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Language-model agents now run the whole of quantitative factor research: they propose investment factors, backtest them, select the survivors and retire them, and the answer is governed self-evolution: the agent may propose, and a frozen statistical referee that the agent cannot touch must judge.
The literature is connected to the theory of RSI limits and to the safety and governance questions raised by frontier-lab accounts of closing the loop, and governance-grade measurement of self-improvement is identified as the field's most underpopulated niche.
This work decomposes the always-revise accuracy shift into a content margin (both answers parseable) and format-recovery/loss margins (parseability changes) and shows this can fail at the answer-extraction boundary, and test the failure causally rather than only observationally.
A corpus of 1,547 arXiv papers collected via systematic seed harvest with a disclosed 26.8% bleed filter is surveyed, finding the same pattern: outcome-only signals grow uninformative as horizons lengthen, and the field's response manufactures denser step-level signals.
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