3 papers
cs.DB2026
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…
cs.DB2026
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…
cs.DB2025
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…