This paper presents an efficiency-aware pipeline for automated fact-checking of real-world image–text claims that treats multimodality as a controllable design variable rather than a property that must be uniformly propagated through every stage of the system. The approach decomposes claims into verification questions, assigns each to text- or image-related types, and applies modality-aware retrieval strategies, while ultimately relying on text-only evidence for verdict prediction and justification generation. Evaluated on the AVerImaTeC dataset within the FEVER-9 shared task, the system achieves competitive question, evidence, verdict, and justification scores and ranks fourth overall, outperforming the official baseline on evidence recall, verdict accuracy, and justification quality despite not using visual evidence during retrieval. These results demonstrate that strong performance on multimodal fact-checking can be achieved by selectively controlling where visual information influences retrieval and reasoning, rather than performing full multimodal fusion at every stage of the pipeline.

Given the limited computational and financial resources of news agencies, real-life usage of fact-checking systems requires fast response times. For this reason, our submission to the FEVER-8 claim verification shared task focuses on optimizing the efficiency of such pipelines built around subtasks such as evidence retrieval and veracity prediction. We propose the Semantic Filtering for Efficient Fact Checking (SFEFC) strategy, which is inspired by the FEVER-8 baseline and designed with the goal of reducing the number of LLM calls and other computationally expensive subroutines. Furthermore, we explore the reuse of cosine similarities initially calculated within a dense retrieval step to retrieve the top 10 most relevant evidence sentence sets. We use these sets for semantic filtering methods based on similarity scores and create filters for particularly hard classification labels “Not Enough Information” and “Conflicting Evidence/Cherrypicking” by identifying thresholds for potentially relevant information and the semantic variance within these sets. Compared to the parallelized FEVER-8 baseline, which takes 33.88 seconds on average to process a claim according to the FEVER-8 shared task leaderboard, our non-parallelized system remains competitive in regard to AVeriTeC retrieval scores while reducing the runtime to 7.01 seconds, achieving the fastest average runtime per claim.