Modern large language models now support context windows of more than one million tokens, which has raised the question of whether retrieval-augmented generation (RAG) is still necessary.
Pure long-context (LC) processing is expensive and is known to under-attend to information placed in the middle of long inputs, while pure RAG is fast but bounded by retrieval quality and prone to errors when retrieved chunks are partially relevant or contradictory.
We propose the Entropy-Driven Adaptive Router (EDAR), a framework that decides at inference time whether to answer a query from retrieved chunks or to escalate it to full long-context processing.
The decision uses the predictive entropy of the token-level probability distribution computed over the first few generated tokens of the RAG response.
The entropy threshold is selected on a held-out validation set by sweeping cost against accuracy.
Experiments
Experiments compare EDAR against pure-RAG and pure-LC baselines on LongBench v2 and Infinity-Bench.
Predictive entropy correlates strongly with hallucination rate on a held-out set of 2,000 generations (Pearson r = 0.85, 95% CI [0.83, 0.87]).
On the long-context benchmarks, EDAR retains 97.4% of the accuracy of the pure long-context baseline while reducing total token expenditure by 70.7%, escalating only 18.2% of incoming queries.
The accuracy gap between EDAR and the pure long-context system is not statistically distinguishable from zero at standard sample sizes.
Conclusions
Predictive entropy is a useful model-internal signal for routing between RAG and long-context inference, and a threshold-based hybrid system can recover most of the accuracy of long-context models at a small fraction of the cost.
The framework does not depend on a specific retriever or LC backbone, and it does not require additional supervision beyond what is normally produced during decoding.