PRefLexOR (Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning) combines preference optimization with concepts from Reinforcement Learning to enable models to self-teach through iterative reasoning improvements. We propose a recursive learning approach that engages the model in multi-step reasoning, revisiting, and refining intermediate steps before producing a final output in training and inference phases. Through multiple training stages, the model first learns to align its reasoning with accurate decision paths by optimizing the log odds between preferred and non-preferred responses. During this process, PRefLexOR builds a dynamic knowledge graph by generating questions from random text chunks and retrieval-augmentation to contextualize relevant details from the entire training corpus. In the second stage, preference optimization enhances model performance by using rejection sampling to fine-tune reasoning quality by continually producing in-situ training data while masking the reasoning steps. Recursive optimization within a thinking token framework introduces iterative feedback loops, where the model refines reasoning, achieving deeper coherence, consistency, and adaptability. Implemented in small language models with only 3 billion parameters, we should that even tiny models can iteratively teach themselves to reason with greater depth and reflectivity. Our implementation is straightforward and can be incorporated into any existing pretrained LLM. We focus our examples on applications in biological materials science and demonstrate the method in a variety of case studies that range from in-domain to cross-domain applications. Using reasoning strategies that include thinking and reflection modalities we build a multi-agent recursive self-improving inference approach to successively improve responses via repeated sampling in inference time.

The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge has been systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model was finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further enhanced with enhanced reasoning ability, as well as with retrieval-augmented generation to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has been shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model showed impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.

When the gravity acting on them is increased, locusts adapt. Locusts placed in a centrifuge to mimic the conditions of hypergravity grew tougher legs than those living normally – but not all of them survived the process. Many biological materials, such as bone and wood, can adapt and become stronger under physical strain, but it isn't clear whether animals with shell-like exoskeletons can adapt in the same way as those with internal skeletons. Karen Stamm and Jan-Henning Dirks at the City University of Applied Sciences in Bremen, Germany, studied this by placing locusts inside a specially designed centrifuge to stress-test their exoskeletons using simulated hypergravity. The locusts were assigned to one of four gravity conditions: 1g – which is typical gravity at sea level and didn't involve a centrifuge – and 3g, 5g or 8g conditions, all of which did involve centrifuging the insects.

Organoids 3D printing has quickly become one of the leading segments of the 3D printing industry in terms of innovation. Until recently, the market was primarily focused on North America, however many companies, laboratories, and universities around the world are exploring this field as well. Thanks to 3D printing techniques, cells and biomaterials can be combined and deposited layer by layer to create biomedical developments that have the same properties as living tissues. During this process, various bio-links can be used to create these tissue-like structures, which have applications in the fields of medical and tissue engineering. Of course, it is more than knowing that the goal of all these developments is to successfully bioprint a fully functional human organ.

A Note on Optimal Sampling Strategy for Structural V ariant Detection Using Optical Mapping Weiwei Li Department of Statistics and Operations Research University of North Carolina at Chapel Hill weiweili@live.unc.edu Abstract Structural variants compose the majority of human genetic variation, but are difficult to assess using current genomic sequencing technologies. Optical mapping technologies, which measure the size of chromosomal fragments between labeled markers, offer an alternative approach. As these technologies mature towards becoming clinical tools, there is a need to develop an approach for determining the optimal strategy for sampling biological material in order to detect a variant at some threshold. Here we develop an optimization approach using a simple, yet realistic, model of the genomic mapping process using a hyper-geometric distribution and probabilistic concentration inequalities. Our approach is both computationally and analytically tractable and includes a novel approach to getting tail bounds of hyper-geometric distribution. We show that if a genomic mapping technology can sample most of the chromosomal fragments within a sample, comparatively little biological material is needed to detect a variant at high confidence. 1 Introduction Structural variants (SV), insertions, deletions, translocations, copy number variants, are by far the most common types of human genetic variation (Chaisson et al., 2015). They have been linked to large number of heritable disorders (Hurles et al., 2008). Technology to assay the presence or absence of these variants has steadily improved in ease and resolution (Huddleston and Eichler, 2016; Audano et al., 2019).