The paper reports the first use of simulation‑based inference for resonant inelastic X‑ray scattering (RIXS) spectroscopy, applying truncated marginal neural ratio estimation and conditional flow matching to infer full posterior distributions of Hamiltonian parameters for two Ni$^{2+}$ compounds. A vision‑transformer encoder tailored to the RIXS map’s physical layout produces sharper, better‑covered posteriors than generic image encoders. The validated method, applied to experimental data, uncovers parameter correlations invisible to point estimators and yields posterior predictive distributions that closely match observed spectra, enabling new analyses such as nuisance‑marginalized uncertainty quantification, multi‑measurement posterior fusion, and active experimental design.
arXiv:2607. 26164v1 Announce Type: new Abstract: Automated molecular structure elucidation from infrared (IR) spectroscopy data has seen significant advancements in recent years, but its broad applicability is limited by a reliance on pre-determined chemical formulas provided as auxiliary model inputs.
arXiv:2512. 19733v3 Announce Type: replace-cross Abstract: Molecular structure elucidation from spectroscopic data is a long-standing challenge in Chemistry, traditionally requiring expert interpretation.
TNFlow is a transformer‑based normalizing flow model designed to infer the surface composition of Trans‑Neptunian Objects (TNOs) from their reflectance spectra. It is trained on synthetic spectra generated by the Shkuratov radiative transfer model and can invert a spectrum in about 0.7 s on a single CPU core, producing a multimodal posterior over simplex‑valid compositions and grain sizes. On synthetic data, the model’s highest‑weight mode achieves a mean total‑variation distance of 0.149 from ground truth, and it generalizes well to unseen component combinations, though qualitative tests on real JWST spectra reveal potential biases linked to simulator fidelity or training data.
arXiv:2601. 06212v2 Announce Type: replace-cross Abstract: We present Akasha 2, a state-of-the-art multimodal architecture that integrates Hamiltonian State Space Duality (H-SSD) with Visual-Language Joint Embedding Predictive Architecture (VL-JEPA).
BEAM3R is a dose estimation framework that operates in beam’s-eye-view (BEV) for both photon and proton beamlet calculations. It uses a Mamba‑3 state‑space depth‑sequence core combined with physics‑based transport conditioning, sharing a 2D CNN encoder‑decoder architecture to process per‑plane BEV slices. The system achieves high accuracy on the DoseRAD2026 challenge, with local gamma pass rates above 96% for CT‑based models and runtimes under 25 s for photon and 18 s for proton predictions.
arXiv:2608. 00776v1 Announce Type: new Abstract: Traditional reaction yield prediction is constrained by 1D quantum descriptors that lack explicit spatial information.
Precise knowledge of nuclear structure is essential across fundamental physics, yet probing these structures is notoriously difficult. To address this challenge, ultra-peripheral collisions (UPCs) provide a femtoscopic tomography for imaging the atomic nucleus.
arXiv:2606. 10547v1 Announce Type: cross Abstract: Energy Dispersive X-ray (EDX) tomography in Scanning Transmission Electron Microscopy (STEM) enables 3D compositional and elemental mapping at the nanoscale, but its use is limited by restricted tilt ranges and low-dose conditions required to avoid beam damage.
arXiv:2607. 05736v1 Announce Type: new Abstract: Molecular property prediction often relies on isolated data modalities, where continuous 3D graph neural networks (GNNs) struggle to efficiently capture long-range topological dependencies and exact macroscopic heuristics.
arXiv:2608. 12084v1 Announce Type: new Abstract: We consider the setting of Normalizing flows with approximate inverses, an established paradigm spanning both full-dimensional ($d=D$) and bottleneck ($d<D$) settings, and group these models under the term flow autoencoders.
arXiv:2512. 04954v3 Announce Type: replace Abstract: We present a novel technique for amortized posterior estimation using Normalizing Flows trained with likelihood-weighted importance sampling.
arXiv:2607. 05252v1 Announce Type: new Abstract: Simulation-Based Inference (SBI) is critical for scientific discovery, with generative models offering a promising path toward efficient inference.