Perspective-Conditioned Multimodal Reasoning for E-Commerce Disputes Verdict

The growing volume of E-commerce disputes creates an increasing demand for automated verdict systems. Existing multi-agent methods introduce diverse role prompts and interaction contexts, aiming to elicit complementary reasoning and improve verdict accuracy. However, their effectiveness remains limited by the inherent biases of underlying models. To address this, we propose *PrismJudge*, a Perspective-Conditioned Multimodal Reasoning Framework for E-Commerce Dispute Verdict (EDV). Inspired by diverse human perspectives, *PrismJudge* constructs $K$ perspective branches, each associated with a learnable latent perspective condition. These conditions jointly guide evidence grounding and verdict reasoning to capture diverse responsibility attribution patterns. Specifically, *Perspective-Conditioned Visual Cue Grounding* (PCVCG) exploits perspective-conditioned visual scores to selectively ground informative visual tokens, enabling the discovery of pivotal cues from redundant inputs. Built upon this, *Perspective-Conditioned Verdict Rationale Generation* (PCVRG) injects perspective conditions into the verdict reasoning process through case-adaptive perspective states and perspective-conditioned LoRA, enabling branches to share common verdict knowledge while learning distinct reasoning viewpoints for the final verdicts. Subsequently, *Rationale-Aligned Joint Learning* (RAJL) aligns human rationales with perspective branches and jointly optimizes generation, rationale matching, and diversity objectives to encourage diverse cue interpretation and complementary verdict generation. Extensive experiments show that *PrismJudge* improves accuracy by ${\Delta }_{\mathrm{Acc}}8.58\%$ and reduces the buyer-seller accuracy gap by ${\Delta }_{\mathrm{Gap}}7.24\%$ compared with the strongest baseline, highlighting the effectiveness of perspective-conditioned reasoning for accurate and balanced EDV.