Large language models are increasingly becoming a cornerstone technology in artificial intelligence, the sciences, and society as a whole, yet the optimal strategies for dataset composition and filtering remain largely elusive. Many of the top-performing models lack transparency in their dataset curation and model development processes, posing an obstacle to the development of fully open language models. In this paper, we identify three core data-related challenges that must be addressed to advance open-source language models. These include (1) transparency in model development, including the data curation process, (2) access to large quantities of high-quality data, and (3) availability of artifacts and metadata for dataset curation and analysis. To address these challenges, we release RedPajama-V1, an open reproduction of the LLaMA training dataset. In addition, we release RedPajama-V2, a massive web-only dataset consisting of raw, unfiltered text data together with quality signals and metadata. Together, the RedPajama datasets comprise over 100 trillion tokens spanning multiple domains and with their quality signals facilitate the filtering of data, aiming to inspire the development of numerous new datasets. To date, these datasets have already been used in the training of strong language models used in production, such as Snowflake Arctic, Salesforce's XGen and AI2's OLMo. To provide insight into the quality of RedPajama, we present a series of analyses and ablation studies with decoder-only language models with up to 1.6B parameters. Our findings demonstrate how quality signals for web data can be effectively leveraged to curate high-quality subsets of the dataset, underscoring the potential of RedPajama to advance the development of transparent and high-performing language models at scale.

This chapter explores the symbiotic relationship between Artificial Intelligence (AI) and trust in networked systems, focusing on how these two elements reinforce each other in strategic cybersecurity contexts. Using a game-theoretic framework, this chapter presents approaches to trust evaluation, the strategic role of AI in cybersecurity, and governance frameworks that ensure responsible AI deployment. We investigate how trust, when dynamically managed through AI, can form a resilient security ecosystem. By examining trust as both an AI output and an AI requirement, this chapter sets the foundation for a positive feedback loop where AI enhances network security and the trust placed in AI systems fosters their adoption. The rapid development of network systems has been a catalyst for innovations such as 5G communications, edge computing, and network slicing [6], driving the transformation of Industry 4.0 [18] and introducing new services for critical infrastructures.

Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hyperparameter selection in the analysis workflow. In this study, we explore the effects of descriptors and hyperparameters on the capability of unsupervised ML methods to distill local structural information, exemplified by discovery of polarization and lattice distortion in Sm doped BiFeO3 (BFO) thin films. We demonstrate that a reward-driven approach can be used to optimize these key hyperparameters across the full workflow, where rewards were designed to reflect domain wall continuity and straightness, ensuring that the analysis aligns with the material's physical behavior. This approach allows us to discover local descriptors that are best aligned with the specific physical behavior, providing insight into the fundamental physics of materials. We further extend the reward driven workflows to disentangle structural factors of variation via optimized variational autoencoder (VAE). Finally, the importance of well-defined rewards was explored as a quantifiable measure of success of the workflow.

This paper introduces a \textit{Process-Guided Learning (Pril)} framework that integrates physical models with recurrent neural networks (RNNs) to enhance the prediction of dissolved oxygen (DO) concentrations in lakes, which is crucial for sustaining water quality and ecosystem health. Unlike traditional RNNs, which may deliver high accuracy but often lack physical consistency and broad applicability, the \textit{Pril} method incorporates differential DO equations for each lake layer, modeling it as a first-order linear solution using a forward Euler scheme with a daily timestep. However, this method is sensitive to numerical instabilities. When drastic fluctuations occur, the numerical integration is neither mass-conservative nor stable. Especially during stratified conditions, exogenous fluxes into each layer cause significant within-day changes in DO concentrations. To address this challenge, we further propose an \textit{Adaptive Process-Guided Learning (April)} model, which dynamically adjusts timesteps from daily to sub-daily intervals with the aim of mitigating the discrepancies caused by variations in entrainment fluxes. \textit{April} uses a generator-discriminator architecture to identify days with significant DO fluctuations and employs a multi-step Euler scheme with sub-daily timesteps to effectively manage these variations. We have tested our methods on a wide range of lakes in the Midwestern USA, and demonstrated robust capability in predicting DO concentrations even with limited training data. While primarily focused on aquatic ecosystems, this approach is broadly applicable to diverse scientific and engineering disciplines that utilize process-based models, such as power engineering, climate science, and biomedicine.

