Behavioural Signatures of a Language-Model Agent in Autonomous Vision-Architecture Research

Autonomous, agent-driven research is increasingly plausible, yet we have little concrete evidence of what a general-purpose language model actually does when given sustained responsibility for an open-ended research problem. We report a long-horizon study in which a single language-model agent acted as the sole researcher on a vision-architecture problem. Given only a framed question, an initial hypothesis, a compute allocation, and a small set of tools, the agent proposed, implemented, trained, and recorded more than a hundred sequential single-variable experiments with no human tactical input. It arrived at a channel-attention vision transformer that mixes tokens purely through attention over the channel axis, using no spatial self-attention at any stage, and drove it from a $69.67\%$ baseline to $96.59\%/97.25\%$ top-1 (100/300 epochs) on CIFAR-10 at $5.8$M parameters and to $85.07\%$ on CIFAR-100 at $22$M; on ImageNet-1K it reached $79.8\%$ at $26.6$M parameters and $3.7$ GFLOPs, matching DeiT-S at lower compute but about two points below the strongest published backbones. Beyond the artefact, we document and analyse a dense, reproducible behavioural signature: rapid early gains, a long saturation plateau, a persistent bias toward greedy incremental hypotheses, independent rediscovery (and one overturning) of established design choices, and recovery only after a human expanded the agent's tools. We find this signature is shaped at least as much by the surrounding workflow as by the model itself. We release the architecture, the agent's complete per-hypothesis record, and a behavioural analysis of the process, and discuss what autonomously-produced results imply for how such work should be verified and evaluated.