4 papers · 1 filter
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…
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…
Continuous Prompts: LLM-Augmented Pipeline Processing over Unstructured Streams
Shu Chen, Deepti Raghavan, UÄur Ãetintemel
Monitoring unstructured streams increasingly requires persistent, semantics-aware computation, yet today's LLM frameworks remain stateless and one-shot, limiting their usefulness f…
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.…