Bioprocesses are central to modern biotechnology, enabling sustainable production in pharmaceuticals, specialty chemicals, cosmetics, and food. However, developing high-performing processes is costly and complex, requiring iterative, multi-scale experimentation from microtiter plates to pilot reactors. Conventional Design of Experiments (DoE) approaches often struggle to address process scale-up and the joint optimization of reaction conditions and biocatalyst selection. We propose a multi-fidelity batch Bayesian optimization framework to accelerate bioprocess development and reduce experimental costs. The method integrates Gaussian Processes tailored for multi-fidelity modeling and mixed-variable optimization, guiding experiment selection across scales and biocatalysts. A custom simulation of a Chinese Hamster Ovary bioprocess, capturing non-linear and coupled scale-up dynamics, is used for benchmarking against multiple simulated industrial DoE baselines. Multiple case studies show how the proposed workflow can achieve a reduction in experimental costs and increased yield. This work provides a data-efficient strategy for bioprocess optimization and highlights future opportunities in transfer learning and uncertainty-aware design for sustainable biotechnology.

Large language models (LLMs) have shown promising performance across diverse domains. Many practical applications of LLMs, such as code completion and structured data extraction, require adherence to syntactic constraints specified by a formal language. Yet, due to their probabilistic nature, LLM output is not guaranteed to adhere to such formal languages. Prior work has proposed constrained decoding as a means to restrict LLM generation to particular formal languages. However, existing works are not applicable to the emerging paradigm of diffusion LLMs, when used in practical scenarios such as the generation of formally correct C++ or JSON output. In this paper we address this challenge and present the first constrained decoding method for diffusion models, one that can handle formal languages captured by context-free grammars. We begin by reducing constrained decoding to the more general additive infilling problem, which asks whether a partial output can be completed to a valid word in the target language. This problem also naturally subsumes the previously unaddressed multi-region infilling constrained decoding. We then reduce this problem to the task of deciding whether the intersection of the target language and a regular language is empty and present an efficient algorithm to solve it for context-free languages. Empirical results on various applications, such as C++ code infilling and structured data extraction in JSON, demonstrate that our method achieves near-perfect syntactic correctness while consistently preserving or improving functional correctness. Importantly, our efficiency optimizations ensure that the computational overhead remains practical.

These observations suggest the existence of a tradeoff between average robustness and robust fairness. We use var and rob.acc to denote the variance of class-wise robust accuracy, and average robust accuracy, respectively. We use var and rob.acc to denote the variance of class-wise robust accuracy, and average robust accuracy, respectively. B.1 Naturally Trained Linear model We use var and rob.acc to denote the variance of class-wise robust accuracy, and average robust accuracy, respectively. For any classifier f ( x) in Equation ( 2), we first calculate its natural risk.

Large language models lie at the center of recent advances in artificial intelligence.