Truss Links are a modular robot platform originally developed for studying Robot Metabolism, the ability for machines to grow, repair, and intelligently adapt by integrating components from their environment or other robots. Here, we investigate whether Truss Links can be more than robots that build themselves: can they serve as a creative medium accessible to non-experts? In this paper, we present a preliminary research-through-design exploration of Truss Links as digital building blocks for the physical world. We demonstrate how Truss Links can be used to animate text, animate two- and three-dimensional shapes, form kinetic sculptures, and hand-build a delta pick-and-place robot. We discuss what this exploration reveals about the design properties that can make a research-grade robotic platform accessible as a creative medium.

Large Language Models (LLMs) generate text by sampling the next token from a probability distribution over the vocabulary at each decoding step. Popular sampling methods like top-p (nucleus sampling) often struggle to balance quality and diversity, especially at higher temperatures which lead to incoherent or repetitive outputs. We propose min-p sampling, a dynamic truncation method that adjusts the sampling threshold based on the model's confidence by using the top token's probability as a scaling factor. Our experiments on benchmarks including GPQA, GSM8K, and AlpacaEval Creative Writing show that min-p sampling improves both the quality and diversity of generated text across different model families (Mistral and Llama 3) and model sizes (1B to 123B parameters), especially at higher temperatures. Human evaluations further show a clear preference for min-p sampling, in both text quality and creativity. Min-p sampling has been adopted by popular open-source LLM frameworks, including Hugging Face Transformers, VLLM, and many others, highlighting its considerable impact on improving text generation quality.

We introduce OpenDebateEvidence, a comprehensive dataset for argument miningand summarization sourced from the American Competitive Debate community.This dataset includes over 3.5 million documents with rich metadata, making itone of the most extensive collections of debate evidence. OpenDebateEvidencecaptures the complexity of arguments in high school and college debates, pro-viding valuable resources for training and evaluation. Our extensive experimentsdemonstrate the efficacy of fine-tuning state-of-the-art large language models forargumentative abstractive summarization across various methods, models, anddatasets. By providing this comprehensive resource, we aim to advance com-putational argumentation and support practical applications for debaters, edu-cators, and researchers. OpenDebateEvidence is publicly available to supportfurther research and innovation in computational argumentation. Access it here:https://huggingface.co/datasets/Yusuf5/OpenCaselist.