LLM Digest

Story

arxiv_cs_lg · Nov 6, 2026 · paper

Source brief

One Run Is Not Enough: A Monte Carlo Protocol for Evaluating LLM Reliability on Data Science Tasks

arxiv.orgNov 6, 2026
original source linked

In brief

Large language models (LLMs) have shown promise for automating data science workflows. Benchmarks such as DataSciBench (2026) and StatQA (2024) have advanced the evaluation of their capabilities across data science an...

Continues in

Evaluating Tasks — open the evidence trace →

Feed lens

evaluation

Read the original at arxiv.org →Open in live feed Read that day’s brief

One Run Is Not Enough: A Monte Carlo Protocol for Evaluating LLM Reliability on Data Science Tasks

Earlier in this thread 4 items

τ-bench-LILT: Evaluating Conversational Agents on Realistic Multilingual Tool-Use Tasks

LifeVal: Evaluating AI Model Performance on Personalized Agentic Consumer Tasks

JHiMA-WebRL: A Joint Hierarchical Multi-Agent Reinforcement Learning Framework for Long-Horizon Web Tasks

Is Your Long-Horizon Benchmark Really Deep? Measuring and Synthesizing Depth in Terminal Tasks