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

• URL: http://arxiv.org/abs/2603.18446v1
• Date: Thu, 19 Mar 2026 03:17:50 GMT
• Title: UT-ACA: Uncertainty-Triggered Adaptive Context Allocation for Long-Context Inference
• Abstract summary: Long-context inference is challenging for large language models due to attention dilution and out-of-distribution degradation.<n>We propose Uncertainty-Triggered Adaptive Context Allocation (UT-ACA), an inference-time framework that dynamically adjusts the context window based on token-wise uncertainty.<n>Experiments show that UT-ACA substantially reduces average context usage while preserving generation quality in long-context settings.
• Score: 41.15520696680647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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