UT-ACA: Uncertainty-Triggered Adaptive Context Allocation for Long-Context Inference
Long-context inference remains challenging for large language models due to attention dilution and out-of-distribution degradation. Context selection mitigates this limitation by attending to a subset of key-value cache entries, yet most methods allocate a fixed context budget throughout decoding despite highly non-uniform token-level contextual demands. To address this issue, we propose Uncertainty-Triggered Adaptive Context Allocation (UT-ACA), an inference-time framework that dynamically adjusts the context window based on token-wise uncertainty. UT-ACA learns an uncertainty detector that combines semantic embeddings with logit-based confidence while accounting for uncertainty accumula...
| Authors | Lang Zhou, Shuxuan Li, Zhuohao Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 March 2026 |
| Last updated at source | 24 August 2026 |
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