Multi-agent systems can learn continually through skill libraries with credit assignment, not reflection memories

arXiv 2609.02094·medium signalXBlueskyLinkedInCopy link

MASkills (arXiv 2609.02094, 2026-09-02) makes the case that experience memories built by self-reflection are hard to invoke, refine, or scale, and that the actionable unit is a skill: structured procedural knowledge specifying when to act, how to act, and which tools to use. Its pipeline combines skill-conditioned credit assignment, hierarchical credit aggregation, and momentum-smoothed optimization so libraries evolve through refinement, induction, consolidation, and pruning, evaluated on HotpotQA, LoCoMo, and GAIA. Code is at github.com/DaRL-GenAI/MASkills, created 2026-08-30 with 0 stars, so this is published-and-unexamined rather than a shipped artifact.