publications

2026

1. CoRL

   

   DepthWorld: 3D World Model for Robot Manipulation

   Jai Bardhan, Josef Sivic, and Vladimir Petrik

   In Conference on Robot Learning (CoRL), 2026

   arXiv Code Website
2. ECCV

   

   Persistent Robot World Models: Stabilizing Multi-Step Rollouts via Reinforcement Learning

   Jai Bardhan, Patrik Drozdík, Josef Sivic, and 1 more author

   In European Conference on Computer Vision (ECCV), 2026

   Outstanding Paper Award arXiv Code Website

   Oral and Outstanding Paper Award at the UniWorld Workshop @ ECCV 2026.

3. RA-L

   

   REALM: A Real-to-Sim Validated Benchmark for Generalization in Robotic Manipulation

   Martin Sedlacek, Pavlo Yefanov, Georgy Ponimatkin, and 7 more authors

   IEEE Robotics and Automation Letters (RA-L), 2026

   arXiv
4. WACV

   

   Curve Skeletonization in Continuous domain for Meshes and Point Clouds

   Jai Bardhan, Ramya Hebbalaguppe, and Aravind Udupa

   In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2026

   arXiv Website
5. AAAI

   

   Refine and Align: Confidence Calibration through Multi-Agent Interaction in VQA

   Ayush Pandey, Jai Bardhan, Ishita Jain, and 3 more authors

   In Proceedings of the AAAI Conference on Artificial Intelligence (Special Track on AI Alignment), 2026

2025

1. ICLR-W

   

   HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture

   Jai Bardhan, Radhikesh Agrawal, Abhiram Tilak, and 2 more authors

   In ICLR 2025 Workshops on World Models and on Agentic AI for Scientific Discovery, 2025

   arXiv
2. PRD

   

   Tagging fully hadronic exotic decays of the vectorlike B quark using a graph neural network

   Jai Bardhan, Tanumoy Mandal, Subhadip Mitra, and 2 more authors

   Physical Review D, 2025

   Alphabetical author order
3. AAAI-W

   

   Constructing sensible baselines for Integrated Gradients

   Jai Bardhan, Cyrin Neeraj, Mihir Rawat, and 1 more author

   In AI to Accelerate Science and Engineering (AI2ASE) Workshop @ AAAI 2025, 2025

2024

1. NeurIPS-W

   

   Loss function to optimise signal significance in particle physics

   Jai Bardhan, Cyrin Neeraj, Subhadip Mitra, and 1 more author

   In Machine Learning and the Physical Sciences Workshop @ NeurIPS 2024, 2024
2. CVPR-W

   

   ReMOVE: A Reference-free Metric for Object Erasure

   Aditya Chandrasekar, Goirik Chakrabarty, Jai Bardhan, and 2 more authors

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPR-W), 2024
3. EPJ ST

   

   Unsupervised and lightly supervised learning in particle physics

   Jai Bardhan, Tanumoy Mandal, Subhadip Mitra, and 2 more authors

   The European Physical Journal Special Topics, Jul 2024

   Abs DOI

   We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfolding. Unsupervised methods are ideal for anomaly detection tasks—machine learning models can be trained on background data to identify deviations if we model the background data precisely. The learning can also be partially unsupervised when we can provide some information about the anomalies at the data level. Generative models are useful in speeding up detector simulations—they can mimic the computationally intensive task without large resources. They can also efficiently map detector-level data to parton-level data (i.e., data unfolding). In this review, we focus on interesting ideas and connections and briefly overview the underlying techniques wherever necessary.

4. JCC

   Deep reinforcement learning in chemistry: A review

   Bhuvanesh Sridharan, Animesh Sinha, Jai Bardhan, and 3 more authors

   Journal of Computational Chemistry, Jul 2024

2023

1. PRD

   

   Machine learning-enhanced search for a vectorlike singlet B quark decaying to a singlet scalar or pseudoscalar

   Jai Bardhan, Tanumoy Mandal, Subhadip Mitra, and 1 more author

   Phys. Rev. D, Jun 2023

   DOI arXiv