3 papers
cs.CL2026
Graph Fusion Across Languages using Large Language Models
Kaung Myat Kyaw, Khush Agarwal, Jonathan Chan
Combining multiple knowledge graphs (KGs) across linguistic boundaries is a persistent challenge due to semantic heterogeneity and the complexity of graph environments. We propose…
cs.LG2025
Interpretable Hybrid Deep Q-Learning Framework for IoT-Based Food Spoilage Prediction with Synthetic Data Generation and Hardware Validation
Isshaan Singh, Divyansh Chawla, Anshu Garg +5
The need for an intelligent, real-time spoilage prediction system has become critical in modern IoT-driven food supply chains, where perishable goods are highly susceptible to envi…
cs.LG2025
floq: Training Critics via Flow-Matching for Scaling Compute in Value-Based RL
Bhavya Agrawalla, Michal Nauman, Khush Agrawal +1
A hallmark of modern large-scale machine learning techniques is the use of training objectives that provide dense supervision to intermediate computations, such as teacher forcing…