2 citations · 5 across the 15 of their papers we have counts for
17 papers
Do Astronomical Foundation Models Know When They Will Fail?
Vaidehi Bulusu, Mehul Goyal, Md Rabius Sany Apu +2
Astronomical foundation models are increasingly used as reusable representations across surveys, but it is unclear when the same object remains consistently represented across inst…
Gestalt: a meta-foundation model for astronomy
Michael J. Smith, Shashwat Sourav
The Platonic Representation Hypothesis predicts that sufficiently scaled foundation models converge on a shared representation of the world. As each non-converged model gives a noi…
What AstroPT knows about galaxies, and what that can teach us about LLMs
UniverseTBD, :, Kshitij Duraphe +3
Interpretability research increasingly asks when concepts emerge during training and whether linear probes recover real structure, but in language models these claims are hard to v…
Visualizing Graph-to-Answer Mechanism Recovery in Materials-Science Hypothesis Generation
Shashwat Sourav, Subhadeep Pal, Markus J. Buehler +4
AI co-scientists can generate fluent materials-science hypotheses, but fluency does not show that an answer preserves a scientifically meaningful mechanism. We present a graph-to-a…
The Tell-Tale Trace: Detecting Reasoning Failures in LLMs Using Chain-of-Thought Dynamics
Shashwat Sourav, Aishwarya Balwani
Chain-of-thought (CoT) reasoning improves large language model (LLM) performance while also providing an observable interface to the model's reasoning process. Existing approaches…
Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination
Subhadeep Pal, Shashwat Sourav, Tirthankar Ghosal +1
Accelerating materials discovery requires AI systems that can generate scientifically valid hypotheses through multi-step, domain-grounded reasoning. Standard large language models…
Matter to Mechanism: A Benchmark for AI Co-Scientists in Materials and Battery Research
Shashwat Sourav, Tanjin. He, Maria K. Y. Chan +2
AI co-scientists are increasingly used for scientific discovery, but current evaluations still do not test them on a key task: moving from a concrete scientific or technological pr…
The Compressive Knowledge Graph Hypothesis: Which Graph Facts Matter for Scientific Hypothesis Generation?
Shashwat Sourav, Viktoriia Baibakova, Sanjay Das +4
Knowledge graphs (KGs) can provide structured scientific context to language models, but it remains unclear which graph facts actually shape the generated hypotheses. We study KG-g…
f-INE: A Hypothesis Testing Framework for Estimating Influence under Training Randomness
Subhodip Panda, Dhruv Tarsadiya, Shashwat Sourav +2
Influence estimation methods promise to explain and debug machine learning by estimating the impact of individual samples on the final model. Yet, existing methods collapse under t…
The Platonic Universe: Do Foundation Models See the Same Sky?
UniverseTBD, :, Trinidad Borrell +11
We investigate when foundation models converge towards shared representations, and how this convergence depends on model capacity, training regime, and model architecture. We take…
Cosmology-informed Neural Networks to infer dark energy equation-of-state
Anshul Verma, Shashwat Sourav, Pavan K. Aluri +1
We present a framework that combines physics-informed neural networks (PINNs) with Markov Chain Monte Carlo (MCMC) inference to constrain dynamical dark energy models using the Pan…
Covariant Energy Density Functionals for Neutron Star Matter Equation of State Modeling: Cross-Comparison Analysis Using \texttt{CompactObject}
João Cartaxo, Chun Huang, Tuhin Malik +5
This study analyzes and contrasts different phenomenological methods used to model the nuclear equation of state (EOS) for neutron star matter based on covariant energy density fun…
A Survey on Hypothesis Generation for Scientific Discovery in the Era of Large Language Models
Atilla Kaan Alkan, Shashwat Sourav, Maja Jablonska +14
Hypothesis generation is a fundamental step in scientific discovery, yet it is increasingly challenged by information overload and disciplinary fragmentation. Recent advances in La…
CompactObject: An open-source Python package for full-scope neutron star equation of state inference
Chun Huang, Tuhin Malik, João Cartaxo +14
The CompactObject package is an open-source software framework developed to constrain the neutron star equation of state (EOS) through Bayesian statistical inference. It integrates…
Partially Blinded Unlearning: Class Unlearning for Deep Networks a Bayesian Perspective
Subhodip Panda, Shashwat Sourav, Prathosh A. P
In order to adhere to regulatory standards governing individual data privacy and safety, machine learning models must systematically eliminate information derived from specific sub…
Predicting Missing Light Curves of Gamma-Ray Bursts with Bidirectional-LSTM: An Approach for Enhanced Analysis
S. Sourav, A. Shukla, R. Dwivedi +1
Gamma-ray bursts (GRB) are powerful transient events that emit a large output of gamma rays within a few seconds. Studying these short bursts is vital for cosmological research sin…
A Bayesian Learning, Greedy agglomerative clustering approach and evaluation techniques for Author Name Disambiguation Problem
Shashwat Sourav
Author names often suffer from ambiguity owing to the same author appearing under different names and multiple authors possessing similar names. It creates difficulty in associatin…