Justin Rokisky

Cash Costello, James Mayfield, Elsbeth Turcan et al.

We present SFBench, a benchmark dataset for evaluating systems that assess the feasibility of scientific claims. SFBench includes 197 claims in materials science, each annotated with a ground-truth feasibility score on a five-point scale along with an explanation of that assessment. The collection differs from previous collections in several important ways: 1) it defines a complex task that requires reasoning over claims of varying scientific feasibility; 2) its claims are not extracted from existing scientific publications but are created de novo, greatly reducing the chances that LLMs have trained on them; 3) claims and ground truth are established by subject matter experts, not by artificial intelligence; and 4) unlike many benchmarks that ask about question/answer pairs, provide multiple choice answers, or ask questions requiring short, fixed answers, SFBench explanations are completely open-ended. We describe the benchmark design, data creation process, and evaluation metrics, and we report baseline results using recent GPT models.

Alexander Spinos, Bradley Woosley, Justin Rokisky et al.

Traditionally, autonomous reconnaissance applications have acted on explicit sets of historical observations. Aided by recent breakthroughs in generative technologies, this work enables robot teams to act beyond what is currently known about the environment by inferring a distribution of reasonable interpretations of the scene. We developed a map predictor that inpaints the unknown space in a multi-agent 2D occupancy map during an exploration mission. From a comparison of several inpainting methods, we found that a fine-tuned latent diffusion inpainting model could provide rich and coherent interpretations of simulated urban environments with relatively little computation time. By iteratively inferring interpretations of the scene throughout an exploration run, we are able to identify areas that exhibit high uncertainty in the prediction, which we formalize with the concept of generative entropy. We prioritize tasks in regions of high generative entropy, hypothesizing that this will expedite convergence on an accurate predicted map of the scene. In our study we juxtapose this new paradigm of task ranking with the state of the art, which ranks regions to explore by those which maximize expected information recovery. We compare both of these methods in a simulated urban environment with three vehicles. Our results demonstrate that by using our new task ranking method, we can predict a correct scene significantly faster than with a traditional information-guided method.