Existing large language model (LLM) agents for automating data science show promise, but they remain constrained by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs.We introduce DatawiseAgent 1 , a notebook-centric LLM agent framework for adaptive and robust data science automation.Inspired by how human data scientists work in computational notebooks, DatawiseAgent introduces a unified interaction representation and a multi-stage architecture based on finitestate transducers (FSTs).This design enables flexible long-horizon planning, progressive solution development, and robust recovery from execution failures.Extensive experiments across diverse data science scenarios and models show that DatawiseAgent consistently achieves SOTA performance by surpassing strong baselines such as AutoGen and TaskWeaver, demonstrating superior effectiveness and adaptability.Further evaluations reveal graceful performance degradation under weaker or smaller models, underscoring the robustness and scalability.

Multimodal learning in open environments faces significant challenges due to modality incompleteness and noise interference. Current prompt engineering emphasizes modality absence over task-instance contextualization that hinders cross-modal knowledge transfer, whereas conditional generation approaches for missing modality recovery exhibit an excessive reliance on the quality of available modalities. To address these issues, we propose a Gamma-Cooperative Retrieval Diffusion model (γ-CRD), inspired by the human brain's multi-source contextual completion mechanism, which leverages a retrieval-augmented prompt generation framework and normal-inverse Gamma noise modeling to enhance robustness in incomplete multimodal learning. Specifically, it consists of three modules: (1) Retrieval-Augmented Contextualization: Construct a multimodal memory bank and retrieve relevant instances via similarity calculation under a gating mechanism to augment the contextualization of missing modalities. (2) Prompt-Driven Diffusion Generation Module: Builds prompts based on retrieval results and incorporates them into a denoising diffusion probabilistic model through an attention mechanism to enhance contextualized knowledge transfer and generate missing modalities. (3) Inverse-Gamma Noise Optimization Module: Model a mixed normal-inverse gamma distribution, which is dynamically aware of noise and enables uncertainty estimation in multimodal fusion, ensuring robust and reliable multimodal regression. Extensive experiments on three real-world datasets demonstrate that γ-CRD consistently outperforms state-of-the-art baselines, especially achieving a 5.7% improvement in accuracy compared to the leading model e.g., IMDer [1].