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Life sciences · Preprint
arXiv · September 9, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint proposes a dual technique combining fine-tuning with KV cache concatenation awareness and selective cache recomputation to improve long-context retrieval-augmented generation. On the RULER benchmark at 124k tokens, the method reports a 9.7-point improvement in RULER score over recomputation-only baselines and 80% reduction in time to first token, but the work lacks peer review, statistical validation, and generalization data across multiple benchmarks.
Preprint. Retrieval-augmented generation systems with very long input contexts (tested up to 124k tokens). Intervention: Fine-tuning model with KV cache concatenation awareness combined with selective KV cache recomputation. Compared with: Baseline that recomputes KV caches only and full attention.
For 124k-token input, method improves RULER score by 9.7 points over baseline that recomputes KV caches only Time to first token (TTFT) reduced by 80% compared with full attention Combined approach of fine-tuning with KV cache concatenation awareness and selective recomputation demonstrated improved accuracy for long-context inputs
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This is an unrefereed arXiv preprint proposing a technical method for improving retrieval-augmented generation systems; it presents experimental results on a benchmark but has not undergone peer review.
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In Retrieval-Augmented Generation (RAG) systems, a large number of retrieved chunks are concatenated to form the input context so that users can receive high-quality responses based on external knowledge. As a result, the input context length increases substantially, leading to a larger prefill workload and, in turn, a longer time to first token (TTFT). While previous works that reuse precomputed key-value (KV) caches effectively reduce TTFT for long-context inputs, it remains unclear whether response quality is preserved when the input context becomes very long. In this paper, we propose a combined approach that (i) fine-tunes the model while taking KV cache concatenation into account and (ii) selectively recomputes a subset of the KV caches. By applying both techniques, we demonstrate improved accuracy for long-context inputs. Experiments on the RULER benchmark show that, for a 124k-token input, our method improves the RULER score by 9.7 point over the baseline that recomputes KV caches only. Moreover, TTFT is reduced by 80% compared with full attention.
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