# ARCHER: Amortized cross-specimen pose estimation for cryo-electron microscopy

arXiv: https://arxiv.org/abs/2608.22029 · HTML: https://mlsanity.com/paper/2608.22029/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 93 references that are in the map.
- First posted on arXiv 22 Aug 2026.

## Builds on (most cited first, 29 of 29)

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- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
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- [Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold](https://mlsanity.com/paper/2106.05965/) (2021)
- [Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs](https://mlsanity.com/paper/1803.08207/) (2018)
- [CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images](https://mlsanity.com/paper/2203.08138/) (2022)
- [Amortized Inference for Heterogeneous Reconstruction in Cryo-EM](https://mlsanity.com/paper/2210.07387/) (2022)
- [Image to Sphere: Learning Equivariant Features for Efficient Pose Prediction](https://mlsanity.com/paper/2302.13926/) (2023)
- [CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM](https://mlsanity.com/paper/2408.05526/) (2024)
- [CryoSPIN: Improving Ab-Initio Cryo-EM Reconstruction with Semi-Amortized Pose Inference](https://mlsanity.com/paper/2406.10455/) (2024)
- [CryoFM: A Flow-based Foundation Model for Cryo-EM Densities](https://mlsanity.com/paper/2410.08631/) (2024)
- [CryoFastAR: Fast Cryo-EM Ab Initio Reconstruction Made Easy](https://mlsanity.com/paper/2506.05864/) (2025)
- [A Fourier-based Approach for Iterative 3D Reconstruction from Cryo-EM Images](https://mlsanity.com/paper/1307.5824/) (2013)
- [Learning to recover orientations from projections in single-particle cryo-EM](https://mlsanity.com/paper/2104.06237/) (2021)
- [CryoNet.Refine: A One-step Diffusion Model for Rapid Refinement of Structural Models with Cryo-EM Density Map Restraints](https://mlsanity.com/paper/2602.22263/) (2026)

## Abstract (arXiv)

Single-particle cryo-electron microscopy (cryo-EM) pose estimation is traditionally solved anew for each dataset, where iterative refinement is done from scratch while the estimator learns to store the molecule in its weights. In this work, we show that pose inference is a generalizable, specimen-agnostic operation when conditioned explicitly on a reference volume. We introduce ARCHER, an amortized contrastive classifier that models the pose posterior over a discrete rotation grid. Trained across a variety of protein structures, it operates zero-shot without retraining per structure. This transferability is grounded in Fourier-space information mechanics, where all specimen dependence is captured by the reference structure's power spectrum and spatial extent. ARCHER achieves a median angular error of 5.0deg on 100 held-out test structures and 2.5deg on experimental particles, matching dedicated estimators within 0.16 AA in 3D reconstruction. Crucially, downstream conformational signal is preserved. The leading conformational coordinate correlates at 0.97 with deposited benchmarks, faithfully reconstructing free-energy basins and mobile domains. These results overall demonstrate that cryo-EM pose estimation can be generalized across different structures.