Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards
Inference-time guided sampling steers state-of-the-art diffusion and flow models without fine-tuning by interpreting the generation process as a controllable trajectory. This provides a simple and flexible way to inject external constraints (e.g., cost functions or pre-trained verifiers) for controlled generation. However, existing methods often fail when composing multiple constraints simultaneously, which leads to deviations from the true data manifold. In this work, we identify root causes of this off-manifold drift and find that the approximation error scales severely with gradient misalignment. Building on these findings, we propose Conflict-Aware Additive Guidance ($g^\text{car}$), ...
| Authors | Xuehui Yu, Fucheng Cai, Meiyi Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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