5 papers
Reward Design for Physical Reasoning in Vision-Language Models
Derek Lilienthal, Manisha Mukherjee, Sameera Horawalavithana
Physical reasoning over visual inputs demands tight integration of visual perception, domain knowledge, and multi-step symbolic inference. Yet even state-of-the-art Vision Language…
Inference-Time Safety For Code LLMs Via Retrieval-Augmented Revision
Manisha Mukherjee, Vincent J. Hellendoorn
Large Language Models (LLMs) are increasingly deployed for code generation in high-stakes software development, yet their limited transparency in security reasoning and brittleness…
SOSecure: Safer Code Generation with RAG and StackOverflow Discussions
Manisha Mukherjee, Vincent J. Hellendoorn
Large Language Models (LLMs) are widely used for automated code generation. Their reliance on infrequently updated pretraining data leaves them unaware of newly discovered vulnerab…
From Documents to Dialogue: Building KG-RAG Enhanced AI Assistants
Manisha Mukherjee, Sungchul Kim, Xiang Chen +3
The Adobe Experience Platform AI Assistant is a conversational tool that enables organizations to interact seamlessly with proprietary enterprise data through a chatbot. However, d…
Skill over Scale: The Case for Medium, Domain-Specific Models for SE
Manisha Mukherjee, Vincent J. Hellendoorn
Recent advancements in AI have sparked a trend in constructing large, generalist language models that handle a multitude of tasks, including many code-related ones. While these mod…