Large language model (LLM) agents for long-horizon interactive tasks typically follow a ReAct-style protocol, issuing one primitive action per LLM round. While this enables frequent replanning, it is inefficient for long-horizon tasks where many rounds are spent on routine action sequences. A natural alternative is to let the agent emit variable-length action chunks. However, naively training such policies with standard reinforcement learning fails: the agent either collapses to single-action behavior or over-commits to excessively long sequences. Both failures share a common root cause: the inability to learn chunk boundaries. We propose SPACE, which addresses this challenge by distilling chunk-boundary supervision from trajectory-induced programmatic skills. We induce two-level programmatic skills from successful trajectories, where subskill boundaries serve as direct chunk-boundary supervision. This temporal structure is then distilled into a primitive-chunk policy via hybrid on-/off-policy optimization with chunk-aware credit assignment. Experiments on ALFWorld and ScienceWorld show that SPACE improves success rates by 7.0%-31.3% over the strongest baseline in each setting while reducing average LLM decision rounds by up to 78.9%.

The growth of agent skills has transformed how agentic systems are built, evaluated, and deployed. As skill libraries continue to scale, rigorous evaluation becomes critical to ensuring their utility, quality, and safety in real-world applications. Consequently, the field is undergoing an emerging paradigm shift from isolated skill creation to automated, evaluation-driven skill evolution. In this survey, we systematically examine the landscape of skill evolution and evaluation beyond foundational skill creation. We categorize evolution into four distinct paradigms, spanning execution feedback, trajectory distillation, compression, and reinforcement learning, showing how each element contributes to improving skill utility and reliability. We also provide an analysis of six skill-centric benchmark categories, identifying structural gaps in benchmark coverage, trade-offs, and metric richness to advance skill research. Finally, we identify open directions for building skill ecosystems that are generalizable, efficient, and verifiably safe. The project URL is https://github.com/Cassie07/AgentSkill_Survey

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides open access to macromolecular structures and offers an extensive structural keyword search infrastructure. However, the current RCSB PDB APIs do not support natural-language semantic search. To overcome this challenge, we propose RAD-PROTEIN, a novel multi-source knowledge-enriched Retrieval-Augmented Protein structure search system that answers user natural-language queries by jointly leveraging heterogeneous structural literature and metadata. In addition, we introduce the first benchmark for text-based protein-structure search evaluation, comprising 454 expert-verified queries. RAD-PROTEIN substantially outperforms baselines, achieving up to +19.8% Recall@1 and +16.1% in MRR with hybrid retrieval and multi-source knowledge enrichment. Comprehensive ablation studies further confirm the robustness of our proposed approach. Finally, we develop an interpretable interface to return top-ranked structures with LLM-generated explanations, enabling efficient human verification and downstream scientific analysis. Code: https://github.com/Cassie07/RAD_PROTEIN