Conditioning-Aware Adaptive Noising for Factual and Edit-Sensitive Graph-to-Sequence Generation

Graph-to-sequence (G2S) generation is commonly evaluated by similarity to reference text, but high reference similarity does not imply reliable generation, which additionally requires factual grounding in the input graph and edit sensitivity to controlled graph edits. Diffusion language models are well suited to these properties because they iteratively revise the output, while the noise schedule controls how much target information is retained during denoising. However, adaptive noising is typically guided by denoising-side signals rather than by support from the conditioning graph. Across WikiOFGraph, GenWiki, and TekGEN, we find that denoising difficulty is only weakly and inconsistently associated with token-level graph support. We therefore introduce DLM4G, which separates adaptive noising into two roles: a global adaptive schedule uses token-wise denoising difficulty to identify positions that benefit from retaining more signal, while dynamic conditioning modulation uses decoder-to-graph cross-attention and conditioning sensitivity-the change in prediction when graph conditioning is removed, to control how strongly that schedule is applied for the current graph during both training and inference. We also introduce Factual Grounding (FGT) and Edit Sensitivity Rate (ESR) to measure the two reliability properties directly, and validate both against human and semantic assessments. The 63M-parameter DLM4G improves BLEU, chrF++, and METEOR over diffusion baselines on all three benchmarks while remaining competitive with 770M and 7-8B models. Within DLM4G, conditioning-aware modulation over difficulty-only adaptation yields absolute gains of 4-7 points in [email protected], 6-9 points in ESR@2, 5-7 points in relation-edit ESR, and approximately 3 BLEU points across the three benchmarks.