output
20072026
most citedA Review on Quantum Approximate Optimization Algorithm and its Variants

394 citations

269 papers

cs.LG2026★ 2 cited

LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing

Mantek Singh, Jeshwanth Challagundla, Prateek Karnal +3

This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes. Motivated by the scarcity of label…

cond-mat.mtrl-sci2026

Impact of interstitial carbon on local lattice distortions in CoCrFeMnNi high-entropy alloys

Alevtina Smekhova, Alexei Kuzmin, G. Mohan Muralikrishna +5

Here, we explore component-dependent local lattice distortions in polycrystalline, equiatomic, face-centered cubic CrMnFeCoNi high-entropy alloys and their modifications induced by…

cs.CV2026

A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning

Sushant Dagaji Desale, Rahul Mishra, Ashutosh Kumar Sinha

Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional shifts between the clean sourc…

cs.MA2026

A Reinforcement Learning Inspired Latent Yield Based Adaptive Algorithm Switching Mechanism

Jayprakash S. Nair, Jimson Mathew, Shivashankar B. Nair

Selecting the most suitable algorithm for a given problem instance remains a challenging task, particularly in online or dynamic environments where problem characteristics evolve o…

cond-mat.mtrl-sci2026

Electrochemical Performance of Gold Monolayers for Lithium-Ion Batteries: A First Principles Study

Ajay Kumar, Pritam Samanta, Prakash Parida

Being motivated by recent synthesis of a monolayer of gold, named goldene, from the nano-laminated ternary ceramic phase of Ti3AuC2, we are proposing two phases of goldene viz. gol…

cs.LG2026

Posterior-Calibrated Causal Circuits in Variational Autoencoders: Why Image-Domain Interpretability Fails on Tabular Data

Dip Roy, Rajiv Misra, Sanjay Kumar Singh +1

Although mechanism-based interpretability has generated an abundance of insight for discriminative network analysis, generative models are less understood -- particularly outside o…