arXiv AI
Cross-Organizational SysML Model Integration: A Survey of Challenges and AI-Supported Tasks
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arXiv AI
Jul 7
LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2
arXiv:2508. 16181v2 Announce Type: replace-cross Abstract: Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models.
arXiv AI
Aug 6
A Chain Is Only as Strong as Its Weakest Link: A Scoping Review of System Integration Audits in AI
arXiv:2608. 04921v1 Announce Type: cross Abstract: As AI systems become increasingly integrated into diverse interfaces and applications, model-centric audits are insufficient to address risks arising from interactions among system components and deployment environments.
arXiv AI
Jul 17
LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain
arXiv:2607. 14659v1 Announce Type: cross Abstract: Interoperability between heterogeneous modeling tools remains a significant challenge in Model-Driven Engineering (MDE), particularly in the automotive domain where multiple modeling languages, as well as defacto standard proprietary and open-source tools coexist.
Hugging Face Trending Papers
Jul 7
Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents
Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons. Reported benchmark gains often obscure recurring failure modes documented across otherwise unrelated evaluation efforts.
arXiv AI
Sep 7
Corporate Language Model (CLM): Transforming Tacit and Fragmented Enterprise Knowledge into a Sovereign, Auditable, and Executable Corporate Intelligence Layer
arXiv:2609. 04377v1 Announce Type: new Abstract: Enterprise AI deployments fail not from model inadequacy, but because organizations lack a structured substrate encoding how they decide, negotiate, and execute.
arXiv AI
Sep 18
A Unified Evaluation Framework for Trustworthy Large Language Models, Agentic AI, and Multimodal Systems
The paper introduces a unified evaluation framework for assessing the trustworthiness of large language models, agentic AI, and multimodal systems. It connects output-level, trajectory-level, and cross-modal assessments across eight dimensions—capability, robustness, safety, fairness, transparency, governance, oversight, and efficiency—while preserving system-specific metrics and providing uncertainty estimates. A meta-evaluation layer checks the validity, reliability, and reproducibility of the evaluation itself, and the framework aligns with governance standards and regulatory requirements.