UT-ACA: Uncertainty-Triggered Adaptive Context Allocation for Long-Context Inference

Abstract: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 accumulation across decoding steps. When insufficient evidence is indicated, UT-ACA selectively rolls back, expands the context window, and regenerates the token with additional support. Experiments show that UT-ACA substantially reduces average context usage while preserving generation quality in long-context settings. Code and dataset are available at this https URL.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2603.18446 [cs.CL]

(or arXiv:2603.18446v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.18446 Focus to learn more arXiv-issued DOI via DataCite

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