Explicit Logic Channel for Validation and Enhancement of MLLMs on Zero-Shot Tasks

Frontier Multimodal Large Language Models (MLLMs) ex-hibit remarkable capabilities in Visual-Language Comprehension (VLC)tasks. However, they are often deployed as zero-shot solution to newtasks in a black-box manner. Validating and understanding the behav-ior of these models become important for application to new task. Wepropose an Explicit Logic Channel, in parallel with the black-box modelchannel, to perform explicit logical reasoning for model validation, selec-tion and enhancement. The frontier MLLM, encapsulating latent vision-language knowledge, can be considered as an Implicit Logic Channel. Theproposed Explicit Logic Channel, mimicking human logical reasoning, in-corporates a LLM, a VFM, and logical reasoning with probabilistic infer-ence for factual, counterfactual, and relational reasoning over the explicitvisual evidence. A Consistency Rate (CR) is proposed for cross-channelvalidation and model selection, even without ground-truth annotations.Additionally, cross-channel integration further improves performance inzero-shot tasks over MLLMs, grounded with explicit visual evidence toenhance trustworthiness. Comprehensive experiments conducted for tworepresentative VLC tasks, i.e., MC-VQA and HC-REC, on three chal-lenging benchmarks, with 11 recent open-source MLLMs from 4 frontierfamilies. Our systematic evaluations demonstrate the effectiveness of pro-posed ELC and CR for model validation, selection and improvement onMLLMs with enhanced explainability and trustworthiness.