Beyond Aesthetics: LogicBench for Paper-to-Poster Generation

Recent multimodal models have made it increasingly feasible to generate scientific posters directly from research papers. Existing evaluation largely emphasizes visual quality or isolated fact recovery, leaving unclear whether a poster preserves the scientific logic that connects evidence to conclusions. We introduce LogicBench, a benchmark for measuring logical path coverage in generated posters with source-grounded multi-hop questions derived from validated scientific logic graphs. Questions are verified against the source paper and calibrated with reference posters before held-out poster scoring, so the benchmark measures what the poster makes recoverable. On 134 NIPS25 papers with 1,354 questions, human posters achieve 98.3% accuracy, while five poster-generation methods score 24.4–61.8%. Although these methods achieve high scores on PaperQuiz and P2PEval, which emphasize fact-level correctness, their lower LogicBench scores show that factual recovery does not ensure preserved reasoning. Our analysis shows that accuracy reaches only 66.4% even when all facts needed for a question are present on the poster, suggesting that the logical connections between these facts are not preserved. Moreover, among the evaluated methods, aesthetic scores increase over time while LogicBench accuracy decreases. These findings establish logical path coverage as a distinct dimension of poster quality and motivate methods that preserve scientific logic alongside visual quality.