// TOPIC STREAM
Concept-first guides to complex systems.
This article examines how the Structured State Space sequence model (S4) structures state space models to handle long dependencies and reduces them to computable Cauchy kernel operations. It distinguishes only the claims confirmed by the arXiv abstract and metadata.
Hungry Hungry Hippos decomposes the linguistic weaknesses of state space models into token recall and comparison, then explains how H3, FlashConv, and hybrid attention combinations address them.
This article explains why Mamba targets both long-sequence efficiency and language-modeling performance through selective SSMs that change state-space parameters based on the input and hardware-aware parallel algorithms.
xLSTM redesigns LSTM gating and memory structures from the perspective of scaling modern language models. Based on the public abstract and metadata, this article examines the roles of sLSTM and mLSTM, the meaning of parallelization and memory state, and performance conditions that remain unverified.
Mamba-3 seeks to improve both efficiency and state-tracking ability from a state-space perspective through better recurrence, complex-valued state updates, and multi-input/multi-output I/O. This article analyzes the design and evidence boundary, focusing on the 1.5B results stated in the paper's abstract.
Hidden State Poisoning Attacks against Mamba-based Language Models analyzes how a short trigger can overwrite the hidden state of Mamba-family models and break information retrieval. Centered on HiSPA and RoBench-25, this article explains the relationship between efficient state and the security boundary within the scope of the original abstract.
Resona is a framework that retrieves needed information from the input context and reinjects it into a linear recurrent language model. Based on the paper's abstract, this article examines the bottleneck in context copying, the meaning of combining retrieval with recurrence, and the evaluation, cost, and evidence that must be checked before practical adoption.
This article distinguishes fixed-size recurrent hidden states as memory devices versus information compression. It explains the role of state through S4, Mamba, RWKV, and Resona, while treating Qdrant and Statey separately as external-memory contexts.
This article treats recurrent state and agent memory as security boundaries. It connects Mamba, Hidden State Poisoning, trajeckt, and Valmis while separating paper evidence from product and security context to examine the risks of state poisoning, reuse, and data flows.
This article reads the competition between state space models and Transformers as a question of division of labor rather than structural replacement. Based on the abstracts and publicly stated claims of Mamba, Griffin, Jamba, and Mamba-3, it organizes the trade-offs among long contexts, generation, and hardware efficiency.