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MKA: Memory-Keyed Attention for Efficient Long-Context Reasoning

Dong Liu, Yanxuan Yu, Ben Lengerich, Ying Nian Wu · Mar 21, 2026 · Citations: 0

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Mar 24, 2026, 11:05 AM

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Mar 24, 2026, 11:05 AM

Stale

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Abstract

As long-context language modeling becomes increasingly important, the cost of maintaining and attending to large Key/Value (KV) caches grows rapidly, becoming a major bottleneck in both training and inference. While prior works such as Multi-Query Attention (MQA) and Multi-Latent Attention (MLA) reduce memory by sharing or compressing KV features, they often trade off representation quality or incur runtime overhead. We propose Memory-Keyed Attention (MKA), a hierarchical attention mechanism that integrates multi-level KV caches (local, session, and long-term) and learns to route attention across them dynamically. We further introduce Route-Fused MKA (FastMKA), a broadcast-routed variant that fuses memory sources before attention computation for improved efficiency. Experiments on different sequence lengths show that FastMKA achieves a favorable accuracy-efficiency trade-off: comparable perplexity to MLA while achieving up to 5x faster training throughput and 1.8x lower evaluation latency. These results highlight MKA as a practical and extensible framework for efficient long-context attention.

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Human Feedback Signal

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Field Provenance & Confidence

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Evidence snippet: As long-context language modeling becomes increasingly important, the cost of maintaining and attending to large Key/Value (KV) caches grows rapidly, becoming a major bottleneck in both training and inference.

Evaluation Modes

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Automatic metrics

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Evidence snippet: As long-context language modeling becomes increasingly important, the cost of maintaining and attending to large Key/Value (KV) caches grows rapidly, becoming a major bottleneck in both training and inference.

Quality Controls

provisional

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Confidence: Provisional Source: Persisted extraction inferred

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Evidence snippet: As long-context language modeling becomes increasingly important, the cost of maintaining and attending to large Key/Value (KV) caches grows rapidly, becoming a major bottleneck in both training and inference.

Benchmarks / Datasets

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Evidence snippet: As long-context language modeling becomes increasingly important, the cost of maintaining and attending to large Key/Value (KV) caches grows rapidly, becoming a major bottleneck in both training and inference.

Reported Metrics

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Accuracy

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Evidence snippet: Experiments on different sequence lengths show that FastMKA achieves a favorable accuracy-efficiency trade-off: comparable perplexity to MLA while achieving up to 5x faster training throughput and 1.8x lower evaluation latency.

Rater Population

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Unknown

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Evidence snippet: As long-context language modeling becomes increasingly important, the cost of maintaining and attending to large Key/Value (KV) caches grows rapidly, becoming a major bottleneck in both training and inference.

Human Data Lens

Structured extraction is still processing. Below are provisional signals inferred from abstract text only.

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  • Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Lens

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  • Potential evaluation modes: Automatic metrics
  • Potential metric signals: Accuracy
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Research Brief

Deterministic synthesis

As long-context language modeling becomes increasingly important, the cost of maintaining and attending to large Key/Value (KV) caches grows rapidly, becoming a major bottleneck in both training and inference.

Generated Mar 24, 2026, 11:05 AM · Grounded in abstract + metadata only

Key Takeaways

  • As long-context language modeling becomes increasingly important, the cost of maintaining and attending to large Key/Value (KV) caches grows rapidly, becoming a major bottleneck in both training and inference.
  • While prior works such as Multi-Query Attention (MQA) and Multi-Latent Attention (MLA) reduce memory by sharing or compressing KV features, they often trade off representation quality or incur runtime overhead.
  • We propose Memory-Keyed Attention (MKA), a hierarchical attention mechanism that integrates multi-level KV caches (local, session, and long-term) and learns to route attention across them dynamically.

Researcher Actions

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Caveats

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  • Signals below are heuristic and may miss details reported outside the abstract.

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