11 papers
Double-Take: Improving Capacity, Binding, and Selectivity In Long-Context Within Bidirectional Memory
Vakul Raghavan, Utkarsh Grover, Mingyang Mao +2
Processing a long sequence does not guarantee that distant information remains usable. As context grows, a fixed-size memory must retain many associations, connect information acro…
ORACLE: Optimizer-Relative Alignment for Constrained LEarning
Utkarsh Grover, Wyatt Mackey, Kaixun Hua +2
Constraint handling methods typically intervene before the optimizer acts, by modifying the objective or the gradient. Yet momentum, adaptive scaling, and structured preconditionin…
DePICT: Decision-Preserving Interface for Constrained Downstream Tasks
Utkarsh Grover, Ravi Ranjan, Agoritsa Polyzou +5
A constrained optimization problem may involve a parameter in its objective and active constraints, yet the final decision may remain insensitive to small changes in that parameter…
Listening with Attention: Entropy-Guided Explainability for Transformer-Based Audio Models
Ravi Ranjan, Utkarsh Grover, Xiaomin Lin +1
Transformer-based automatic speech recognition (ASR) models such as Whisper are highly accurate, but their predictions remain difficult to interpret. Existing explainable AI (XAI)…
FAM-Bench: Benchmarking Condition-Aware Food-as-Medicine Reasoning in Vision-Language Models
Mingyang Mao, Bhargav Rishi Medisetti, Utkarsh Grover +4
Food AI can recognize dishes, ingredients, and nutrients. However, these abilities do not show whether a dish is suitable for a person with a specific health condition. They also d…
PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs
Ravi Ranjan, Utkarsh Grover, Xiaomin Lin +1
Large language models (LLMs) can provide automated feedback in educational settings, but aligning an LLMs style with a specific instructors tone while maintaining diagnostic correc…
G-Drift MIA: Membership Inference via Gradient-Induced Feature Drift in LLMs
Ravi Ranjan, Utkarsh Grover, Xiaomin Lin +1
Large language models (LLMs) are trained on massive web-scale corpora, raising growing concerns about privacy and copyright. Membership inference attacks (MIAs) aim to determine wh…
CatRAG: Functor-Guided Structural Debiasing with Retrieval Augmentation for Fair LLMs
Ravi Ranjan, Utkarsh Grover, Mayur Akewar +2
Large Language Models (LLMs) are deployed in high-stakes settings but can show demographic, gender, and geographic biases that undermine fairness and trust. Prior debiasing methods…
Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies
Utkarsh Grover, Ravi Ranjan, Mingyang Mao +9
Deploying foundation models in embodied edge systems is fundamentally a systems problem, not just a problem of model compression. Real-time control must operate within strict size,…
RAZOR: Ratio-Aware Layer Editing for Targeted Unlearning in Vision Transformers and Diffusion Models
Ravi Ranjan, Utkarsh Grover, Xiaomin Lin +1
Transformer based diffusion and vision-language models have achieved remarkable success; yet, efficiently removing undesirable or sensitive information without retraining remains a…
Position: LLMs Must Use Functor-Based and RAG-Driven Bias Mitigation for Fairness
Ravi Ranjan, Utkarsh Grover, Agorista Polyzou
Biases in large language models (LLMs) often manifest as systematic distortions in associations between demographic attributes and professional or social roles, reinforcing harmful…