We describe the TVKS submission to the MMLow 2026 shared task on multilevel classification of Tamil political memes, which asks systems to predict a stance label (Level 1: Support/Praise vs. Troll/Oppose) and a four-way target label (Level 2) that combines stance with a Person/Party distinction. We factorize the Level 2 label into two binary axes—stance and person/party target—reconstructing Level 2 as their product rather than predicting four classes directly. A flat four-way head drives the rarest class (Troll/Oppose Against Party) to an F1 of 0.000; factorization recovers a non-degenerate prediction over all four classes. We also fix a calibration failure caused by stacking a class-balanced sampler with a class-weighted loss, and combine two multimodal models (MuRIL + ViT and MuRIL + ResNet-50) in an axis-wise ensemble that routes each binary axis to its stronger model. Our system reaches a macro-F1 of 0.8594 on Level 1 and 0.5926 on Level 2 (mean 0.7260), ranking second of seven teams. An error analysis attributes 77% of Level 2 errors to the person/party axis, where architecturally distinct visual encoders converge to the same ceiling—evidence that the residual gap is dataset ambiguity rather than a modelling deficiency.