CMAC: Cross-Modal Attribute Consistency as Label-Free Supervision for Multimodal Product Retrieval

Multimodal representation is crucial for e-commerce tasks such as identical prod- uct retrieval. Attribute-enhanced models define the state of the art on this task, yet nothing inside the pipeline checks whether the extracted attributes are faith- ful: our bad-case analysis of 1,918 retrieval queries finds 48.7% of failures in- volve missing, conflicting, or hallucinated entities. To address this, we propose CMAC, which defines attribute faithfulness as a three-view consistency problem. It runs one extractor under independent image-only, text-only, and multimodal input masks, then uses TRIAD, a closed-form reliability score whose conflict penalty is adjusted by multimodal arbitration and whose complementarity term retains view-specific information. CMAC uses TRIAD for consistency-weighted contrastive learning, conflict-driven adversarial supervision, and a dense, policy- dependent GRPO reward—whose pure form needs no retriever, oracle, or extra annotation in the reward loop. This paper evaluates the contrastive and RL stages; the adversarial stage is specified and its calibrated evaluation is deferred. After reinforcement learning, the generator improves on the fixed evaluation sample: at- tribute consistency rises from 0.6265 to 0.6916, parse failures drop from 3.41% to 0.36%, and response length falls from 697 to 339 tokens. Under the strongest pub- lished baseline’s own evaluation protocol, the CMAC encoder consuming identi- cal entity inputs outperforms it by +5.98 Recall@1 (95% paired-bootstrap CI [+4.91, +7.08], ≈5× the protocol’s 2σ resampling-noise floor), following the first faithful external reproduction of that baseline. These results establish three-view consistency as an evidence-grounded source of label-free supervision—first for the retrieval encoder that consumes attributes, then for the generator that produces them.