Krishnam Soni

I'm interested in machine learning and world models. From May to September 2026, I was a research intern at the CPS Lab, Montanuniversität Leoben, working on world models for robotics. My main focus was test-time adaptation: correcting a pretrained world model when a robot's dynamics change, without retraining it. The internship led to three papers: one submitted to ICRA 2027 and two accepted at NeurIPS 2026 workshops.

Before that, I worked on medical image segmentation: 3D cardiac MRI at NTU Singapore, and retinal OCT scans at IIT Kharagpur, which led to a paper at IEEE INDICON 2024. I completed my B.Tech in Computer Science (AI & ML) at Manipal University Jaipur in 2026, which included an exchange semester at Montanuniversität Leoben.

Krishnam Soni in silhouette, watching the sunset over Lake Garda between two mountains
Watching the sunset at Lake Garda, Italy

Research interests: Machine learning World models Test-time adaptation Computer vision

News
  • Oct 2026ALRA and PULSE accepted at NeurIPS 2026 workshops.
  • Sep 2026Sandwich-Residuals is on arXiv and submitted to ICRA 2027.
  • Sep 2026Reviewing for the NeurIPS 2026 WM PAI and TTCL workshops.
  • May 2026Started a research internship at the CPS Lab, Montanuniversität Leoben, Austria.
  • Oct 2025Began an exchange semester in Industrial Data Science at Montanuniversität Leoben on a MULgraIN scholarship.
  • May 2025Started a remote research internship at NTU Singapore on cardiac MRI segmentation.
  • Dec 2024OCT segmentation paper accepted at IEEE INDICON 2024.
  • Jun 2024Started a research internship at IIT Kharagpur on OCT scan segmentation.
Selected research

Adapting world models at test time.

All publications →
Sandwich-Residuals architecture diagram

Sandwich-Residuals: Parameter-Efficient Test-time Adaptation of World Models

Krishnam Soni, Aditya Sehgal, Vedant Dave, Elmar Rueckert

Adapts a frozen world model via small predictor residuals, with 97–99% fewer adapted parameters.

Submitted to ICRA 2027
ALRA architecture diagram

ALRA: Action-Latent Residual Adaptation for World Models

Krishnam Soni, Aditya Sehgal, Vedant Dave, Elmar Rueckert

Corrects dynamics shifts with a 210-parameter affine correction to a frozen world model's actions.

NeurIPS 2026 Workshops: PTA, TTCL, CL4FMAgents