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The paper introduces Evidence‑Gated Research (EGR), a statistical adoption layer designed for adaptive model search where each new challenger is frozen until evidence is revealed. EGR constructs anytime‑valid evidence across predefined environments, routes it toward unresolved components, composes a persistent candidate e‑value, and forwards this to an online controller. The method guarantees false discovery rate control for the declared all‑environment adoption target, even when earlier adoptions influence later challengers, and demonstrates evidence efficiency and persistent FDR control in large‑scale benchmarks.
arXiv:2608. 09629v1 Announce Type: new Abstract: Self-evolving agents are usually built around prescribed optimization pipelines: the framework decides how to gather evidence, revise a persistent artifact, select candidates, and stop.
The paper explores how data from fixed A/B tests can guide the deployment of adaptive experiments using contextual bandits. By combining off‑policy evaluation with a controlled warm‑start simulation, the authors rank pre‑specified adaptive and non‑adaptive policies using doubly robust estimators. Experiments on synthetic trials and real benchmarks show that adaptive, context‑aware policies outperform fixed allocations when heterogeneity exists, but offer little advantage otherwise.
arXiv:2608. 09537v1 Announce Type: new Abstract: Foundation world models have made remarkable progress in planning, simulation, and embodied intelligence.
arXiv:2608. 04872v1 Announce Type: cross Abstract: Symbolic regression aims to discover closed-form equations from data, but existing LLM-guided methods often rely on a unified proposal loop that compresses heterogeneous search failures into a scalar score and a single prompt.
arXiv:2608. 02636v1 Announce Type: cross Abstract: Self-evolving skill systems promise to improve agents by turning execution feedback into persistent skill updates without changing the underlying model.