Author

Jingzhe Xu

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Preprint Aug 2026

GDPevo: Evaluating Agent Self-Evolution on Real Business Tasks

Agent self-evolution updates an agent's persistent state from prior experience and reuses it to solve related tasks more effectively. Evaluating self-evolution is difficult: existing benchmarks provide limited coverage of economically valuable task domains, do not always design training and test tasks such that test-time gains can be attributed to training experience, and remain vulnerable to data contamination. We present GDPevo, an evolution-native benchmark grounded in GDP-related enterprise workflows, together with the fully automated data pipeline that generates it. Its core mechanism, rule hybridization, decomposes each enterprise workflow into atomic business rules, distributes subsets of these rules across training tasks, and recombines them in held-out test tasks so that test-time gains are attributable. GDPevo spans CRM, ERP, finance, healthcare, legal, and data-centric workflows. Its V1 release contains 120 tasks in 12 groups, with five training and five held-out test tasks per group. Full automation enables the pipeline to expand the suite to 240 tasks in 24 groups (V2) within two days, providing a practical response to contamination. Using GDPevo, we evaluate four agents, each comprising a harness and a model, under four supervision types. Self-evolution consistently improves held-out accuracy by up to 16.44 percentage points. But the best evolved agents remain far below the fully informed oracle ceiling of 91.6%, indicating that the self-evolution ability of current agents remains far from fully realized. We publicly release the pipeline, benchmark, and full evaluation results at https://github.com/Prism-Shadow/GDPevo.

Leijun Zhou, Zhihao Liu, Xiang Qu et al. · 0 citations

Conference Jul 2026

Temporal Metamorphic Testing for RAG-Based LLMs under Evolving Knowledge

Evaluating the temporal consistency of RetrievalAugmented Generation (RAG) systems is challenging because conventional evaluation methods rely on ground-truth answers, which are difficult to define under continuously evolving knowledge. This paper presents a temporal metamorphic testing framework for RAG-based large language models (LLMs) that evaluates relational consistency across multiple executions over time rather than comparing outputs against static reference answers. To support temporal metamorphic testing, we construct controlled temporal revision families from seed facts extracted from real-world documents. Each fact is systematically cloned and transformed across multiple timestamps to simulate evolving knowledge states. Based on this design, temporal metamorphic relations are instantiated to systematically test RAG systems under different knowledge snapshots. Experiments on 2344 metamorphic test instances show that 48.04% of them violate the expected temporal relations, indicating that temporal inconsistencies are widespread in the evaluated RAG system. These results demonstrate that temporal metamorphic testing provides an effective way to assess temporal reliability in RAG systems without requiring explicit ground-truth answers.

Hongyu Wang, Dave Towey, Ge Zhang et al. · Annual International Compute... · 0 citations