ArrivalBench: Agent-Generated Data Pipelines Are Correct Once and Wrong Under Time
cs.LG, cs.DB
Submitted: 2026-10-01
Updated: 2026-10-01
Code: https://github.com/great-expectations/great_
Sources
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- BEAVER: An Enterprise Benchmark for Text-to-SQL
- Look-Ahead-Freedom as Temporal Non-Interference: A Verifiable Correctness Property for Backtesting and Agentic Trading Pipelines
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- CDR-Bench: Evaluating Faithful Execution of Compositional, Order-Sensitive Data Refinement Recipes
- ELT-Bench: An End-to-End Benchmark for Evaluating AI Agents on ELT Pipelines
- KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
- The System Prompt Is the Attack Surface: How LLM Agent Configuration Shapes Security and Creates Exploitable Vulnerabilities
- SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization
- Minimal Prompt Perturbations Lead to Code Vulnerabilities: Prompt Fragility and Hidden-State Signals in Coding LLMs
- AgenticDataBench: A Comprehensive Benchmark for Data Agents
- Activating Latent Security Knowledge through LLM-Guided Risk Analysis for Secure Code Generation
- AutoStreamPipe: LLM Assisted Automatic Generation of Data Stream Processing Pipelines
- ELT-Bench-Verified: Benchmark Quality Issues Underestimate AI Agent Capabilities
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