Translating Multimodal AI into Real-World Inspection: TEMAI Evaluation Framework and Pathways for Implementation Zehan LI 1,3, Jinzhi Deng 1,2, Haibing Ma 1,2, Chi Zhang 1, and Dan Xiao 1 1 Moximize.ai 2 Shanghai Zhongqiao Vocational And Technical University 3 China Creative Studies Institute April 22, 2025 Abstract This paper introduces the Translational Evaluation of Multimodal AI for Inspection (TEMAI) framework, bridging multimodal AI capabilities with industrial inspection implementation. Adapting translational research principles from healthcare to industrial contexts, TEMAI establishes three core dimensions: Capability (technical feasibility), Adoption (organizational readiness), and Utility (value realization). The framework demonstrates that technical capability alone yields limited value without corresponding adoption mechanisms. TEMAI incorporates specialized metrics including the Value Density Coefficient and structured implementation pathways. Empirical validation through retail and photovoltaic inspection implementations revealed significant differences in value realization patterns despite similar capability reduction rates, confirming the framework's effectiveness across diverse industrial sectors while highlighting the importance of industry-specific adaptation strategies. Keywords: Multimodal AI, Industrial Inspection, Translational Framework, TEMAI 1 Introduction Industrial inspection tasks are fundamental to ensuring operational continuity and safety in manufacturing sectors, serving as a cornerstone for preventive maintenance and risk mitigation. These tasks, however, are plagued by systemic inefficiencies, including labor-intensive workflows, hazardous working environments (e.g., high-temperature zones or toxic gas exposure), and heavy reliance on empirical knowledge that is difficult to standardize or transfer across industries[1]. Despite incremental advancements in automation technologies--such as drones, AR-assisted devices, and IoT-enabled sensors--the integration of these tools into inspection workflows has yielded limited returns due to fragmented deployment, high implementation costs, and insufficient interoperability between hardware and software systems [2]. For instance, while drones have reduced human exposure to dangerous environments in power grid inspections, their operational scope remains constrained by battery life and data processing bottlenecks[3].

Modern society is full of computational challenges that rely on probabilistic reasoning, statistics, and combinatorics. Interestingly, many of these questions can be formulated by encoding them into propositional formulas and then asking for its number of models. With a growing interest in practical problem-solving for tasks that involve model counting, the community established the Model Counting (MC) Competition in fall of 2019 with its first iteration in 2020. The competition aims at advancing applications, identifying challenging benchmarks, fostering new solver development, and enhancing existing solvers for model counting problems and their variants. The first iteration, brought together various researchers, identified challenges, and inspired numerous new applications. In this paper, we present a comprehensive overview of the 2021-2023 iterations of the Model Counting Competition. We detail its execution and outcomes. The competition comprised four tracks, each focusing on a different variant of the model counting problem. The first track centered on the model counting problem (MC), which seeks the count of models for a given propositional formula. The second track challenged developers to submit programs capable of solving the weighted model counting problem (WMC). The third track was dedicated to projected model counting (PMC). Finally, we initiated a track that combined projected and weighted model counting (PWMC). The competition continued with a high level of participation, with seven to nine solvers submitted in various different version and based on quite diverging techniques.

Drones could be used for NHS-related missions in remote areas, inspecting offshore wind turbines and supplying oil rigs by 2026 as part of a new regulatory regime in the UK. David Willetts, the head of a new government unit helping to deploy new technologies in Britain, said there were obvious situations where drones could be used if the changes go ahead next year. Ministers announced plans this month to allow drones to fly long distances without their operators seeing them. Drones cannot be flown "beyond visual line of sight" under current regulations, making their use for lengthy journeys impossible. In an interview with the Guardian, Lord Willetts, chair of the Regulatory Innovation Office (RIO), said the changes could come as soon as 2026, but that they would apply in "atypical" aviation environments at first, which would mean remote areas and over open water. Referring to the NHS, Willetts said there was potentially a huge market for drone operators.

Inspired by the Kolmogorov-Arnold superposition theorem, Kolmogorov-Arnold Networks (KANs) have recently emerged as an improved backbone for most deep learning frameworks, promising more adaptivity than their multilayer perception (MLP) predecessor by allowing for trainable spline-based activation functions. In this paper, we probe the theoretical foundations of the KAN architecture by showing that it can optimally approximate any Besov function in $B^{s}_{p,q}(\mathcal{X})$ on a bounded open, or even fractal, domain $\mathcal{X}$ in $\mathbb{R}^d$ at the optimal approximation rate with respect to any weaker Besov norm $B^{\alpha}_{p,q}(\mathcal{X})$; where $\alpha < s$. We complement our approximation guarantee with a dimension-free estimate on the sample complexity of a residual KAN model when learning a function of Besov regularity from $N$ i.i.d. noiseless samples. Our KAN architecture incorporates contemporary deep learning wisdom by leveraging residual/skip connections between layers.

We propose a scalable, approximate inference hypernetwork framework for a general model of history-dependent processes. The flexible data model is based on a neural ordinary differential equation (NODE) representing the evolution of internal states together with a trainable observation model subcomponent. The posterior distribution corresponding to the data model parameters (weights and biases) follows a stochastic differential equation with a drift term related to the score of the posterior that is learned jointly with the data model parameters. This Langevin sampling approach offers flexibility in balancing the computational budget between the evaluation cost of the data model and the approximation of the posterior density of its parameters. We demonstrate performance of the hypernetwork on chemical reaction and material physics data and compare it to mean-field variational inference.

