translate research to product

This study addresses the limited depth and breadth of interdisciplinary knowledge integration caused by the poor reusability of underlying data in traditional literature reviews. We propose a "dual-track integration" conceptual framework that leverages the TIB Knowledge Loom to generate machine-readable outputs, combining systematic review methodologies with knowledge gap mapping techniques to comparatively evaluate manual extraction against automated approaches for knowledge synthesis. Our analysis reveals that only 8% of the examined literature provides reusable data, while demonstrating that constructing knowledge integration frameworks linking publications to research infrastructure effectively expands integration pathways. The core contribution of this work lies in identifying that ensuring the accessibility and executability of data, code, and workflows is essential for overcoming existing bottlenecks in knowledge integration.

This study addresses the fragmentation of evaluation criteria for automated research systems and the difficulty of direct cross-task comparison. Employing a systematic literature review, it comprehensively examines evaluation designs across six task categories, including literature synthesis and ideation. By comparing benchmark construction and scoring protocols, this work proposes a complementary evaluation framework encompassing output-level, process-level, and human-subject assessments. It reveals the capability differences reflected by distinct designs and underscores the critical role of calibration specificity and resource budgets in performance interpretation. Furthermore, the project identifies gaps in diagnostic evaluation and provides recommendations for standardized reporting and auditing. Ultimately, these contributions offer practical guidance for benchmark selection and future research design in evaluating automated scientific discovery systems.

Scientific software development suffers from poorly specified requirements and inadequate management, severely compromising software quality and experimental reproducibility. To address this gap, this study formally establishes scientific software as a novel application domain for requirements engineering (RE). Through eight in-depth interviews with 12 researchers, we conduct an exploratory qualitative study employing thematic coding analysis. Our findings identify three core challenges: (1) highly ambiguous and evolving requirements, (2) latent or unidentified stakeholders, and (3) absence of systematic requirement validation mechanisms. Based on these insights, we propose a domain-specific RE vision and a challenge framework tailored to scientific software contexts. This work lays the theoretical foundation and methodological guidance for lightweight, agile, and traceable RE practices in scientific software development—thereby filling a critical void in systematic RE research for this domain.

This work addresses the challenge of quantifying the academic impact of commercial engineering software such as Ansys Granta, which is hindered by inconsistent citation practices and rapidly growing publication volumes. We propose the first reproducible, semi-automated framework that integrates DOI and citation parsing, expert annotation, and a relational database (Ansys Granta MI Enterprise) to transform heterogeneous usage evidence into a structured knowledge base. As of September 2025, the framework has compiled a multi-source literature repository comprising over 1,100 manually verified records, enabling rapid retrieval, systematic review reproduction, and technology landscape scanning. The resulting knowledge base reveals dominant application domains, key contributing institutions, and integration patterns within CAD/CAE/FEM environments, thereby facilitating systematic tracking and analysis of the long-term technical influence of commercial engineering software.

Existing research ideation tools emphasize breadth-oriented idea generation but lack support for iterative refinement, elaboration, and evaluation—hindering literature-grounded, deep-reading–driven conceptual evolution. Method: We propose the first literature-driven interactive research ideation system, integrating a composable “idea element” canvas model with a multi-dimensional (problem/solution/evaluation/contribution) co-evolution mechanism. Our approach innovatively incorporates LLM-powered literature-aware feedback generation, graph-structured idea modeling, and interactive multi-path variant exploration. Contribution/Results: Experiments demonstrate a 42% increase in user-generated idea output and significantly enhanced detail elaboration. Seven researchers successfully applied the system across the full ideation pipeline—from initial topic conception to paper outline revision—validating its efficacy in supporting deep, iterative, literature-informed research design.

本文提出ScholarStack框架，通过构建可重用、分层的研究资产来解决科学代理任务中的重复处理和知识复用问题。

This study addresses the persistent challenge of translating European academic research into industrial impact, particularly in light of Industry 5.0’s demands for technical depth, sustainability, and human-centric design—requirements inadequately met by traditional doctoral training. To bridge this gap, the project proposes a dual-layer competence framework guided by four design principles: modularity, practical relevance, robust mentorship, and cross-domain applicability. Through expert interviews, co-design workshops, and a multi-method analytical framework, the approach systematically integrates academic rigor with industrial needs, yielding a scalable and modular developmental pathway for early-career researchers. This model effectively narrows the translational divide between scholarly output and real-world industrial application, offering an innovative paradigm for cultivating research talent aligned with the ethos and exigencies of Industry 5.0.

This work addresses the lack of systematic grounding in existing research idea generation methods, which often fail to identify bottlenecks, differentiate prior work, or assess risks effectively. To bridge this gap, the authors propose an evidence-driven framework for generating research ideas, introducing “idea cards” that encapsulate 15 reusable creative patterns distilled from top-tier machine learning conference papers. Each card is structured around context, bottleneck type, and differentiation strategy. The framework integrates multi-source literature retrieval, prior-work collision detection, and pattern-guided generation to support traceable and auditable proposal development. In blind evaluations, proposals generated by this approach significantly outperformed both unskilled and general-purpose baselines in quality while maintaining high novelty.

This study addresses how to evaluate the ability of AI agents to conduct prospective literature searches for unpublished research questions within open corpora. To this end, it constructs a benchmark comprising hidden target papers and their citation contexts, proposes a three-stage decoupled analysis framework encompassing resource exposure, selection, and ranking, and validates the effectiveness of retrospective demonstrations as an actionable learning signal. Performance is quantified through end-to-end retrieval evaluation using the nDCG metric, revealing that even the strongest agent achieves an nDCG@10 of only 0.284. The findings identify resource exposure as the core bottleneck and demonstrate that the proposed benchmark can effectively guide targeted improvements in agent retrieval capabilities.

研究解决科研方法说明不足的问题，通过构建IdeaAMBIG基准测试660个实例来评估编码准备度、缺陷定位及澄清行动生成能力。