4 citations · 4 across the 8 of their papers we have counts for
9 papers
Recursive Self-Improvement on a Budget: Scoring Code Edits Before They Run
Erfan Darzi, Sree Bhargavi Balija, Sheng He
A coding agent that rewrites its own scaffold has to choose which proposed edit to expand before that edit has been evaluated, and the search then has to return one agent. Each run…
Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability
Alicia Parrish, Rajat Shinde, Sanket Badhe +57
Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional laws, socio-linguistic nuances,…
Predictable LLM Serving on GPU Clusters
Erfan Darzi, Shreeanant Bharadwaj, Sree Bhargavi Balija
Latency-sensitive inference on shared A100 clusters often suffers noisy-neighbor interference on the PCIe fabric, inflating tail latency and SLO violations. We present a fabric-agn…
Fortifying the Agentic Web: A Unified Zero-Trust Architecture Against Logic-layer Threats
Ken Huang, Yasir Mehmood, Hammad Atta +3
This paper presents a Unified Security Architecture that fortifies the Agentic Web through a Zero-Trust IAM framework. This architecture is built on a foundation of rich, verifiabl…
The Trust Fabric: Decentralized Interoperability and Economic Coordination for the Agentic Web
Sree Bhargavi Balija, Rekha Singal, Ramesh Raskar +4
The fragmentation of AI agent ecosystems has created urgent demands for interoperability, trust, and economic coordination that current protocols -- including MCP (Hou et al., 2025…
Decoding Federated Learning: The FedNAM+ Conformal Revolution
Sree Bhargavi Balija, Amitash Nanda, Debashis Sahoo
Federated learning has significantly advanced distributed training of machine learning models across decentralized data sources. However, existing frameworks often lack comprehensi…
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh, Heather Frase, Adina Williams +99
The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehen…
FedMM-X: A Trustworthy and Interpretable Framework for Federated Multi-Modal Learning in Dynamic Environments
Sree Bhargavi Balija
As artificial intelligence systems increasingly operate in Real-world environments, the integration of multi-modal data sources such as vision, language, and audio presents both un…
CPTQuant - A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models
Amitash Nanda, Sree Bhargavi Balija, Debashis Sahoo
Large language models have transformed the comprehension and generation of natural language tasks, but they come with substantial memory and computational requirements. Quantizatio…