2 citations · 4 across the 8 of their papers we have counts for
8 papers
Runtime-Structured Task Decomposition for Agentic Coding Systems
Shubhi Asthana, Bing Zhang, Chad DeLuca +2
Agentic coding systems increasingly use large language models (LLMs) for software engineering tasks such as debugging, root cause analysis, and code review. However, many existing…
A Systematic Approach for Large Language Models Debugging
Basel Shbita, Anna Lisa Gentile, Bing Zhang +10
Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based reasoning. However, debugging…
How Much Reasoning Do Retrieval-Augmented Models Add beyond LLMs? A Benchmarking Framework for Multi-Hop Inference over Hybrid Knowledge
Junhong Lin, Bing Zhang, Song Wang +4
Large language models (LLMs) continue to struggle with knowledge-intensive questions that require up-to-date information and multi-hop reasoning. Augmenting LLMs with hybrid extern…
STRIDE: A Systematic Framework for Selecting AI Modalities -- Agentic AI, AI Assistants, or LLM Calls
Shubhi Asthana, Bing Zhang, Chad DeLuca +2
The rapid shift from stateless large language models (LLMs) to autonomous, goal-driven agents raises a central question: When is agentic AI truly necessary? While agents enable mul…
OneShield -- the Next Generation of LLM Guardrails
Chad DeLuca, Anna Lisa Gentile, Shubhi Asthana +7
The rise of Large Language Models has created a general excitement about the great potential for a myriad of applications. While LLMs offer many possibilities, questions about safe…
Adaptive PII Mitigation Framework for Large Language Models
Shubhi Asthana, Ruchi Mahindru, Bing Zhang +1
Artificial Intelligence (AI) faces growing challenges from evolving data protection laws and enforcement practices worldwide. Regulations like GDPR and CCPA impose strict complianc…
Deploying Privacy Guardrails for LLMs: A Comparative Analysis of Real-World Applications
Shubhi Asthana, Bing Zhang, Ruchi Mahindru +3
The adoption of Large Language Models (LLMs) has revolutionized AI applications but poses significant challenges in safeguarding user privacy. Ensuring compliance with privacy regu…
Enterprise Benchmarks for Large Language Model Evaluation
Bing Zhang, Mikio Takeuchi, Ryo Kawahara +5
The advancement of large language models (LLMs) has led to a greater challenge of having a rigorous and systematic evaluation of complex tasks performed, especially in enterprise a…