9 papers
Automated Prefetching for Object Spatial Programming Using Temporal Trace Graphs
Baichuan Li, Jayanaka Dantanarayana, Savini Kashmira +2
Dependent database accesses can serialize application--database round trips on a request's critical path. Planning across traversal steps requires knowing both which relationships…
ContextRender: From Execution Dependencies to Agent Context
Savini Kashmira, Jayanaka L. Dantanarayana, Lingjia Tang +1
LLM agents performing long-horizon tasks accumulate tool results that later steps may need. Passing the full history to every invocation is costly even when it fits within the cont…
SIGIL: Skill Compilation for Reliable and Efficient Agent Execution
Jayanaka Dantanarayana, Savini Kashmira, Lingjia Tang +1
Agent skills describe reusable procedures, but runtime models must still interpret their instructions and coordinate execution. We introduce Skill Compilation, which translates the…
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…
Prompt Less, Smile More: Semantic Engineering in Lieu of Prompt Engineering
Jayanaka L. Dantanarayana, Savini Kashmira, Xiaoyu Ma +5
AI-integrated applications increasingly rely on large language models (LLMs), but their intended behavior is not always fully specified by code. Meaning Typed Programming (MTP) aut…
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…
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…
MTP: A Meaning-Typed Language Abstraction for AI-Integrated Programming
Jayanaka L. Dantanarayana, Yiping Kang, Kugesan Sivasothynathan +6
Software development is shifting from traditional programming to AI-integrated applications that leverage generative AI and large language models (LLMs) during runtime. However, in…