11 citations · 11 across the 6 of their papers we have counts for
6 papers · 1 filter
Grounding Large Language Models in DSGE Simulators for Policy Generation and Forecasting
Aditya Dubey, Namah Gupta, Vinti Agarwal
Large language models can produce economic policy responses that sound reasonable, but this does not show that their actions are consistent with economic dynamics. We test this by…
Repurposing Unified Topological Signatures for Graph Representation Learning
Sanyam Sanjay Jain, Anshika Krishnatray, Aditya Sharma +1
Message-passing Graph Neural Networks (GNNs) iteratively propagate and aggregate local neighborhood information followed by global readout to learn graph representations. However,…
When Prompts Ignore Structure: Graph-Based Attribute Reasoning for Calibrated VLMs
Tanay Sodha, Aditya Sharma, Ramya Hebbalaguppe +2
Reliable confidence estimation remains a key limitation of test-time adaptation in vision-language models (VLMs), where prompt tuning improves zero-shot accuracy but often degrades…
What Do They See? Interpreting Complex Road Scenarios Through the Eyes of Vision-Language-Action Models for Safe and Trustworthy Autonomous Vehicle Learning
Kalpana Panda, Wesley Maia, Vinti Agarwal +1
End-to-end autonomous driving models are now able to navigate complex road scenarios, mapping raw sensor observations directly to observed paths for open-loop evaluation and often…
ModTGCN: Modularity-aware Graph Neural Networks for Text Classification
Rajarshi Misra, Aditya Sharma, Vinti Agarwal +1
Graph-based text classification models typically rely on local neighborhood aggregation and overlook global community structure, despite semantic document graphs exhibiting strong…
G-Loss: Graph-Guided Fine-Tuning of Language Models
Aditya Sharma, Vinti Agarwal, Rajesh Kumar
Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operat…