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2024 / Research engine

Palimpzest

A Declarative System for Optimizing AI Workloads

What it contributes

Turns declarative AI analytics into executable plans that trade off output quality, runtime and model cost.

How to read the evidence

This record describes the 2024 prototype; it is not a specification of every capability in the current project.

We checked the primary publication record and abstract. This profile summarizes author-reported work; it does not claim a full-paper audit, implementation audit, or independent reproduction.

Explore the connections

PipelinesModel selectionCost models
  • SemBaker — Execution plugin
  • NousDB — Semantic relational algebra
  • PLOP — Hybrid query plans