--Efficient path optimization for drones in search and rescue operations faces challenges, including limited visibility, time constraints, and complex information gathering in urban environments. We present a comprehensive approach to optimize UA V-based search and rescue operations in neighborhood areas, utilizing both a 3D AirSim-ROS2 simulator and a 2D simulator . The path planning problem is formulated as a partially observable Markov decision process (POMDP), and we propose a novel "Shrinking POMCP" approach to address time constraints. In the AirSim environment, we integrate our approach with a probabilistic world model for belief maintenance and a neu-rosymbolic navigator for obstacle avoidance. The 2D simulator employs surrogate ROS2 nodes with equivalent functionality. We compare trajectories generated by different approaches in the 2D simulator and evaluate performance across various belief types in the 3D AirSim-ROS simulator . Experimental results from both simulators demonstrate that our proposed shrinking POMCP solution achieves significant improvements in search times compared to alternative methods, showcasing its potential for enhancing the efficiency of UA V-assisted search and rescue operations. Search and rescue (SAR) operations are critical, time-sensitive missions conducted in challenging environments like neighborhoods, wilderness [1], or maritime settings [2]. These resource-intensive operations require efficient path planning and optimal routing [3]. In recent years, Unmanned Aerial V ehicles (UA Vs) have become valuable SAR assets, offering advantages such as rapid deployment, extended flight times, and access to hard-to-reach areas. Equipped with sensors and cameras, UA Vs can detect heat signatures, identify objects, and provide real-time aerial imagery to search teams [4]. However, the use of UA Vs in SAR operations presents unique challenges, particularly in path planning and decision-making under uncertainty. Factors such as limited battery life, changing weather conditions, and incomplete information about the search area complicate the task of efficiently coordinating UA V movements to maximize the probability of locating targets [3].

Transcatheter tricuspid valve replacement (TTVR) is the latest treatment for tricuspid regurgitation and is in the early stages of clinical adoption. Intelligent robotic approaches are expected to overcome the challenges of surgical manipulation and widespread dissemination, but systems and protocols with high clinical utility have not yet been reported. In this study, we propose a complete solution that includes a passive stabilizer, robotic drive, detachable delivery catheter and valve manipulation mechanism. Working towards autonomy, a hybrid augmented intelligence approach based on reinforcement learning, Monte Carlo probabilistic maps and human-robot co-piloted control was introduced. Systematic tests in phantom and first-in-vivo animal experiments were performed to verify that the system design met the clinical requirement. Furthermore, the experimental results confirmed the advantages of co-piloted control over conventional master-slave control in terms of time efficiency, control efficiency, autonomy and stability of operation. In conclusion, this study provides a comprehensive pathway for robotic TTVR and, to our knowledge, completes the first animal study that not only successfully demonstrates the application of hybrid enhanced intelligence in interventional robotics, but also provides a solution with high application value for a cutting-edge procedure.

Neural Machine Translation systems are used in diverse applications due to their impressive performance. However, recent studies have shown that these systems are vulnerable to carefully crafted small perturbations to their inputs, known as adversarial attacks. In this paper, we propose a new type of adversarial attack against NMT models. In this attack, we find a word to be added between two sentences such that the second sentence is ignored and not translated by the NMT model. The word added between the two sentences is such that the whole adversarial text is natural in the source language. This type of attack can be harmful in practical scenarios since the attacker can hide malicious information in the automatic translation made by the target NMT model. Our experiments show that different NMT models and translation tasks are vulnerable to this type of attack. Our attack can successfully force the NMT models to ignore the second part of the input in the translation for more than 50% of all cases while being able to maintain low perplexity for the whole input.

