#data-centric-ai News & Analysis

6 articles tagged with #data-centric-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · Jun 117/10

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ISE: An Execution-Grounded Recipe for Multi-Turn OS-Agent Trajectories

Researchers introduce ISE (Intent → Simulate → Execute), a three-stage framework for training OS agents that generates 43,956 structured intents and 23,132 multi-turn trajectories with live execution validation. Fine-tuning Qwen3-8B on this dataset achieves 37.7% pass@1 on ClawEval, outperforming GPT-4o zero-shot and the larger Qwen3-32B model, demonstrating that high-quality synthetic data design can overcome model scale limitations.

🧠 GPT-4

AIBullisharXiv – CS AI · Jun 17/10

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Fighting Numerical Hallucinations via Data-centric Compilation for Online Financial QA

Researchers propose DCRC, a data-centric framework addressing numerical hallucinations in LLM-based financial question-answering systems. The approach combines adversarial data construction, multi-stage training, and executable reasoning programs to improve reliability in high-stakes financial applications where accuracy is critical.

AIBullisharXiv – CS AI · May 297/10

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PhoneWorld: Scaling Phone-Use Agent Environments

PhoneWorld introduces a scalable pipeline that automatically converts real mobile app interactions into controllable environments, tasks, and training data for phone-use AI agents. The system demonstrates significant performance improvements across multiple benchmarks by leveraging real GUI trajectories rather than hand-built environments, addressing a critical bottleneck in mobile agent development.

AINeutralarXiv – CS AI · May 97/10

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Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code

A systematic review of 114 studies reveals that code quality defects in large language models stem primarily from training data imperfections rather than model limitations alone. The research establishes a taxonomy linking 18 propagation mechanisms between data quality issues and generated code failures, while advocating for proactive data governance over reactive post-generation filtering.

AIBullisharXiv – CS AI · Jun 116/10

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APEX: Automated Prompt Engineering eXpert with Dynamic Data Selection

APEX introduces a data-efficient framework for automatic prompt optimization in large language models by dynamically categorizing training data into Easy, Hard, and Mixed tiers. The system prioritizes Mixed-tier data to identify high-leverage subsets that improve prompt quality, achieving 11.2% performance gains on Gemini 2.5 Flash with 40% fewer evaluations than static approaches.

🧠 Gemini

AINeutralarXiv – CS AI · May 276/10

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Alignment Tuning for Large Language Models: A Data-Centric Lens on Alignment Data Pipelines

A new arXiv survey reframes large language model alignment tuning through a data-centric lens, decomposing alignment data construction into three stages: response synthesis, preference evaluation, and preference instantiation. By organizing existing alignment methods into a unified taxonomy, the research identifies design trade-offs and failure modes while establishing principles for improving alignment data pipeline design.