Phytoplankton is an extremely diverse set of microorganisms, varying in cell morphologies, biogeochemical functions, and physiological responses to environmental disturbances [21]. As the primary producer of ocean's food web, phytoplankton produces approximately 50 percent of Earth's oxygen, regulate the global carbon cycle and climate, and support various ecosystem services, such as fisheries, water quality, and biodiversity [7]. Therefore, knowledge of phytoplankton biomass and their community composition is critical to understanding the food web structure, higher trophic level production (e.g., fisheries), and biological shifts among other complex Earth Science questions, especially in the context of degraded water quality (e.g., eutrophication) and climate change (e.g., warming temperatures), demanding attention at local, regional, and global scales [9]. As such, there is an increasing interdisciplinary interest in studying phytoplankton community dynamics in estuarine-coastal waters, where massive riverine inputs of nutrient-rich freshwaters often lead to eutrophication, harmful algal blooms (HABs), and the annual recurrence of bottom-water hypoxia events, which cause widespread and severe impacts on the aquatic ecosystem [3], [43], [57], [23], [54]. In the field of ocean color remote sensing, the concentration of chlorophyll a (Chl-a) and phytoplankton absorption properties (aphy) are two of the most commonly used metrics for assessing phytoplankton abundance and diversity in aquatic environments [41], [56]. Those phytoplankton-related satellite algorithms are rooted in the physical principle that remote sensing reflectance (Rrs, sr 1)), the ratio of water-leaving radiance to the total downwelling irradiance just above water, is determined by the inherent optical properties (IOPs), most importantly the total backscattering coefficient (btotal, m 1) and the total absorption coefficient (atotal, m 1) [38].

Predicting the remaining useful life (RUL) of rotating machinery is critical for industrial safety and maintenance, but existing methods struggle with scarce target-domain data and unclear degradation dynamics. We propose a Meta-Learning and Knowledge Discovery-based Physics-Informed Neural Network (MKDPINN) to address these challenges. The method first maps noisy sensor data to a low-dimensional hidden state space via a Hidden State Mapper (HSM). A Physics-Guided Regulator (PGR) then learns unknown nonlinear PDEs governing degradation evolution, embedding these physical constraints into the PINN framework. This integrates data-driven and physics-based approaches. The framework uses meta-learning, optimizing across source-domain meta-tasks to enable few-shot adaptation to new target tasks. Experiments on industrial data and the C-MAPSS benchmark show MKDPINN outperforms baselines in generalization and accuracy, proving its effectiveness for RUL prediction under data scarcity

Telecom services are at the core of today's societies' everyday needs. The availability of numerous online forums and discussion platforms enables telecom providers to improve their services by exploring the views of their customers to learn about common issues that the customers face. Natural Language Processing (NLP) tools can be used to process the free text collected. One way of working with such data is to represent text as numerical vectors using one of many word embedding models based on neural networks. This research uses a novel dataset of telecom customers' reviews to perform an extensive study showing how different word embedding algorithms can affect the text classification process. Several state-of-the-art word embedding techniques are considered, including BERT, Word2Vec and Doc2Vec, coupled with several classification algorithms. The important issue of feature engineering and dimensionality reduction is addressed and several PCA-based approaches are explored. Moreover, the energy consumption used by the different word embeddings is investigated. The findings show that some word embedding models can lead to consistently better text classifiers in terms of precision, recall and F1-Score. In particular, for the more challenging classification tasks, BERT combined with PCA stood out with the highest performance metrics. Moreover, our proposed PCA approach of combining word vectors using the first principal component shows clear advantages in performance over the traditional approach of taking the average.

In the era of large-scale model training, the extensive use o f available datasets has resulted in significant computation al inefficiencies. T o tackle this issue, we explore methods for identifying informative subsets of training data that can achieve comparable or even superior model performance. W e propose a technique based on influence functions to determine which training samples should be included in the training set. W e conducted empirical evaluations of our method on binary classification tasks utilizing logistic re - gression models. Our approach demonstrates performance comparable to that of training on the entire dataset while using only 10% of the data. Furthermore, we found that our method achieved even higher accuracy when trained with just 60% of the data.

Non-Intrusive Load Monitoring (NILM) has emerged as a key smart grid technology, identifying electrical device and providing detailed energy consumption data for precise demand response management. Nevertheless, NILM data suffers from missing values due to inescapable factors like sensor failure, leading to inaccuracies in non-intrusive load monitoring. A stochastic gradient descent (SGD)-based latent factorization of tensors model has proven to be effective in estimating missing data, however, it updates a latent factor solely based on the current stochastic gradient, without considering past information, which leads to slow convergence of anLFT model. To address this issue, this paper proposes a Nonlinear Proportional-integral-derivative (PID)-Incorporated Latent factorization of tensors (NPIL) model with two-fold ideas: a) rebuilding the instant learning error according to the principle of a nonlinear PID controller, thus, the past update information is efficiently incorporated into the learning scheme, and b) implementing gain parameter adaptation by utilizing particle swarm optimization (PSO) algorithm, hence, the model computational efficiency is effectively improved. Experimental results on real-world NILM datasets demonstrate that the proposed NPIL model surpasses state-of-the-art models in convergence rate and accuracy when predicting the missing NILM data.