Autonomous Research Project Management as an Agent Skill: A Case Study in Exact Spectral Spatial Regression

Abstract:This work presents an end-to-end demonstration of autonomous machine learning research conducted by an agent skill on consumer hardware. The demonstration evaluates an FFT-based Kernel Ridge Regression (KRR) solver for regular spatial grids using 2005 monthly NOAA Kaplan SST v2 anomaly fields on a $36 \times 72$ grid. This was autonomously executed by DeepSeek V4 Flash, orchestrated by our agent skill suite within DeepSeek Harness (DSH). Experiments were executed on CPU-only hardware (Apple M2 Pro; 78.7 s solver time, 1.57 GB peak RSS). Long-horizon state was decoupled into a file-based epic- and issue-tracking substrate. Across 74 sub-agent sessions, the agent demonstrated closed-loop scientific resilience: routing two failed hypothesis review gates back to literature retrieval, patching bootstrap indexing bugs, and executing with only four discrete human steering events. Finally, we reflect on autonomous research governance, arguing that scientific credibility requires inspectable state, falsifiable review gates, and transparent reporting of negative results, urging the machine learning community to favour agent-accessible structured formats over static PDF manuscripts.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

arXiv:2609.31683 [cs.LG]

(or arXiv:2609.31683v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.31683 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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