Xi Zheng, Yinghui Huang, Xiangyu Chang, Ruoxi Jia, Yong Tan
Modern AI model markets assemble models from heterogeneous pipeline contributions, including raw data, derivative data, and data-related services, whose value can depend on upstream inputs and inherited model states. Conventional valuation approaches focus on data contributions without explicitly accounting for these production relationships. They can therefore misattribute value by evaluating contributions outside their upstream production context. We formalize this pipeline-context valuation mismatch and develop Asymmetric Data Shapley (ADS), a unified framework for structure-aware value attribution. ADS translates documented production relationships into ordered valuation groups and evaluates contributions among peers conditional on their upstream production context. We provide an axiomatic characterization of the resulting allocation and quantify allocation uncertainty when evidence supports multiple valuation structures. To support valuation at larger scales or with costly utility evaluations, we develop a Monte Carlo estimator (MC-ADS) and a surrogate-assisted estimator (SA-MC-ADS). Experiments show that ignoring production dependencies systematically shifts value toward redundant derivative data and away from useful upstream data and services. Moreover, experiments on derivative data selection and on dataset procurement during multi-stage large language model fine-tuning show that decisions guided by ADS improve downstream model performance relative to the respective benchmarks.
Hanze Li, Athan Z. Li, Feiyang Kang, Jiachen T. Wang, Yuhan Chen, Ruishan Liu, Ruoxi Jia
Data curation is a first-order design choice in machine learning, yet tailoring training data to a downstream model and task remains costly in labor, expertise, and compute. Datasets are therefore often processed through fixed pipelines and reused across applications. We show that this assumption is fragile: across 55 dataset--model tasks spanning biological domains, the same curation recipe can help one model while hurting another. Effective curation must adapt to the downstream setting, motivating automated search over data recipes. We first build an agentic system that searches for curation policies at deployment, improving data utility via \textit{test-time curation (TTC)}. Although achieving performance gains, its search is driven largely by recent feedback and remains local. Adding persistent memory helps the agent retain evidence across iterations and reveals a deeper problem: it forms claims that extend beyond the conditions in which they were observed, sometimes abandoning an entire strategy after a single unsuccessful trial. To address this problem, we introduce CaliMem, a calibrated-memory agentic loop that requires the agent to predict the outcome of each proposed policy before executing it. Comparing this prediction with the observed result gives the agent a \textit{reality check} on the claims behind its proposal. It uses the prediction error to retain supported claims, revise mistaken ones, and specify the conditions under which a conclusion holds. This makes the agent's beliefs testable, reduces overgeneralization, and supports more effective long-horizon exploration. Across biological and LLM post-training tasks, CaliMem consistently outperforms fixed curation baselines and published methods, and exceeds full-data training by up to 19.4\%. Code is open-sourced.