17 citations · 25 across the 7 of their papers we have counts for
7 papers
VectraFlow: Long-Horizon Semantic Processing over Data and Event Streams with LLMs
Shu Chen, Junhan Liu, Deepti Raghavan +1
Monitoring continuous data for meaningful signals increasingly demands long-horizon, stateful reasoning over unstructured streams. However, today's LLM frameworks remain stateless…
Continuous Prompts: LLM-Augmented Pipeline Processing over Unstructured Streams
Shu Chen, Deepti Raghavan, Uğur Çetintemel
Monitoring unstructured streams increasingly requires persistent, semantics-aware computation, yet today's LLM frameworks remain stateless and one-shot, limiting their usefulness f…
When Do Reactive Notebooks Fail to React?
Yuchen Lu, Megan Zheng, Will Crichton +3
Computational notebooks are convenient for programmers, but can easily become confusing and inconsistent due to the ability to incrementally edit a program that is running. Recent…
Making Prompts First-Class Citizens for Adaptive LLM Pipelines
Ugur Cetintemel, Shu Chen, Alexander W. Lee +3
Modern LLM pipelines increasingly resemble complex data-centric applications: they retrieve data, correct errors, call external tools, and coordinate interactions between agents. Y…
Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems
Alexander W. Lee, Justin Chan, Michael Fu +4
AI-augmented data processing systems (DPSs) integrate large language models (LLMs) into query pipelines, allowing powerful semantic operations on structured and unstructured data.…
Alto: Orchestrating Distributed Compound AI Systems with Nested Ancestry
Deepti Raghavan, Keshav Santhanam, Muhammad Shahir Rahman +7
Compound AI applications chain together subcomponents such as generative language models, document retrievers, and embedding models. Applying traditional systems optimizations such…
Model Assertions for Monitoring and Improving ML Models
Daniel Kang, Deepti Raghavan, Peter Bailis +1
ML models are increasingly deployed in settings with real world interactions such as vehicles, but unfortunately, these models can fail in systematic ways. To prevent errors, ML en…