ARM: Attention with Routed-Memory for Learnable Sparse Control
Quick summary
arXiv:2609.24417v1 Announce Type: cross Abstract: Despite advances in long-context inference, large language models (LLMs) remain fundamentally limited by the key-value (KV) caching mechanisms that are necessary for stable computation. Techniques such as selective token eviction and pruning have vastly mitigated these issues, but often discard core information to manage the growing cache. In this paper, we propose Attention with Routed Memory (ARM) a novel KV caching structure that introduces a fully differentiable, fixed-size memory system organized as a hierarchical router. Via a Gumbel-Soft
Key takeaways
- arXiv:2609.24417v1 Announce Type: cross Abstract: Despite advances in long-context inference, large language models (LLMs) remain fundamentally limited by the key-value (KV) caching mechanisms that are necessary for stable computation.
- Techniques such as selective token eviction and pruning have vastly mitigated these issues, but often discard core information to manage the growing cache.
- In this paper, we propose Attention with Routed Memory (ARM) a novel KV caching structure that introduces a fully differentiable, fixed-size memory system organized as a hierarchical router.
Why it matters
“ARM: Attention with Routed-Memory for Learnable Sparse Control” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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