Internal Learning Spatial
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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...
Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models
Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to maintain geometric and spatial consistency across video frames. Given the scarcity of large-scale 3D data, we present GeoVR, a novel framework that learns geometric representations using purely 2D video sequences. This approach effectively restructures the semantic latent space within MLLMs to unlock spatial...
The Geography of Algorithmic Judgment: LLM Intermediaries, Place Identity, and Racial Steering in Housing Search
Announce Type: new Abstract: Large language models (LLMs) are rapidly assuming an intermediary role in housing search through the integration of listing platforms within conversational interfaces, mediating access to information, search, and recommendations within urban settings. We expand on prior work on racial steering in LLMs by conducting a behavioral audit of seven open-weight and closed-source LLMs across four U.S. cities, testing location recommendations across three iterative...
Deep learning four decades of human migration
Abstract Human migration is a fundamental driver of global demographic change, shaping population structure, labour markets and social policy across countries1,2,3. Although long-term migration patterns are often linked to economic development4, they can shift rapidly in response to shocks such as conflict, environmental crises and political change5. Despite its importance, migration remains difficult to measure consistently: existing data are sparse, concentrated in high-income settings and...
Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture
arXiv:2602.05175v2 Announce Type: replace Abstract: Deep neural networks demonstrate impressive performance in visual recognition but remain highly vulnerable to imperceptible adversarial attacks. Existing defense strategies such as adversarial training and diffusion-based purification have achieved significant progress but are frequently constrained by high computational cost, information loss, and inference latency.
Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma
Announce Type: new Abstract: Whether attention maps from pathology foundation models capture genuine biology remains unknown, yet this question is critical for clinical trust and regulatory approval. We propose a spatial transcriptomics-based framework for orthogonal, hypothesis-free evaluation of attention and apply it to five pathology foundation models (CONCH v1.5, UNI v2, Virchow2, GigaPath, H-Optimus-1) and a ResNet50 baseline. Using attention-based multiple instance learning, we train...
Tessera AI model offers accessible way to view Earth
Tessera AI model offers accessible way to view Earth Lisa Lock Scientific Editor Andrew Zinin Lead Editor A foundation model trained on Earth observation data from Copernicus Sentinel-1 and Sentinel-2 has been made widely available to researchers, it was announced at a computer industry conference this week in Denver, U.S. Tessera, an advanced artificial intelligence (AI) model, offers high-accuracy datasets that encode what the satellite "sees" of Earth's surface during the course of a...
Octopuses learn mirror-guided navigation to locate prey
Octopuses learn mirror-guided navigation to locate prey Lisa Lock Scientific Editor Robert Egan Associate Editor Octopuses are remarkably intelligent creatures, as was demonstrated by Inky the Octopus's famous escape from the National Aquarium of New Zealand through a drainpipe back to sea in 2016. A new Dartmouth study shows octopuses can use mirrors to find food out of sight, demonstrating spatial cognitive abilities. The results are published in Current Biology.
Octopuses use mirrors to find food they cannot see
Octopuses use mirrors to find food they cannot see Octopuses just joined an exclusive intelligence club by learning to use mirrors to find hidden food. - Date: - June 5, 2026 - Source: - Dartmouth College - Summary: - Octopuses may be even smarter than we thought.
AlloSpatial: Agentic Harness Framework for Spatial Reasoning in Foundation Models
arXiv:2606.08952v1 Announce Type: new Abstract: Multimodal Foundation Models (MFMs) have made substantial progress, yet remain fragile in spatial reasoning over the physical world. A key bottleneck lies in their inability to transform local egocentric observations into a global allocentric spatial representation. To address this, we propose AlloSpatial, an agentic framework for allocentric spatial cognition in foundation models.