Counting Evidence, Not Feedback: Provenance-Aware Agent Self-Improvement

Self-improving language-model agents can reduce manual workflow design, but deciding which updates to retain remains difficult when repeated interpretations of the same observations are counted as additional evidence. We introduce Provenance-Adjusted Evidence (PAE), a fixed-budget acceptance rule that traces feedback to its originating observations, removes replayed records, and pools judgments sharing the same sources. PAE assigns influence according to source overlap independently of feedback volume, reducing to the mean paired task gain with complete direct scores and adjusting overlapping joint assessments when per-task scores are incomplete. Across GEPA, TextGrad, and MIPROv2-No-Demos with Qwen3-8B, PAE improves five-run mean accuracy and code pass@1 by 0.72–1.33 percentage points on GSM8K, MATH-500, MBPP+, and IFBench under matched budget ceilings. PAE provides a common acceptance framework for direct and joint evaluations, allowing richer interpretations while keeping their influence tied to the underlying observations.