Underreporting is a defining problem in the registration of workplace violence across public services. This is reinforced by inconsistent, often poor incident descriptions and the absence of a clear, shared nomenclature. Without an ontology-backed structure, similar events are recorded differently across settings or remain uncodable and effectively invisible. Moreover, WPV is typically registered and studied within sector- and even organization-specific categories and local reporting logics rather than through a shared semantic framework, limiting comparability and cumulative understanding across sectors. To address this, we propose creating and publicizing a workplace violence ontology (WPV-ONTO) to unify the representation of WPV events across public services.
Objective
The objective of this protocol is to describe and justify a research methodology for developing a cross-sector ontology and reference nomenclature for WPV in public services (WPV-ONTO), including the scoping review, expert-consensus, and evaluation procedures used to build it. Promoting openness and high standards during its creation and encouraging its uptake once available are broader project goals that this protocol is designed to support, rather than measurable objectives that this protocol itself tests.
Methods
Using the Protégé ontology editor and the METHONTOLOGY ontology development life-cycle guidelines, we will create an ontology that captures the cross-sector WPV domain in Web Ontology Language (OWL). In order to find common WPV concepts, definitions, and synonyms, the modeling process will employ a methodologically defined, iterative workflow that combines (1) focused scoping searches (Web of Science/PubMed/APA PsycInfo/ERIC/Sociological Abstracts); (2) structured extraction from industry-standard incident reporting tools and code lists; and (3) expert consensus. Based on iterative rounds of scientific literature reviews and industry-standard code lists, a team of domain experts will use a hybrid top-down and bottom-up approach to define and identify key ideas and relationships. A small pilot with 6-8 frontline workers from two sectors will test the prototype reporting form against usability criteria before version 1.0 release.
Results
The primary output will be a comprehensive, versioned WPV-ONTO accommodating key WPV concepts relevant to public services, augmented with synonyms, definitions, and references. WPV-ONTO will include an explicit hierarchical structure and relations supporting inheritance and compositional incident encoding. WPV-ONTO seeks to integrate the needs and conceptualizations of frontline workers, safety and aftercare professionals, sector policymakers, WPV researchers, and health/information systems experts. Recruitment of the expert panel is planned to begin in March 2027, and WPV-ONTO version 1.0 is expected at the end of the 24-month development period.
Conclusions
WPV-ONTO is expected to enable reasoning, inference, and consistent representation of relationships among WPV concepts for application in multiple contexts, including cross-sector reporting harmonization, software and API development, research data integration, and evaluation of prevention and aftercare initiatives. By providing a shared vocabulary and explicit structure, WPV-ONTO may also facilitate linkage with other relevant information systems, such as electronic medical records, and justice and police information systems, reducing methodological fragmentation and supporting coordinated cross-sector learning while retaining the contextual specificity necessary for public service environments.
CLINICALTRIAL
Not applicable.
FLARE is proposed, a novel framework that endows VLAs with robust error recovery capabilities through a ``Retry" and ``Reset" Paradigm, and significantly improves task success and robustness.
Ganlong Zhao, Zijia Tang, Xingping Chen et al.· 3 citations
LifeSciBench is introduced, a benchmark of 750 expert-authored tasks designed to evaluate whether language models can handle realistic life science research work, with each constituent task paired with a human expert-written rubric.
Amelia Liu, Andrew Ho, Anne Marie Droste et al.· bioRxiv· 2 citations
General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sources, and executable code, together with state maintenance, failure recovery, and verifiable delivery. We call this \emph{working capability}: sustained, verifiable progress toward a real-world objective. Apodex 1.1 develops this capability along two complementary dimensions. \emph{Environment Scaling} expands the diversity and verifiability of executable file, search, and code environments, while \emph{Agentic Coordination Scaling} trains agents to decompose long-horizon tasks, delegate parallel work, integrate asynchronous results, and replan. A shared execution harness and AgentOS maintain task state and provenance across tools and agents, and training turns environment trajectories and coordination traces into reliable behavior. Across complex professional work, finance, scientific research, mathematics, coding, and search, Apodex 1.1 reaches the leading performance band despite using a substantially smaller model than many frontier systems. The 35B-parameter Apodex 1.1 Mini further retains strong working capability in a locally deployable form. These results ground agentic intelligence in useful, verifiable work completed over time and advance our goal of building a \emph{Heavy-Duty Solver} for ambitious, long-running tasks.
Apodex Team B. An, B. Li, B. Wang et al.· 1 citation
MAGE explains how externalized knowledge, bounded action, independent evaluation, and retained human authority can compose into a governed engineering environment, and proposes tests of when that environment turns commodity intelligence into durable engineering progress.
James C. Davis, Kelechi G. Kalu, Huiyun Peng et al.· 1 citation
The complex multi-energy coupling characteristics inherent to integrated energy system (IES) present unprecedented challenges for the implementation of low-carbon scheduling. Existing optimization methods often exhibit limitations in system scalability, algorithm adaptivity, and carbon reduction efficacy for complex IES. This paper proposes a Large Language Model (LLM)-Embedded Multi-Agent Reinforcement Learning (LEMARL) to address the aforementioned issues. The proposed method integrates the global perception capability of LLMs with the dynamic optimization capability of MARL. Specifically, the LLM-Embedded module generates high-quality reward functions and policy frameworks from a global perspective, while the MARL module leverages these LLM-generated strategies for distributed interactive iterations—greatly enhancing computation efficiency and scalability. Simulation results demonstrate that LEMARL reduces carbon emissions by 7.76% and simultaneously decreases operating costs by 4.49% in a small-scale IES. Furthermore, LEMARL also exhibits superior applicability and scalability in large-scale IES of the IEEE 141-bus power grid integrated with 51-node thermal system.
Chen Xia, Tong Gou, Yinliang Xu et al.· IEEE Transactions on Smart G...· 1 citation
TestifAI, a deep learning testing framework for efficient and accurate estimation of robustness against combinations of perturbations, is proposed and partial model tomography is introduced, a novel approach to reconstructing model behaviour in a multi-perturbation space from tests that apply only a small number of perturbations.
Arooj Arif, T. Hartung, E. Botoeva et al.· 1 citation
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduJul 13, 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.