arXiv:2605.09778v2 Announce Type: replace-cross Abstract: Evaluating softmax attention over a fixed long context requires reading every cached key-value pair for each new query token. For a given context (a book, a manual, a legal corpus) the attention output is a deterministic function of the query. We propose Nectar, which fits a compact neural network to this function for queries drawn from a task-relevant distribution. Nectar fits two networks per layer and KV-head: a target network that predicts the attention output and a score network that predicts the log-normalizer. The pair plugs into
The increasing demand for larger context windows in AI models is pushing the limits of current attention mechanisms, necessitating more efficient approaches to manage computational and memory costs for long sequences.
This development offers a novel method to significantly reduce the computational burden of processing long contexts in transformer models, making extremely long context AI applications more feasible and cost-effective.
Traditional softmax attention's quadratic scaling with context length is mitigated by 'Nectar', enabling more efficient and deterministic retrieval of attention outputs for fixed long contexts.
- · AI model developers
- · Cloud computing providers
- · Enterprises with large text corpuses
- · AI-as-a-Service platforms
- · Less efficient attention mechanism designs
Reduced inference costs and latency for large language models operating on long documents.
Enables new applications and capabilities for AI agents requiring deep understanding and summarization of massive text datasets.
Potentially democratizes access to advanced large context AI, reducing the barrier to entry for smaller organizations.
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Read at arXiv cs.CL