AutoDataBench: A Data-centric Testbed for Accelerating Auto Research

arXiv:2609.40097 · cs.CL · Submitted 2026-09-30 · Read on arXiv

cs.CL

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/AutoDataBench/AutoDataBench

Terminology

• Benchmark — 24× in this paper · explained in 2 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Generalization — 10× in this paper · explained in 13 episodes
• Distillation — 5× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Benchmarking — 3× in this paper · explained in 4 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Agentic Framework — 1× in this paper · explained in 6 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Annotation Errors — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Program Synthesis with Large Language Models
• FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI
• LoRA: Low-Rank Adaptation of Large Language Models
• DCA-Bench: A Benchmark for Dataset Curation Agents
• Qwen2.5-Coder Technical Report
• Can Generalist Agents Automate Data Curation?
• Efficient Memory Management for Large Language Model Serving with PagedAttention
• APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets
• The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
• Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
• Propagating Knowledge Updates to LMs Through Distillation
• PostTrainBench: Can LLM Agents Automate LLM Post-Training?
• Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
• Representation Learning with Contrastive Predictive Coding
• MIRA: Mid-training Rubric Anchoring for Source-Aware Data Selection
• FrontierScience: Evaluating AI's Ability to Perform Expert-Level Scientific Tasks
• MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
• Qwen2 Technical Report
• Qwen3 Technical Report
• SWE-smith: Scaling Data for Software Engineering Agents

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