LYNX: Analytical Program Synthesis via Grounding and Closed-Loop Refinement
We present LYNX, a multi-agent framework that formulates LLM-driven analytics as a schema- and knowledge-constrained analysis problem rather than a single-pass generation task.
ProofPaper ↗
Key points
- LLM-powered data analytics aims to translate natural language instructions into executable analytical programs over underlying data.
- However, existing approaches, whether treating LLMs as intent-to-code translators or as standalone analytical engines, often fail to reliably align domain semantics with complex schemas and heterogeneous metadata, resulting in brittle execution and semantically inconsistent analytical outputs in real-world workloads.
- LYNX organizes analytical synthesis into a closed-loop workflow of planning, generation, verification, and refinement through three role-specialized agents that collaboratively enforce schema grounding, semantic consistency, and execution correctness.
- To address domain ambiguity and schema misalignment, LYNX integrates Semantic-Structural Distillation and Stage-Aware Allocation (SSD-SAA).
Sources (1)
- [1]LYNX: Analytical Program Synthesis via Grounding and Closed-Loop RefinementarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 10:01 AM
We present LYNX, a multi-agent framework that formulates LLM-driven analytics as a schema- and knowledge-constrained analysis problem rather than a single-pass generation task.
LLM-powered data analytics aims to translate natural language instructions into executable analytical programs over underlying data.
Extractive summary: sentences quoted from the sources.
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