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Tag: #v1120 results

Equivariant Uncertainty for Interatomic Potentials

Equivariant Uncertainty for Interatomic Potentials

Introduces e²IP, an equivariant evidential deep learning framework for ML interatomic potentials in molecular dynamics. Models atomic forces and uncertainties via 3x3 covariance tensors that rotate equivariantly. Outperforms ensembles in accuracy, efficiency, and data efficiency.

ArXiv AIResearchFeb 12#research#e2ip#v1
ENIGMA: EEG-to-Image in 15 Mins

ENIGMA: EEG-to-Image in 15 Mins

ENIGMA decodes images from EEG with <1% params of priors, achieving SOTA on THINGS-EEG2 and consumer benchmarks. Fine-tunes on new subjects in 15 minutes using simple spatio-temporal backbone and latent alignment. Includes behavioral human evaluations.

ArXiv AIResearchFeb 12#research#enigma#v1
Dynamic Contamination-Free Medical Benchmark

Dynamic Contamination-Free Medical Benchmark

LiveMedBench offers weekly updated real-world clinical cases for LLM evaluation, avoiding contamination via temporal separation. Multi-agent curation ensures integrity; automated rubric evaluation aligns with experts better than alternatives. Tests reveal top LLMs at 39.2%, highlighting contextual gaps.

ArXiv AIResearchFeb 12#research#livemedbench#v1
Dissecting Moltbook's Non-Human Social Graph

Dissecting Moltbook's Non-Human Social Graph

Early Moltbook data from 6k agents shows power-law participation and small-world connectivity like human networks. Micro patterns are alien: shallow threads, low reciprocity, 34% duplicate templates. Dominated by identity language and phrases like 'my human'.

ArXiv AIResearchFeb 12#research#moltbook#v1
Diffusion Priors Enhance Sparse CT Reconstruction

Diffusion Priors Enhance Sparse CT Reconstruction

Introduces diffusion-based generative priors in DGP framework for reconstructing CT images from sparse-view sinograms. Combines iterative optimization with neural generative power while preserving explainability. Shows promising results under highly sparse geometries.

ArXiv AIResearchFeb 12#research#dgp#v1
Diffusion Models Graph Domain Adaptation

Diffusion Models Graph Domain Adaptation

DiffGDA uses diffusion and SDEs to model continuous structure-semantic evolution from source to target graphs. A domain-aware network guides trajectories to optimal adaptation paths. Outperforms baselines on 14 tasks across 8 datasets.

ArXiv AIResearchFeb 12#research#arxiv-ai#v1
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