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.