Large language model (LLM) agents are increasingly being used to automate machine learning (ML) research workflows, ranging from code development and experiment execution to research reproduction and scientific discovery. Recent systems demonstrate that LLMs can perform substantial portions of ML research engineering with limited human intervention. These advances suggest a shift from automating individual research tasks toward automating long-horizon workflows that transform research artifacts into executable ML systems. However, ML research engineering remains substantially more difficult than isolated code generation. A research artifact typically specifies a system only implicitly through a mixture of natural-language descriptions, equations, pseudo-code, implementation details, datasets, and evaluation protocols. An agent must first infer the intended computational structure, translate it into executable components, coordinate their dependencies, and repeatedly diagnose and repair failures during execution. Consequently, the quality of the final system depends not only on code generation, but also on whether the agent can maintain a consistent representation of what should be implemented throughout this long-horizon process.
Yu-An Liu, Ruqing Zhang Institute of Computing Technology, Chinese Academy of Sciences, Hongru Song , Chinese Academy of Sciences, Jiafeng Guo Institute of Computing Technolgy, Chinese Academy of Sciences, Yixing Fan, Xueqi Cheng Institute of Computing Technology, Chinese Academy
While neural ranking models (NRMs) have achieved state-of-the-art performance in information retrieval, they remain highly vulnerable to imperceptible adversarial perturbations. Existing defenses are predominantly data-centric, exemplified by adversarial training, which requires constructing large collections of adversarial examples. By treating NRMs as black boxes and indiscriminately optimizing all model parameters, these methods incur substantial computational cost and often degrade performance on clean data due to overfitting. In this paper, we advocate that adversarial vulnerability is not uniformly distributed across model parameters, but instead originates from specific internal units. We propose a paradigm shift toward a model-centric defense that addresses vulnerability at its architectural source, without requiring costly retraining or adversarial data generation. Specifically, we introduce Search in the Model, a novel training-free framework that performs fine-grained identification and rectification of vulnerable neurons directly within the model. By formulating neuron identification as a ranking problem, we develop a maximum marginal vulnerability criterion to precisely locate the top-K neurons most responsible for model vulnerability, and apply targeted neuronal inverse perturbation to correct them. Extensive experiments on MS MARCO and TREC 19 show that our approach outperforms state-of-the-art baselines in both defense efficiency and robustness to seen and unseen attacks, while preserving strong performance on clean data.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表