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Related Articles from SNS
Mechanistic Diagnostics of Spatial Lexical Bias in Multimodal Large Language Model Spatial Reasoning
Announce Type: new Abstract: Multimodal large language models (MLLMs) remain unreliable on spatial multiple-choice questions, and their failures are often attributed to poorly attended visual information. In this work, we identify a complementary failure mode, spatial lexical bias: adding a spatial relation word to the answer options can attract the model's decision and make the newly added option likely to be selected.
SciCore-Omics: a tri-modal foundation model unifying histology, spatial transcriptomics and language for spatial biology
Histomorphology and spatial transcriptomics capture complementary aspects of tissue biology, but their relationships remain difficult to extract, align, and interpret at scale. Existing foundation models typically connect histology, omics, or language only pairwise, which limits their capacity to jointly infer molecular states, decode spatial tissue organization, and generate biologically grounded explanations. Here, we show SciCore-Omics, the first tri-modal foundation model linking...
Applying Spatial Statistics to Spatial Transcriptomics Reveals Local Association Between M2-like Macrophages and Fibrosis in Diabetic Kidney Disease
Renal fibrosis is the common final pathway of chronic kidney disease (CKD), driven in part by myofibroblast-mediated extracellular matrix deposition. M2 macrophages have been implicated as a source of myofibroblasts through macrophage-to-myofibroblast transition (MMT), yet whether M2 macrophages are pro- or anti-fibrotic remains controversial, and the spatial context in which MAC-M2-fibrosis coupling occurs is unknown. Here, we applied geographically weighted regression (GWR), a spatial...
From Symbolic to Geometric: Enabling Spatial Reasoning in Large Language Models
arXiv:2606.04381v1 Announce Type: new Abstract: Recent large language models (LLMs) often appear to exhibit spatial reasoning ability; however, this capability is largely \emph{symbolic}, arising from pattern matching over spatial language rather than true \emph{geometric} reasoning over space. Because LLMs operate on discrete tokens, they lack native support for continuous spatial representations, explicit geometric computation, and structured spatial operators.
Towards Streaming Synchronized Spatial Audio Generation via Autoregressive Diffusion Transformer
arXiv:2605.30940v1 Announce Type: cross Abstract: Real-time and accurate spatial audio generation is pivotal for delivering an immersive experience. However, existing spatial audio synthesis technologies are often encumbered by a tradeoff between generation quality and high inference latency, as well as difficulty in capturing precise spatial information from multimodal inputs. To address these challenges, we propose SwanSphere, a unified streaming framework for high-fidelity spatial audio...
STITCH: Spatial Transcriptomics Imputation via Flow Matching with Internal Learning
Spatial transcriptomics datasets frequently suffer from spatial gaps and missing regions due to sectioning artifacts, tissue damage, and the high cost of sequencing that limits tissue coverage. We present STITCH, a scalable and robust generative framework for multidimensional virtual spatial transcriptomics reconstruction. STITCH models intrinsic spatial-transcriptomic patterns directly from individual tissue samples, enabling reconstruction without requiring external reference atlases or...
SpatialAct: Probing Spatial Reasoning-to-Action Capabilities of VLM Agents in 3D Scenes
arXiv:2605.31148v1 Announce Type: new Abstract: Humans can effortlessly perceive spatial layouts, form cognitive representations, reason about spatial relations, and translate such reasoning into actions in everyday 3D environments. Although recent vision-language models (VLMs) have shown promising performance on observation-conditioned spatial perception and reasoning tasks, it remains unclear whether they can build coherent spatial understanding, act upon it, and refine their actions...
Probing Spatial Structure in Pretrained Audio Representations
Announce Type: new Abstract: Pretrained spatial audio encoders are increasingly used as general-purpose representations for perceptual tasks, yet their spatial encoding capabilities remain poorly understood. We introduce the Spatial Audio Representation Learning (SARL) benchmark, a controlled framework for evaluating spatial information in pretrained audio models. SARL probes source-level factors (azimuth, elevation, distance, class) and room-level factors (RT60, volume, shape).
SpaMEM: Benchmarking Dynamic Spatial Reasoning via Perception-Memory Integration in Embodied Environments
arXiv:2604.22409v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have advanced static visual--spatial reasoning, yet they often fail to preserve long-horizon spatial coherence in embodied settings where beliefs must be continuously revised from egocentric observations under environmental change. We introduce SpaMEM (Spatial Memory from Action Sequences), a large-scale diagnostic benchmark that isolates the mechanics of spatial belief evolution via...
Estimating spatially adjusted temperature-dependent time-varying reproduction numbers for vector-borne diseases
Estimating the effective reproduction number is crucial for understanding and managing infectious disease outbreaks. For vector-borne diseases like dengue, transmission depends on environmental and spatial conditions: temperature affects the extrinsic incubation period in mosquitoes, altering transmission timing, while spatial proximity can lead to clusters of transmission. We integrated a temperature-dependent (TD) generation time (GT) distribution and a spatial decay function weighting...