Adaptive View Harness: Learning to Select Target-Preserving Views for Large Language Models

Different input views have been shown to affect the practical performance of large language models (LLMs). We aim to leverage historical multi-view behavior to identify a more suitable input form for a new query before inference. To this end, we propose Adaptive View Harness (AVH), which selects a target-preserving view for a new query using only pre-call information while preserving single-call inference. AVH first uses Identity (ID) as the reference and defines the expected switching gain of each candidate view in terms of conditional accuracy change, estimating its relative utility from historical multi-view outcomes using calibrated heterogeneous predictors. It then performs instance-level view selection based on the estimated relative utility, while development-level Conservative Activation disables adaptive selection when the available development evidence is insufficient and retains ID instead. We evaluate AVH on BBH and MMLU-Pro with Qwen2.5-7B-Instruct, Hunyuan-A13B-Instruct, Ling-mini-2.0, and GLM-4.5-Air. Across the eight model–benchmark settings, AVH yields non-negative point estimates relative to ID and achieves a maximum accuracy gain of $+3.42$ pp.