playbook development

This work addresses the limitations of traditional security auditing, which relies on manually crafted playbooks that are difficult to automate and lack cross-agent transferability. The authors propose EvoHunt, a novel framework that enables the first fully automated evolution and cross-model transfer of security audit playbooks. EvoHunt employs three types of agents—auditing, evaluation, and revision—that collaboratively optimize playbooks in a closed loop over open-source repositories without human intervention. Integrating large language models, an agent execution framework, and an evolvable playbook structure, the approach significantly enhances the vulnerability discovery and validation capabilities of weaker models. Experiments demonstrate that evolved playbooks improve end-to-end exploit success rates by 6× for Codex/GPT-5.4-xhigh and, when transferred to Qwen3.6-27B, increase target match rates from 2.4% to 6.5%, surpassing existing commercial solutions.

Existing cybersecurity response playbooks are predominantly heterogeneous and non-machine-readable, severely impeding automation and interoperability in SOAR platforms. To address this, we propose a modular, LLM-driven translation framework leveraging prompt engineering to automatically convert unstructured playbooks into standardized CACAO format. Our method innovatively integrates syntactic validation, semantic fidelity constraints, and multi-turn iterative refinement to ensure accurate structural reconstruction of complex control flows and preservation of operational semantics. Evaluated on a curated benchmark dataset, our approach significantly outperforms baseline models: it reduces CACAO syntax error rate by 72%, improves recall of critical procedural nodes by 39%, and supports end-to-end deployment. This work establishes a reusable technical pathway for structured representation and automated orchestration of security knowledge, advancing the operationalization of human-authored playbooks in autonomous security systems.

This study addresses the persistent challenges faced by User Experience Research (UXR) teams—namely, stakeholder bias, reactive engagement, and fragmented insights—that hinder their ability to exert strategic influence. To overcome these limitations, the authors innovatively integrate structured strategic thinking into UXR function development, proposing an organizational maturity model grounded in a UXR Point-of-View (POV) framework. Complementing this model is a practical playbook that combines “offensive” and “defensive” strategies to guide implementation. This integrated approach systematically enables UXR teams to transition from tactical execution to strategic impact, significantly enhancing their capacity to forge strategic partnerships, generate actionable insights, and contribute meaningfully to long-term corporate strategy formulation.

Cybersecurity threats are growing increasingly sophisticated, necessitating standardized, automated incident response via playbooks; however, the emerging CACAO (Cyber Automation and Coordination Application Ontology) standard lacks mature tooling—particularly a knowledge management system (KMS) supporting its full lifecycle—hindering operational adoption. To address this gap, we design and implement the first open-source, CACAO-compliant KMS. Our system adopts a modular microservices architecture and integrates JSON Schema validation, semantic versioning, RESTful APIs, and an extensible execution engine to enable standardized, scalable playbook governance. It is the only publicly available, fully documented CACAO KMS implementation to date. Empirical evaluation demonstrates significant improvements in playbook development velocity and cross-platform collaboration, while enhancing interoperability and engineering readiness of security orchestration, automation, and response (SOAR) workflows.

Rigid activity implementation binding in digital business processes hinders adaptation to heterogeneous organizational requirements. Method: This paper proposes a three-level dynamic binding mechanism—operating at compile time, launch time, and runtime—that enables concurrent execution of multiple implementations for the same activity and supports context-aware, dynamic customization of input/output data contracts. Integrating Software Product Line (SPL) engineering with Process-Aware Information Systems (PAIS), we develop a variability modeling and runtime feature configuration framework. Contribution/Results: Our approach achieves, for the first time, end-to-end flexible activity binding across the full process lifecycle. It overcomes the limitations of conventional single-version, static binding by enabling on-demand composition of diverse activity implementations and data interfaces within a unified process model. This significantly enhances the adaptability and configurability of process systems in multi-organizational settings.

研究通过采访技术作家探讨了软件文档审查过程及其挑战，识别了五个审查阶段，并揭示了组织和技术上的难题。

论文针对软件工程中的需求差距和模型差距问题，提出了一种利用部署证据来持续修订需求、模型或评估器的方法，以缩小这些差距。

本文探讨了通过状态构建方法，利用AI系统生成和修订可交付成果的问题，并提出了维持连贯、负责控制的原则与机会。

This study systematically evaluates the transferability of frozen prompt scripts—those deployed without retraining—across diverse environments, with a focus on their impact on accuracy, inference cost, and runtime behavior. Employing a unified distillation–validation–transfer protocol, experiments are conducted on benchmarks including ALFWorld, TAU2-Bench, and XBench-DeepSearch, enabling the first quantitative assessment of how runtime latency and cost shifts constrain transfer performance. The findings reveal that script transfer is highly condition-dependent, showing effectiveness only in specific scenarios—such as when distillation-based prompting outperforms fixed exemplars—while most transfer paths yield negligible gains and often trigger issues like redundant queries, premature termination, and cost surges. These results suggest that frozen scripts should be treated as conditional cold-start options rather than default reusable strategies, underscoring the necessity of rigorous validation in target environments.

This study addresses the persistent challenge of translating academic frameworks into actionable practices during the pre-production phase of AAA game development, where theoretical models often falter due to misalignment with industrial constraints, production realities, and cross-functional collaboration demands. Through in-depth interviews with 15 AAA game UX leads and subsequent qualitative analysis, the research elucidates how design decisions integrate theory, experiential knowledge, and evidence-informed intuition to balance player needs, technical feasibility, and creative vision. The work proposes a flexible theoretical toolkit that operationalizes academic concepts into context-sensitive insights, systematizes tacit expertise, and adapts to dynamic development workflows. Key contributions include a shared language for cross-team alignment, reusable design systems, and adaptive strategies that offer a practical pathway to bridge the gap between academic research and industry practice.