{"ID":3083761,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T09:16:17.280914754Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.06100","arxiv_id":"2606.06100","title":"HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning","abstract":"Vision-Language Models (VLMs) struggle with compositional reasoning that requires understanding inter-object relationships. A natural remedy is to inject explicit scene graph triplets $\\langle s, p, o \\rangle$ from an off-the-shelf scene graph generator (SGG), but we show this backfires: discrete text labels collide with the continuous visual modality, degrading GQA accuracy from 60.38\\% to 58.86\\%. We propose \\textbf{HyperVis}, which bypasses the SGG semantic bottleneck entirely. From $N$ class-agnostic region proposals, we compute a dense $O(N^2)$ visual relation tensor via spatially-biased cross-attention, project it onto a Lorentz hyperboloid, and enforce hierarchy through spatial physics, namely IoA-driven entailment cones and exterior-angle repulsion. We discover that HyperVis contributes in two complementary ways: (1) as a \\emph{training-time regularizer}, the hyperbolic relational losses shape LoRA representations that improve generative VQA (GQA 61.03\\% vs.\\ 57.21\\% for LoRA fine-tuning without relational losses, recovering and surpassing the baseline); and (2) as an \\emph{inference-time relational encoder}, hyperbolic prefix tokens boost discriminative compositional scoring (SugarCrepe 79.94\\%, $+$6.25pp over baseline). The learned curvature stabilises at $κ{=}4.0$, an order of magnitude above prior hyperbolic VLMs where $κ$ typically collapses toward zero, indicating that continuous visual features genuinely require the exponential volume of strongly curved space. A controlled Euclidean ablation confirms this decomposition: the relational pipeline regularises LoRA comparably in flat space (GQA 60.81\\%), but the compositionality gain is specifically hyperbolic (SugarCrepe $+$4.58pp over Euclidean), with entailment loss ${\\sim}6{\\times}$ higher in Euclidean training. Codes are available at TBA.","short_abstract":"Vision-Language Models (VLMs) struggle with compositional reasoning that requires understanding inter-object relationships. A natural remedy is to inject explicit scene graph triplets $\\langle s, p, o \\rangle$ from an off-the-shelf scene graph generator (SGG), but we show this backfires: discrete text labels collide wi...","url_abs":"https://arxiv.org/abs/2606.06100","url_pdf":"https://arxiv.org/pdf/2606.06100v1","authors":"[\"Moshiur Farazi\",\"Sameera Ramasinghe\",\"Mahbub Ahmed Turza\",\"Shafin Rahman\"]","published":"2026-06-04T12:40:15Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Language Model\",\"LoRA\"]","has_code":false}