DataSense-Bench is a new benchmark that tests whether advanced AI models can choose and rank training data subsets that will improve a small language model’s performance. The benchmark lets agents inspect data, run analysis code, and perform forward passes, but not train the model or access evaluation tasks. After agents select subsets, the base model is fine‑tuned on each subset and evaluated, measuring both the performance of the best subset and the accuracy of the agents’ rankings. Experiments show limited gains over random selection and inconsistent ranking ability across tasks, with agents often using similar data signals but interpreting their value differently.
By Yudi Zhang, Mingyu Cao, Lu Yin, Mykola Pechenizkiy, Shiwei Liu
MLExpert is a 35B Mixture-of-Experts agentic model designed to autonomously perform long‑horizon machine learning engineering (MLE) tasks. It uses a post‑training method that combines hierarchical agentic supervised fine‑tuning with Windowed Interaction RL to learn from intermediate outcomes in complex experimentation workflows. On the MLE‑Bench Lite benchmark, MLExpert raises the average medal rate from 37.9% to 72.7%, matching the performance of larger frontier models and demonstrating generalizable optimization capabilities across related tasks.
By Shangheng Du, Jinxin Shi, Shuaiyu Zhang, Yiming Wu, Junpeng Zhao, Shiyang Feng, Xin Li, Liang He, Xiangchao Yan, Bo Zhang
The paper investigates whether large language models (LLMs) can anticipate the outcomes of machine‑learning experiments they generate. Using a dataset of 288 LLM‑proposed ideas across eight MLE‑bench tasks, the authors prompt five state‑of‑the‑art LLMs to predict each idea’s impact before training. Results show that while predictions outperform random chance, accuracy is limited—Claude Opus 5.5 explains only about half the variance (R² = 0.49), all models underestimate effect sizes, and performance varies by task, with coding harness choice having little influence.
By Ayush Bansal, Aldo Pareja, Sean Klein, Serge Didenko, Artem Lukoianov
arXiv:2606. 11045v1 Announce Type: new Abstract: Reusing a held-out benchmark adaptively should, in principle, invite overfitting.
By Martin Andres Bertran, Aaron Roth, Zhiwei Steven Wu
arXiv:2607. 02032v1 Announce Type: new Abstract: Evaluating LLM agents on benchmarks like SWE-Bench and GAIA can be expensive, time-consuming, and requires complex infrastructure.
By Yueqi Song, Lintang Sutawika, Jiarui Liu, Lindia Tjuatja, Jiayi Geng, Yunze Xiao, Daniel Lee, Aditya Bharat Soni, Vincent Lo, Xiang Yue, Graham Neubig
arXiv:2609.36679v1 Announce Type: new
Abstract: Machine learning engineering (MLE) agents have made substantial progress, but learning through ML experimentation remains costly in time and computatio...
By Xin Yu, Lizhu Zhang, Jiamu Bai, Yanhong Wu, Zellux Wang, Serena Li, Weiwei Li, Lingzhou Xue, Xiangjun Fan, Bo Peng
EarlyEval introduces a lightweight framework that predicts an LLM agent’s final outcome early in its execution, allowing the run to halt when a LightGBM classifier reaches a calibrated confidence threshold. By training success and failure classifiers on behavioral, textual, and reference-solution features, EarlyEval can cut 13%-26% of agent steps and up to 44.1% of input tokens while maintaining 89%-97% prediction accuracy. Across three benchmarks—SWE-bench Verified, TerminalBench, and Toolathlon—this approach reduces evaluation costs with minimal impact on per-agent resolve rates.
By Yuling Shi, Zhensu Sun, Junsen Dong, Chengcheng Wan, David Lo, Xiaodong Gu
MLExpert is a 35B Mixture-of-Experts agentic model designed to autonomously conduct long‑horizon machine learning engineering (MLE) tasks. It uses a post‑training approach that combines hierarchical supervised fine‑tuning with Windowed Interaction RL to learn from intermediate outcomes in complex experimentation workflows. On the MLE‑Bench Lite benchmark, MLExpert raises the average medal rate from 37.9% to 72.7%, matching the performance of larger frontier models and demonstrating transferable optimization capabilities to other benchmarks such as NatureBench Lite.
arXiv:2608. 13940v1 Announce Type: new Abstract: AI research agents (AIRA) can now propose, implement, and evaluate their own machine learning experiments, but progress on frontier tasks is throttled by cost: a candidate solution can be written in minutes, whereas evaluating it can take hours to days of GPU time.
By Thomas Simon Foster, Bassel Al Omari, Tingchen Fu, Thomas Mann, Carl Domond, Lucia Cipolina-Kun, Bhavul Gauri, Muna Aghamelu, Alexander D. Goldie, Eryk Helenowski, Jean-Christophe Gagnon-Audet, Alberto Pepe, Saba Nazir, Daniel Izcovich, Noam Levi, Rishi Hazra, Karen Hambardzumyan, Nicolas Baldwin, Xian Li, Martin Josifoski, Paris Giampouras, Masoud Jalili Sabet, Anya Sims, Hela Momand, Tatiana Shavrina, Despoina Magka, Jason Weston, Yulin Wang, Anirudh Goyal, Jo\~ao Henriques, Yoram Bachrach, Emily McMilin, Jakob Nicolaus Foerster
The paper introduces CoPE, a method that trains language agents by adding multi‑step action targets to the policy backbone. By supervising each action forecasted from preceding histories, CoPE enables acting and forecasting to co‑evolve without extra inference cost. Across five benchmarks, CoPE outperforms existing RL baselines, achieving up to twice the sample efficiency on ScienceWorld and improving overall performance on realistic applications.
By Haofei Yu, Yixing Chen, Yining Zhao, Xuyan Ye, Yuxiang Liu, Zhigang Hua, Yan Xie, Shuang Yang, Jiaxuan You
arXiv:2608. 05797v1 Announce Type: new Abstract: Task difficulty dictates an agent's likelihood of success, and estimating it without rollouts means forecasting this directly from a task description before executing costly simulations in stateful environments.
By Stefan Krsteski, Charlotte Meyer
arXiv:2610.18760v1 Announce Type: new
Abstract: Test-time scaling for large language models (LLMs) can involve generating multiple candidate solutions for selection or revising an earlier attempt. To...
By Zheye Deng, Sha Li, Changlong Yu, Xin Liu, Qin Lu, Priyanka Nigam, Yangqiu Song