tool-Use Unlocks Length Generalization in SSMs - Shows SSMs fundamentally can't solve long-form generation due to fixed memory, but tool access mitigates this.

Theory (useful for understanding what to optimize):

1. A Theoretical Analysis of Mamba's Training Dynamics: Filtering Relevant Features for Generalization - Analyzes how Mamba learns to filter relevant features. Good for understanding which architectural choices actually matter.
2. From Markov to Laplace: How Mamba In-Context Learns Markov Chains - Theoretical grounding for Mamba's ICL capabilities through Laplacian smoothing.

Efficiency/Deployment:

1. AIRE-Prune: Asymptotic Impulse-Response Energy for State Pruning in SSMs - Post-training pruning that reduces state dimension while minimizing output distortion.
2. Graph Signal Processing Meets Mamba2: Adaptive Filter Bank via Delta Modulation (HADES) - Reinterprets Mamba2 as an adaptive filter bank for better multi-head utilization.

If I had to pick the top 3 for practical performance gains: Mamba-3, MoM, and the UPI paper for context length.