Existing studies have introduced method-based reasoning and scope extension as approaches to enhance Large Language Model (LLM) performance beyond direct matrix mappings. Building on these foundations, this paper summarizes and integrates these ideas into a unified Intuition-Method Layered Model with Scope Extension, designed to address indirected (unseen) issues more systematically. In this framework, intuition-based thinking provides rapid first-reaction answers, while method-based thinking decouples questions and solutions into transferable reasoning units. Scope extension is then applied to broaden applicability, including vertical (cause analysis), horizontal (parallel and generalized issues), and for the first time, temporal and spatial extensions, which expand reasoning across time and contextual dimensions. These extensions are organized into systematic knowledge trees that interconnect into a knowledge network, thereby increasing adaptability. To quantitatively evaluate this process, we propose the entropy of method extension, which measures the independence and diversity of extensions as an indicator of the system's capacity to solve unseen questions. By logically connecting existing approaches with new extensions and introducing an entropy-based evaluation framework, this work advances toward a more robust and extensible reasoning paradigm for LLMs in real-world problem-solving.

In human-AI interactions, explanation is widely seen as necessary for enabling trust in AI systems. We argue that trust, however, may be a pre-requisite because explanation is sometimes impossible. We derive this result from a formalization of explanation as a search process through knowledge networks, where explainers must find paths between shared concepts and the concept to be explained, within finite time. Our model reveals that explanation can fail even under theoretically ideal conditions - when actors are rational, honest, motivated, can communicate perfectly, and possess overlapping knowledge. This is because successful explanation requires not just the existence of shared knowledge but also finding the connection path within time constraints, and it can therefore be rational to cease attempts at explanation before the shared knowledge is discovered. This result has important implications for human-AI interaction: as AI systems, particularly Large Language Models, become more sophisticated and able to generate superficially compelling but spurious explanations, humans may default to trust rather than demand genuine explanations. This creates risks of both misplaced trust and imperfect knowledge integration.

A reliable knowledge structure is a prerequisite for building effective adaptive learning systems and intelligent tutoring systems. Pursuing an explainable and trustworthy knowledge structure, we propose a method for constructing causal knowledge networks. This approach leverages Bayesian networks as a foundation and incorporates causal relationship analysis to derive a causal network. Additionally, we introduce a dependable knowledge-learning path recommendation technique built upon this framework, improving teaching and learning quality while maintaining transparency in the decision-making process.

We used a dictionary built from biomedical terminology extracted from various sources such as DrugBank, MedDRA, MedlinePlus, TCMGeneDIT, to tag more than 8 million Instagram posts by users who have mentioned an epilepsy-relevant drug at least once, between 2010 and early 2016. A random sample of 1,771 posts with 2,947 term matches was evaluated by human annotators to identify false-positives. OpenAI's GPT series models were compared against human annotation. Frequent terms with a high false-positive rate were removed from the dictionary. Analysis of the estimated false-positive rates of the annotated terms revealed 8 ambiguous terms (plus synonyms) used in Instagram posts, which were removed from the original dictionary. To study the effect of removing those terms, we constructed knowledge networks using the refined and the original dictionaries and performed an eigenvector-centrality analysis on both networks. We show that the refined dictionary thus produced leads to a significantly different rank of important terms, as measured by their eigenvector-centrality of the knowledge networks. Furthermore, the most important terms obtained after refinement are of greater medical relevance. In addition, we show that OpenAI's GPT series models fare worse than human annotators in this task.

Concerned with user data privacy, this paper presents a new federated learning (FL) method that trains machine learning models on edge devices without accessing sensitive data. Traditional FL methods, although privacy-protective, fail to manage model heterogeneity and incur high communication costs due to their reliance on aggregation methods. To address this limitation, we propose a resource-aware FL method that aggregates local knowledge from edge models and distills it into robust global knowledge through knowledge distillation. This method allows efficient multi-model knowledge fusion and the deployment of resource-aware models while preserving model heterogeneity. Our method improves communication cost and performance in heterogeneous data and models compared to existing FL algorithms. Notably, it reduces the communication cost of ResNet-32 by up to 50\% and VGG-11 by up to 10$\times$ while delivering superior performance.

