Santa Clara University

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bioRxiv Nov 11, 2026
ProteinMPNN's latent space splits into readable axes for secondary structure, residue volume and heme coordination, and turning a branched-chain axis reshapes an antibody fragment's sequence
Sparse autoencoders expand the dense 128-dimensional latent space at each ProteinMPNN encoder layer into an overcomplete sparse space, where individual dimensions correlate with protein features such as secondary structure, residue volume, residue shape, disulfide bridges and ligand coordination. On the Brolucizumab short-chain variable fragment, the mod=0, 0.5 and 1 candidates with branched-chain-amino-acid dimensions raised held lower RMSD at 433 K than the native sequence, the ProteinMPNN sequence and the GeoEvoBuilder sequence, while mod=2, the largest raise, unfolded substantially at that temperature; the unmodified mod=0 candidate improved too, showing the autoencoder itself shifts predictions.
Sparse autoencoders expand the dense 128-dimensional latent space at each ProteinMPNN encoder layer into an overcomplete sparse space, where individual dimensions correlate with protein features such as secondary structure, residue volume, residue shape, disulfide bridges and ligand coordination. On the Brolucizumab short-chain variable fragment, the mod=0, 0.5 and 1 candidates with branched-chain-amino-acid dimensions raised held lower RMSD at 433 K than the native sequence, the ProteinMPNN sequence and the GeoEvoBuilder sequence, while mod=2, the largest raise, unfolded substantially at that temperature; the unmodified mod=0 candidate improved too, showing the autoencoder itself shifts predictions.
Santa Clara University
Sparse Autoencoder ProteinMPNN Protein Engineering Molecular Dynamics
arXiv Nov 3, 2026
Bottom-up trajectory generators lose the long tail: ScaleTraj picks a movement scale before it picks a destination cell
Individual-level trajectory generators can produce plausible single traces, yet once many individuals are aggregated their displacement distribution often misses the heavy tail of real data. ScaleTraj splits next-location prediction into two steps: a Scale Selection Head decides which spatial scale a move belongs to, and a Scale-Aware Cell Selection Head predicts the destination conditioned on that scale. On GeoLife and Tencent Beijing it lowers both the Scale Fidelity Profile error and the deviations of the fitted truncated power-law parameters relative to the compared model-driven and data-driven baselines.
Individual-level trajectory generators can produce plausible single traces, yet once many individuals are aggregated their displacement distribution often misses the heavy tail of real data. ScaleTraj splits next-location prediction into two steps: a Scale Selection Head decides which spatial scale a move belongs to, and a Scale-Aware Cell Selection Head predicts the destination conditioned on that scale. On GeoLife and Tencent Beijing it lowers both the Scale Fidelity Profile error and the deviations of the fitted truncated power-law parameters relative to the compared model-driven and data-driven baselines.
Santa Clara University Concordia University
Yi Zeng（First author）
ScaleTraj Human Trajectory Generation Scale Fidelity Profile Population-Level Displacement Gap
arXiv Oct 12, 2026
A chest X-ray agent stops calling tools by default: it first asks whether evidence is needed, then blocks unusable measurements
The chest X-ray agent X-Ragent splits "should I seek evidence" from "is this measurement usable": the policy invokes region zoom, anatomical parsing and knowledge lookup only when needed, while quantitative measurements such as the cardiothoracic ratio enter reasoning only through an independent validity gate. Training runs in two stages, agentic supervised fine-tuning on 7,715 structured diagnostic trajectories followed by GRPO with an accuracy-gated reward that applies process terms only to correct trajectories. On the fully held-out ChestAgentBench, accuracy rises from 63.1% for the strongest baseline to 71.7%, with gains across all seven interpretation capabilities.
The chest X-ray agent X-Ragent splits "should I seek evidence" from "is this measurement usable": the policy invokes region zoom, anatomical parsing and knowledge lookup only when needed, while quantitative measurements such as the cardiothoracic ratio enter reasoning only through an independent validity gate. Training runs in two stages, agentic supervised fine-tuning on 7,715 structured diagnostic trajectories followed by GRPO with an accuracy-gated reward that applies process terms only to correct trajectories. On the fully held-out ChestAgentBench, accuracy rises from 63.1% for the strongest baseline to 71.7%, with gains across all seven interpretation capabilities.
Alibaba Group (China)National University of Singapore Beijing Jiaotong University City University of Hong Kong Santa Clara University Carnegie Mellon University
Yi Wang（First author）
X-Ragent Tool-Augmented Reinforcement Learning Rad-CoT GRPO Diagnostic Reasoning

You’re all caught up

ProteinMPNN's latent space splits into readable axes for secondary structure, residue volume and heme coordination, and turning a branched-chain axis reshapes an antibody fragment's sequence

Bottom-up trajectory generators lose the long tail: ScaleTraj picks a movement scale before it picks a destination cell

A chest X-ray agent stops calling tools by default: it first asks whether evidence is needed, then blocks unusable measurements