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arXiv · 2610.25877

DeltaTrace: Efficient Signed Attribution for Reasoning Language Models

Chen Liang·Daoping Wu·Donghua Yang·Zhiyu Liang·Ying Zhang·Shuguang Xiong·Hongzhi Wang
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Abstract

We study how words in the context support or oppose a language model's response and present DeltaTrace, a novel attribution framework that explains the effect of input tokens on a fixed model response. DeltaTrace measures how the response's log-probability changes when the context tokens being explained are masked. To distribute this difference among source tokens, it constructs operator-specific local rules for finite changes and composes them backward through the computational graph using effective secant slopes. Each token is assigned a signed attribution as the inner product of its embedding displacement and the corresponding backward-propagated slope. Local conservation makes these contributions sum to the measured score difference and preserves their meaning when aggregated over sentences or passages. DeltaTrace supports both attention and linear-attention models, with auxiliary memory growing linearly with sequence length at fixed model dimensions and chunk size. On the evaluated attention model, DeltaTrace achieves the lowest deletion-faithfulness scores (RISE and MAS) on most retrieval and reasoning tasks and the highest needle-in-a-haystack Recall, exceeding FlashTrace by 5.67-24.33 percentage points. On the evaluated hybrid attention and linear-attention model, DeltaTrace lowers RISE on 9 tasks and MAS on 12 relative to FlashTrace.

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Chen Liang, Daoping Wu, Donghua Yang, Zhiyu Liang, Ying Zhang, Shuguang Xiong, Hongzhi Wang. 2026-10-19. DeltaTrace: Efficient Signed Attribution for Reasoning Language Models. https://arxiv.org/abs/2610.25877

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