Abstract: We study autonomous mathematical discovery in the Station, an open-world multi-agent environment in which AI agents from different model families pursue a shared research goal without a central coordinator or scripted pipeline. Agents choose their own research directions, conduct experiments, collaborate, and build a shared scientific literature. Across 12 construction problems from the AlphaEvolve catalogue and two additional case studies, the Station obtained results novel relative to the prior literature on five problems: a new infinite family of finite-field Kakeya sets, new ex

Third-year PhD student, NLP / interpretability. I want a reality check from people doing similar work. I started using Claude Code for the boring parts: argparse boilerplate, plotting, config wrangling. Over the last few months the scope has crept. It now writes most of my experiment scaffolding, refactors my dataloaders, does first-pass debugging on training runs, and drafts the analysis scripts. I mostly read diffs and say yes. The output is fine. My throughput is up. The thing bothering me is that I no longer hold my own codebase in my head. When a result looks off, I used to have an inst

Working on a pipeline that recovers a patient specific 3D distal femur from two orthogonal X-ray views (PA + lateral). No CT, no neural network, no massive training set. approach: build a PCA shape model from 50 CT-derived femur meshes (MedShapeNet), then fit it to two silhouettes using PyTorch3D's soft rasterizer with sigma annealing. 10 shape coefficients, Mahalanobis prior to keep things plausible, Adam optimizer, ~1000 iterations. The part that took the longest (and made me suffer the most too) : correspondence. Tried KD-tree nearest neighbor (50.7x roughness vs CT surface), CPD (28.2x),