Explicit point correspondence is attractive for video generators because it directly describes how visible surfaces move, yet it can overwrite the appearance priors learned by a large pretrained model. This work investigates this tension in a 5.01 B‑parameter text‑to‑video diffusion transformer and proposes AcrossVAM2.0 for conflict‑aware adaptation.\ \ AcrossVAM2.0 consists of three components:\
- a confidence‑weighted track‑and‑visibility head that estimates the reliability and visibility of point tracks;\
- a zero‑initialized residual feedback that applies only minimal corrections to the generator output during adaptation;\
- an asymmetric PCGrad that, on shared LoRA parameters, removes only the auxiliary gradient that opposes the primary flow‑matching gradient, thereby preserving the generator exactly at initialization and protecting its main update.\ \ A four‑way ablation was run for 1,000 steps with three independent seeds. The full method lowers the KLT forward‑backward cycle error in all seeds compared to a flow‑only continuation (mean seed‑level difference ‑0.00711, SD 0.00505) while keeping sharpness positive. An expanded evaluation on 24 prompts (total 216 videos) yields a 9.7% reduction in aggregate CoTracker3 cycle error and near‑matched performance on six VBench dimensions; however, I3D‑FVD regresses from 0.1824 to 0.2022, indicating that quality improvement is not uniform.\ \ The bounded claim is that conflict‑aware adaptation improves correspondence consistency without broad perceptual degradation, but it does not uniformly enhance video quality. We release equations, pseudocode, matched outputs, source tables, and artifact provenance.\ \ Review