SkillProx: Self-Evolving Agent Skills via Proximal Textual Gradient Descent

TL;DR - SkillProx is a proximal-gradient-inspired framework for evolving an LLM agent's textual "skills" (reusable procedural notes loaded into context, no weight updates), pairing closed-loop diagnostic edits with a utility-aware pruning stage. It matters because it treats skill deletion/consolidation as a first-class operation rather than a generic edit, addressing context bloat in self-improving agents.

• Forward stage: re-executes diagnosis-driven edits on the same task batch, rolls back regressions, and feeds measured outcomes back into later diagnoses — creating explicit diagnosis→outcome feedback missing in prior text-gradient methods.
• Backward (proximal) stage: decomposes a skill into auditable knowledge units, scores each via a frozen leave-one-out utility audit, then applies validation-gated consolidation, demotion, or removal.
• Framing is a composite objective trading off task loss against skill complexity, mirroring proximal gradient descent in text space.
• Reported +3.0 percentage points average accuracy over the strongest gradient-based baseline across in-distribution and OOD benchmarks and multiple backbone LLMs; ablations show closed-loop diagnosis and proximal refinement contribute complementarily.