1 citations · 1 across the 7 of their papers we have counts for
9 papers
HELM: A Human-Centered Evaluation Framework for LLM-Powered Recommender Systems
Sushant Mehta
The integration of Large Language Models (LLMs) into recommendation systems has introduced unprecedented capabilities for natural language understanding, explanation generation, an…
The Hierarchy of Agentic Capabilities: Evaluating Frontier Models on Realistic RL Environments
Logan Ritchie, Sushant Mehta, Nick Heiner +2
The advancement of large language model (LLM) based agents has shifted AI evaluation from single-turn response assessment to multi-step task completion in interactive environments.…
Beyond Surface-Level Similarity: Hierarchical Contamination Detection for Synthetic Training Data in Foundation Models
Sushant Mehta
Synthetic data has become essential for training foundation models, yet benchmark contamination threatens evaluation integrity. Although existing detection methods identify token-l…
Beyond Accuracy: A Multi-Dimensional Framework for Evaluating Enterprise Agentic AI Systems
Sushant Mehta
Current agentic AI benchmarks predominantly evaluate task completion accuracy, while overlooking critical enterprise requirements such as cost-efficiency, reliability, and operatio…
When Are Learning Biases Equivalent? A Unifying Framework for Fairness, Robustness, and Distribution Shift
Sushant Mehta
Machine learning systems exhibit diverse failure modes: unfairness toward protected groups, brittleness to spurious correlations, poor performance on minority sub-populations, whic…
Scaling Laws and In-Context Learning: A Unified Theoretical Framework
Sushant Mehta, Ishan Gupta
In-context learning (ICL) enables large language models to adapt to new tasks from demonstrations without parameter updates. Despite extensive empirical studies, a principled under…