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.
Adaptive model search is path dependent: once a challenger is adopted, it becomes the reference from which later candidates are generated. A statistically unsupported replacement can therefore alter h...
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 investigates when historical action credit for tool‑using agents must be updated after policy changes. It introduces pairwise branch sensitivity to measure how policy updates affect action‑distinguishing branches, and proposes a first‑order anchored credit‑transport estimator along with a Decision‑Sufficient Credit Gate (DSC‑Gate) to decide whether to reuse, transport, or resample credit. Experiments show that DSC‑Gate reduces new tool steps by 39.4% with negligible impact on regret, demonstrating that many policy updates can avoid costly recomputation of action credit.
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.
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:2607. 00871v1 Announce Type: new Abstract: Self-evolving agents violate the assumption behind most learning-theoretic guarantees: the data, evaluator, components, and hypothesis space are produced by the policy being updated.
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.
The study introduces RegimeShift‑Surrogates, a streaming benchmark that tests surrogate models across eight tasks and multiple regimes. It compares revalidation—choosing the model with lowest current‑window validation loss—to stateful adaptive controllers and finds that revalidation consistently outperforms stateful methods, achieving lower mean log regret in most task‑scenario combinations. The results suggest that fresh validation evidence is more valuable than carrying over past evidence when dealing with distribution shifts.
arXiv:2610.10426v1 Announce Type: new Abstract: Terminal-agent capability depends jointly on model weights and the runtime harness that formats prompts, binds tools, and handles error recovery. Exist...
arXiv:2608. 01130v1 Announce Type: new Abstract: A broad range of models face the mismatch where they are updated through trajectory losses but are evaluated by downstream task reward.
CAFE (Coupled Agent–Feedback Evolution) is a framework that lets a shared‑parameter model alternate between acting as a search agent and as a critic that provides corrective feedback. By learning when to request feedback and how to use it, CAFE trains the agent to recover from its own failures and shapes rewards both online and offline. Experiments on seven search benchmarks show that CAFE outperforms other RL‑based agents, maintains gains on out‑of‑domain tests, and reduces hallucinations, indicating that co‑evolving feedback is essential for self‑improving search agents.
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. We propose A-SR, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views.