2 citations · 2 across the 12 of their papers we have counts for
12 papers
Kernel Forge: An Agent Harness for LLM-based Generation and Optimization of CUDA Kernels
Joshua Brodsky, Dhravid Kumar, Savini Kashmira +4
Machine learning models are increasingly embedded in everyday software, and most of their runtime is spent in a small set of compute kernels such as matrix multiplication, convolut…
Evaluation of Prompt Injection Defenses in Large Language Models
Priyal Deep, Shane Emmons, Amy Fox +4
LLM-powered applications routinely embed secrets in system prompts, yet models can be tricked into revealing them. We built an adaptive attacker that evolves its strategies over hu…
BlazingAML: High-Throughput Anti-Money Laundering (AML) via Multi-Stage Graph Mining
Haojie Ye, Arjun Laxman, Yichao Yuan +2
Money laundering detection faces challenges due to excessive false positives and inadequate adaptation to sophisticated multi-stage schemes that exploit modern financial networks.…
Prompt Less, Smile More: MTP with Semantic Engineering in Lieu of Prompt Engineering
Jayanaka L. Dantanarayana, Savini Kashmira, Thakee Nathees +4
AI-Integrated programming is emerging as a foundational paradigm for building intelligent systems with large language models (LLMs). Recent approaches such as Meaning Typed Program…
GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2
Savini Kashmira, Jayanaka Dantanarayana, Thamirawaran Sathiyalogeswaran +3
This paper presents GraphMend, a compiler technique that automatically fixes FX graph breaks in PyTorch 2 programs. Although PyTorch 2 introduced TorchDynamo and TorchInductor to e…
GraphRunner: A Multi-Stage Framework for Efficient and Accurate Graph-Based Retrieval
Savini Kashmira, Jayanaka L. Dantanarayana, Krisztián Flautner +2
Conventional Retrieval Augmented Generation (RAG) approaches are common in text-based applications. However, they struggle with structured, interconnected datasets like knowledge g…
SLMEval: Entropy-Based Calibration for Human-Aligned Evaluation of Large Language Models
Roland Daynauth, Christopher Clarke, Krisztian Flautner +2
The LLM-as-a-Judge paradigm offers a scalable, reference-free approach for evaluating language models. Although several calibration techniques have been proposed to better align th…
TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG
Savini Kashmira, Jayanaka L. Dantanarayana, Joshua Brodsky +5
Retrieval-Augmented Generation (RAG) is one of the leading and most widely used techniques for enhancing LLM retrieval capabilities, but it still faces significant limitations in c…
Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat
Roland Daynauth, Christopher Clarke, Krisztian Flautner +2
Deciding which large language model (LLM) to use is a complex challenge. Pairwise ranking has emerged as a new method for evaluating human preferences for LLMs. This approach entai…
Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production
Chandra Irugalbandara, Ashish Mahendra, Roland Daynauth +6
Many companies use large language models (LLMs) offered as a service, like OpenAI's GPT-4, to create AI-enabled product experiences. Along with the benefits of ease-of-use and shor…
Label Agnostic Pre-training for Zero-shot Text Classification
Christopher Clarke, Yuzhao Heng, Yiping Kang +3
Conventional approaches to text classification typically assume the existence of a fixed set of predefined labels to which a given text can be classified. However, in real-world ap…
The Jaseci Programming Paradigm and Runtime Stack: Building Scale-out Production Applications Easy and Fast
Jason Mars, Yiping Kang, Roland Daynauth +4
Today's production scale-out applications include many sub-application components, such as storage backends, logging infrastructure and AI models. These components have drastically…