Fail Loudly: An Auditable Runtime for Agentic Data Analysis

arXiv:2609.32528 · cs.AI · Submitted 2026-09-26 · Read on arXiv

cs.AI

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/huggingface/smolagents

Terminology

• Benchmark — 22× in this paper · explained in 2 episodes
• Machine Learning — 6× in this paper
• Ablation — 5× in this paper · explained in 1 episode
• Verification — 4× in this paper · explained in 2 episodes
• Conference on Innovative Data Systems Research — 3× in this paper
• Accuracy — 2× in this paper · explained in 1 episode
• Robustness — 2× in this paper · explained in 5 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Analyzer — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• In International Conference on Learning Representations — 1× in this paper
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Workshop on Large Language Model — 1× in this paper

Sources

• AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery
• DABstep: Data Agent Benchmark for Multi-step Reasoning
• iPDB -- Optimizing Semantic SQL Queries
• DataSTORM: Deep Research on Large-Scale Databases using Exploratory Data Analysis and Data Storytelling
• DataCross: A Unified Benchmark and Agent Framework for Cross-Modal Heterogeneous Data Analysis
• Abacus: A Cost-Based Optimizer for Semantic Operator Systems
• LLM-Based Multi-Agent Blackboard System for Information Discovery in Data Science
• QUITE: A Query Rewrite System Beyond Rules with LLM Agents
• AgenticData: An Agentic Data Analytics System for Heterogeneous Data
• DAgent: A Relational Database-Driven Data Analysis Report Generation Agent
• A Survey of Data Agents: Emerging Paradigm or Overstated Hype?

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