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CAESURA: Language Models as Multi-Modal Query Planners
Summary: Introduces Language-Model-Driven Query Planning: use LMs to translate natural-language queries into executable multi-modal query plans with operators over arbitrary modalities (images, text, video), unlike traditional SQL planners. Presents CAESURA, a GPT-4 prototype demonstrating feasibility on two datasets and proposing techniques to improve LM planning robustness for end-to-end multi-modal query execution.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 505
- Venue
- CIDR
- Year
- 2024
- Pagerank
- 0.00014726927
- Overall Rank
- 997 | 93.08%
- DOI
-
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Incoming Non-self Citations Over Time
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| 1,839 |
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| 3,639 |
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AOP: Automated and Interactive LLM Pipeline Orchestration for Answering Complex Queries |
2025 |
CIDR |
4.668568e-05 |
| 8,351 |
PalimpChat: Declarative and Interactive AI analytics |
2025 |
SIGMOD |
4.5340791e-05 |
| 8,479 |
Can Large Language Models Be Query Optimizer for Relational Databases? |
2026 |
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4.4967983e-05 |
| 8,732 |
Unveiling Challenges for LLMs in Enterprise Data Engineering |
2026 |
VLDB |
4.4520434e-05 |
| 9,153 |
Unify: A System For Unstructured Data Analytics |
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4.380727e-05 |
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4.2901665e-05 |
| 9,971 |
KathDB: Explainable Multimodal Database Management System with Human-AI Collaboration |
2026 |
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4.1905499e-05 |
| 9,989 |
Deep Research is the New Analytics System: Towards Building the Runtime for AI-Driven Analytics |
2026 |
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4.1905499e-05 |
| 9,993 |
BridgeScope: A Universal Toolkit for Bridging Large Language Models and Databases |
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Beyond Relational: Semantic-Aware Multi-Modal Analytics with LLM-Native Query Optimization |
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4.1905499e-05 |
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SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads |
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4.1905499e-05 |
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SemBench: A Benchmark for Semantic Query Processing Engines |
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QUEST: Query Optimization in Unstructured Document Analysis |
2025 |
VLDB |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
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