Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks

Data preparation is a central and time-consuming stage in data analysis workflows. Traditionally, commercial tools have relied on graphical user interfaces (GUIs) to simplify data preparation, allowing users to define transformations through visual operators and workflows. Recent advances in large language models (LLMs) raise the possibility of a paradigm shift toward natural language (NL)-driven data preparation, in which users can specify preparation intents in NL directly. However, it remains unclear how far current LLM-based agents are from this paradigm shift in practice. Existing code generation benchmarks do not capture key characteristics of data preparation, including ambiguous user intents, imperfect real-world data, and the need to translate code into interpretable workflows for validation. To bridge this gap, we present PrepBench, a benchmark designed to evaluate NL-driven data preparation along three core capabilities: interactive disambiguation, prep-code generation, and code-to-workflow translation. We crawl data from the Preppin' Data Challenges, and then extend it into a systematically designed benchmark. The benchmark covers diverse domains, and each task involves 3 to 18 data preparation steps. Nearly half of the tasks require over 100 lines of Python code, and the longest solutions approach 300 lines. Our evaluation shows that, despite recent progress, realizing this paradigm shift remains challenging for state-of-the-art LLMs. PrepBench provides a principled benchmark for measuring this gap and helps identify key challenges toward realizing NL-driven data preparation.

LLM coding agents make it tempting to automate empirical research by delegating experiments to them directly, but naive delegation does not scale to large projects: low-rate instruction lapses compound into broken, irreproducible artefacts. To address this problem, we present Glite ARF, an open-source Python framework for running many LLM coding agents in parallel on a research repository without sacrificing reproducibility or auditability. The framework defines a three-role stack: a human researcher chooses which hypotheses to test, coding agents (Claude Code, Codex CLI) implement individual tasks under a fixed structure, and deterministic Python verifier scripts enforce task isolation, immutability of completed work, a corrections overlay, and a materialised project overview. We call this verifier-driven research: the rules of the research process live in code that fails loudly when violated, not in prose that agents are merely asked to follow. Using Glite ARF, we developed our submission to the BEA 2026 vocabulary-difficulty shared task, placing first in the closed track and second in the open track on all three target languages (Spanish, German, Mandarin) and reducing the official baseline RMSE by 29.9% (closed) and 35.9% (open). The campaign comprised 273 tracked tasks (146 experiment runs) across 129 feature sets, run by up to twelve parallel agents orchestrated from a single laptop - with some model training on rented A100s - at approximately $450 in LLM API spend ($498 total third-party cost), and structured per-fold provenance let us catch and strip four target-leaking feature sets, correcting an implausible 0.609 RMSE to 0.802. Across three campaigns in three domains, the framework's structural machinery adds only about 1% of wall-clock time. Framework and a public demo project accompany this paper.

Coding agents are rapidly becoming a major application of agentic LLMs, but serving them efficiently remains challenging. Progress on this challenge requires understanding real workload patterns, yet the data needed for such analysis is largely absent. Existing public traces and benchmarks do not capture real, day-to-day coding-agent usage across multiple agents and model families for serving-system analysis. To help fill this gap, we collect and release a trace of roughly 4,300 coding-agent sessions, containing about 350,000 LLM steps and 430,000 tool calls from our own day-to-day use of Claude Code and Codex. Our analysis shows that coding-agent workloads feature long autonomous loops, long contexts with short outputs, diverse and heavily-tailed tool calls, and high but imperfect prefix cache hit rates. These findings point to concrete opportunities for optimizing serving, including lower-overhead tool calling, append-length-aware prefill, semantic-aware tool-latency prediction, and improved KV-cache management around human-paced gaps. We release the dataset, trace collection pipeline, and analysis code at https://github.com/uw-syfi/TraceLab.git the project website is https://tracelab.cs.washington.edu.