This p aper explore s the rapidly evolving ecosystem of publicly available AI models, and their potential implications on the s ecurit y and s afet y lands cape. A s AI models become increasingly prevalent, understanding their potential risks and vulnerabilitie s is crucial. We review the current s ecurit y and s afet y s cenarios while highlighting challenge s such as tracking issue s, remediation, and the app arent abs ence of AI model lifecycle and ownership proce ss e s. Comprehensive strategie s to enhance s ecurit y and s afet y for both model developers and end-us ers are propos ed. This p aper aims to provide s ome of the foundational piece s for more standardized s ecurit y, s afet y, and transp arency in the development and operation of AI models and the larger open ecosystems and communitie s forming around them. Generative AI, a branch of artificial intelligence focus ed on AI produc tion of content such as text, image s and video, has s een significant advancement s since the introduc tion of generative advers arial net works (GANs) in 2014 (Goodfellow et al., 2014), which improved data generation but faced issue s like training instabilit y. The development of transformers and s elf at tention mechanisms in 2017 (Vaswani et al., 2017) facilitated further improvement s in natural language proce ssing, leading to large language models (LLMs) like GPT (Radford et al., 2018) with highly advanced text generation cap abilitie s. Dif fusion models (S ohl-Dickstein et al., 2015) have als o s een rapid advancement in image and video generation. This rapid advancement in technology cap abilit y has been matched by an equally rapid uptake in adoption. A s with any new technology, it is worth noting that the industr y is still identif ying new and valuable us e s for AI and the s e market predic tions may fluc tuate as us e cas e s are te sted in real world environment s with real world problems. For the purpos e of clarit y we shall be using the term public model, for a model which is publicly available for download and us e. LLMs are the next evolution of data s cience, a field focus ed on math and data. Unlike traditional systems and applications which rely on logic and programming for a specified outcome, large language model development t ypically consist s of architec ture re s earch and de sign, which is then coded.

Federated Learning (FL) is a decentralized learning approach that protects sensitive information by utilizing local model parameters rather than sharing clients' raw datasets. While this privacy-preserving method is widely employed across various applications, it still requires significant development and optimization. Automated Machine Learning (Auto-ML) has been adapted for reducing the need for manual adjustments. Previous studies have explored the integration of AutoML with different FL algorithms to evaluate their effectiveness in enhancing FL settings. However, Automated FL (Auto-FL) faces additional challenges due to the involvement of a large cohort of clients and global training rounds between clients and the server, rendering the tuning process time-consuming and nearly impossible on resource-constrained edge devices (e.g., IoT devices). This paper investigates the deployment and integration of two lightweight Hyper-Parameter Optimization (HPO) tools, Raytune and Optuna, within the context of FL settings. A step-wise feedback mechanism has also been designed to accelerate the hyper-parameter tuning process and coordinate AutoML toolkits with the FL server. To this end, both local and global feedback mechanisms are integrated to limit the search space and expedite the HPO process. Further, a novel client selection technique is introduced to mitigate the straggler effect in Auto-FL. The selected hyper-parameter tuning tools are evaluated using two benchmark datasets, FEMNIST, and CIFAR10. Further, the paper discusses the essential properties of successful HPO tools, the integration mechanism with the FL pipeline, and the challenges posed by the distributed and heterogeneous nature of FL environments.

This paper provides a comprehensive survey of the latest research on multilingual large language models (MLLMs). MLLMs not only are able to understand and generate language across linguistic boundaries, but also represent an important advancement in artificial intelligence. We first discuss the architecture and pre-training objectives of MLLMs, highlighting the key components and methodologies that contribute to their multilingual capabilities. We then discuss the construction of multilingual pre-training and alignment datasets, underscoring the importance of data quality and diversity in enhancing MLLM performance. An important focus of this survey is on the evaluation of MLLMs. We present a detailed taxonomy and roadmap covering the assessment of MLLMs' cross-lingual knowledge, reasoning, alignment with human values, safety, interpretability and specialized applications. Specifically, we extensively discuss multilingual evaluation benchmarks and datasets, and explore the use of LLMs themselves as multilingual evaluators. To enhance MLLMs from black to white boxes, we also address the interpretability of multilingual capabilities, cross-lingual transfer and language bias within these models. Finally, we provide a comprehensive review of real-world applications of MLLMs across diverse domains, including biology, medicine, computer science, mathematics and law. We showcase how these models have driven innovation and improvements in these specialized fields while also highlighting the challenges and opportunities in deploying MLLMs within diverse language communities and application scenarios. We listed the paper related in this survey and publicly available at https://github.com/tjunlp-lab/Awesome-Multilingual-LLMs-Papers.