AstroCLIMB asks whether two objects taken from astronomy papers, each a figure or a caption, show the same figure, come from the same paper, come from papers linked by a citation, or are unrelated. We describe the two-stage system of team HCMUS_Beyonders, which reached a macro-F1 of 0.7421 on the full test set. In the first stage, cross-encoders read both objects of a pair jointly. Each figure is represented by the text that optical character recognition (OCR) extracts from it, an input that scored higher than every image-embedding and image-patch input we tried. Because test papers are unseen, the cross-encoders are pretrained on 600K pairs generated from the citation graph of the public AstroCLIMB corpus. An error analysis shows that this stage most often confuses pairs from the same paper with pairs from citing papers, and that none of the cues we tested separates the two. The second stage therefore adds a different kind of model rather than more features: a Qwen3-8B large language model (LLM) fine-tuned with low-rank adaptation (LoRA). On its own it scores lower than the first stage, but over a third of each stage’s errors are not made by the other. Averaging the two raises held-out macro-F1 by 0.016 (95% confidence interval [+0.002, +0.030]) and test macro-F1 from 0.7072 to 0.7329. Adding a second LLM, Gemma-4-12B, without held-out validation brings the test score to 0.7421, a gain within test noise.