State space models like Mamba promised linear scaling and constant memory. They delivered on efficiency, but researchers kept hitting the same wall: ask Mamba to recall something specific from early in a long context, and performance drops.

Three papers at ICLR 2026 independently attacked this limitation. That convergence tells you how fundamental the problem is.

This podcast breaks down:

- Why Mamba's fixed-size state causes "lossy compression" of context

- How Mixture of Memories (MoM) adds multiple internal memory banks

- How Log-Linear Attention finds a middle ground between SSM and full attention

- Why one paper proves SSMs fundamentally can't solve certain tasks without external tools

The pattern across all three: you can add more state, but you have to pay somewhere. Parameters, mechanism complexity, or system infrastructure. No free lunch.

📄 Papers covered:

- MoM: Linear Sequence Modeling with Mixture-of-Memories

https://arxiv.org/abs/2502.13685

- Log-Linear Attention

https://openreview.net/forum?id=mOJgZWkXKW

- To Infinity and Beyond: Tool-Use Unlocks Length Generalization in SSMs

https://openreview.net/forum?id=sSfep4udCb

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#Mamba #SSM #StateSpaceModels #ICLR2026 #LLM #MachineLearning #AIResearch #Transformers #DeepLearningChapters timestamp0:00 Mamba's secret weakness

0:42 The promise: linear scaling, constant memory

1:14 The catch: forgetting specific details

1:34 Memory bottleneck explained

1:43 Attention = perfect recall filing cabinet

2:10 SSM = single notepad with fixed pages

2:49 The core tradeoff

2:57 Three solutions to fix it

3:00 Solution 1: Mixture of Memories (MoM)

3:51 Solution 2: Log-Linear Attention

4:48 Solution 3: External tool use

5:49 The "no free lunch" pattern

6:41 What wins for longer contexts?

7:04 Subscribe for more research deep dives

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