Latent-Condensed Transformer for Efficient Long Context Modeling

• URL: http://arxiv.org/abs/2604.12452v2
• Date: Thu, 16 Apr 2026 06:26:39 GMT
• Title: Latent-Condensed Transformer for Efficient Long Context Modeling
• Abstract summary: We propose Latent-Condensed Attention (LCA) for large language models.<n>LCA condenses context within MLA's latent space, where representation is disentangled into semantic latent vectors and positional keys.<n>LCA achieves up to 2.5$times$ prefilling speedup and 90% KV cache reduction at 128K context.
• Score: 60.72493959155964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) face significant challenges in processing long contexts due to the linear growth of the key-value (KV) cache and quadratic complexity of self-attention. Existing approaches address these bottlenecks separately: Multi-head Latent Attention (MLA) reduces the KV cache by projecting tokens into a low-dimensional latent space, while sparse attention reduces computation. However, sparse methods cannot operate natively on MLA's compressed latent structure, missing opportunities for joint optimization. In this paper, we propose Latent-Condensed Attention (LCA), which directly condenses context within MLA's latent space, where the representation is disentangled into semantic latent vectors and positional keys. LCA separately aggregates semantic vectors via query-aware pooling and preserves positional keys via anchor selection. This approach jointly reduces both computational cost and KV cache without adding parameters. Beyond MLA, LCA's design is architecture-agnostic and readily extends to other attention mechanisms such as GQA. Theoretically, we prove a length-independent error bound. Experiments show LCA achieves up to 2.5$\times$ prefilling speedup and 90% KV cache reduction at 128K context while maintaining competitive performance.

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