Virtual health has been acclaimed as a transformative force in healthcare delivery. Yet, its dropout issue is critical that leads to poor health outcomes, increased health, societal, and economic costs. Timely prediction of patient dropout enables stakeholders to take proactive steps to address patients' concerns, potentially improving retention rates. In virtual health, the information asymmetries inherent in its delivery format, between different stakeholders, and across different healthcare delivery systems hinder the performance of existing predictive methods. To resolve those information asymmetries, we propose a Multimodal Dynamic Knowledge-driven Dropout Prediction (MDKDP) framework that learns implicit and explicit knowledge from doctor-patient dialogues and the dynamic and complex networks of various stakeholders in both online and offline healthcare delivery systems. We evaluate MDKDP by partnering with one of the largest virtual health platforms in China. MDKDP improves the F1-score by 3.26 percentage points relative to the best benchmark. Comprehensive robustness analyses show that integrating stakeholder attributes, knowledge dynamics, and compact bilinear pooling significantly improves the performance. Our work provides significant implications for healthcare IT by revealing the value of mining relations and knowledge across different service modalities. Practically, MDKDP offers a novel design artifact for virtual health platforms in patient dropout management.

Federated Learning (FL) is extensively used to train AI/ML models in distributed and privacy-preserving settings. Participant edge devices in FL systems typically contain non-independent and identically distributed~(Non-IID) private data and unevenly distributed computational resources. Preserving user data privacy while optimizing AI/ML models in a heterogeneous federated network requires us to address data heterogeneity and system/resource heterogeneity. Hence, we propose \underline{R}esource-\underline{a}ware \underline{F}ederated \underline{L}earning~(RaFL) to address these challenges. RaFL allocates resource-aware models to edge devices using Neural Architecture Search~(NAS) and allows heterogeneous model architecture deployment by knowledge extraction and fusion. Integrating NAS into FL enables on-demand customized model deployment for resource-diverse edge devices. Furthermore, we propose a multi-model architecture fusion scheme allowing the aggregation of the distributed learning results. Results demonstrate RaFL's superior resource efficiency compared to SoTA.

Knowledge has been and will continue to be a key competitive differentiator when it comes to driving organizational performance. The power of people and machines working together offers the greatest opportunity for creating knowledge in human history. However, advanced technologies, new ways of working, and shifts in workforce composition are rendering traditional views of knowledge management obsolete. To capitalize on these changes, many organizations need to redefine how they promote knowledge creation to help maximize human potential at work. The Readiness Gap: Seventy-five percent of surveyed organizations say creating and preserving knowledge across evolving workforces is important or very important for their success over the next 12–18 months, but only 9 percent say they are very ready to address this trend; this represents one of the largest gaps between importance and readiness across this year's trends.

The amount of scientific research in AI has been growing exponentially over the last few years, making it challenging for scientists and practitioners to keep track of the progress. Reports suggest that the number of ML papers doubles every 23 months. One of the reasons behind it is that AI is being leveraged in diverse disciplines like mathematics, statistics, physics, medicine, and biochemistry. This poses a unique challenge of organising different ideas and understanding new scientific connections. To this end, a group of researchers led by Mario Krenn and others from the Max Planck Institute for the Science of Light (MPL), Erlangen, Germany, the University of California, the University of Toronto, etc., jointly released a study on high-quality link prediction in an exponentially growing knowledge network.

According to a 2020 report by Emergence, 80% of the global workforce does not sit behind a desk. According to a 2020 report by Emergence, 80% of the global workforce does not sit behind a desk. That’s an overwhelming majority of workers who are deskless and increasingly reliant on technology to do their jobs in industries impacted by factors like the growing skills gap and, most recently, a global pandemic. While employers have done much to address the needs of deskless workers over the past year, there’s untapped opportunity to make these workers – and, in turn, the industries they support – more efficient, resilient, and safe in the current working environment and beyond. On Wednesday, June 23, Rolls Royce’s XXX will join industry experts YYY from PwC and ZZZ from Librestream to teach enterprises about the power of Augmented Reality (AR) and Artificial Intelligence (AI) to enable deskless workers around the world and build knowledge networks capable of sustaining the deskless workforce for decades to come. In this webinar, you will learn: Why traditional deskless worker solutions have fallen short at a time when effective remote collaboration is of peak importance How AR plus AI can improve knowledge sharing among distributed workforces, reduce knowledge loss, eliminate inefficiencies, enhance safety, improve sustainability, lower costs, and more Real-world use cases of AR and AI on devices like Microsoft’s HoloLens and the generated ROI Why organizations with large deskless workforces prefer solutions like Librestream’s AI Connected Expert Vision: Broad device support, specialized accessories, etc. Realizing true IoT: Where AI and AR converge to create the fully connected, deskless worker of tomorrow