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As-Rigid-As- Possible Deformation of Gaussian Radiance Fields

Canonical reference. 95% of citing Pith papers cite this work as background.

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Vega-Video: Integrating Video into the Grammar of Graphics

cs.HC · 2026-04-27 · unverdicted · novelty 7.0

Vega-Video integrates video into Vega via synchronization, annotation, and transformation classes, using split signals and VOD repurposing for responsive mixed-modality visualizations.

Literate Execution

cs.PL · 2026-04-17 · unverdicted · novelty 7.0

Literate execution treats documentation and visualizations as dynamic, computable parts of program execution via provenance tracking, inverting traditional literate programming to make programs more explorable.

When AI reviews science: Can we trust the referee?

cs.AI · 2026-04-26 · unverdicted · novelty 6.0

AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.

NeuVolEx: Implicit Neural Features for Volume Exploration

cs.GR · 2026-04-13 · unverdicted · novelty 6.0

NeuVolEx extracts robust spatial features from INR training via a structural encoder and multi-task scheme to enable accurate ROI classification with limited supervision and unsupervised viewpoint clustering in volume exploration.

A Spectral Framework for Multi-Scale Nonlinear Dimensionality Reduction

cs.LG · 2026-04-02 · unverdicted · novelty 6.0

A spectral framework for nonlinear DR uses spectral bases plus cross-entropy optimization to create multi-scale embeddings that preserve both global manifold geometry and local neighborhoods while supporting graph-frequency analysis.

Visual Analysis of Multi-outcome Causal Graphs

cs.LG · 2024-07-31 · unverdicted · novelty 6.0

Introduces progressive visualization for comparing causal discovery algorithms and comparative graph layouts for analyzing multi-outcome causal graphs in healthcare.

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  • When AI reviews science: Can we trust the referee? cs.AI · 2026-04-26 · unverdicted · none · ref 104

    AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.