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Generative AI is transforming software development from localized tool support into development work that is embedded in processes, tools, and organizational structures. Its use now extends beyond code completion to requirements, architecture, implementation, testing, review, operations, and maintenance. Existing research shows a differentiated picture. Productivity gains are possible, but depend on task type, codebase characteristics, and developers' experience. At the same time, AI-generated artifacts require additional control and governance. Building on these observations, this paper develops a pragmatic organizing framework for the transition toward AI-driven Software Development. It describes a progression from informal and assistive AI use through integrated AI workflows toward controlled agentic development processes. The focus is not on individual tools or models, but on the technical, organizational, and quality-assurance mechanisms needed to embed AI across central software engineering activities. Particular importance is assigned to a harness that connects project context, tool access, verification, permissions, logging, and human approval. The paper draws on current re

Motivated by forthcoming high-energy experiments at Jefferson Lab and the Electron-Ion Collider, this dissertation develops a novel relativistic formulation of nuclear structure. While previous scattering models were updated to include nucleon-nucleon short-range correlations (SRCs) to explain cross-section plateaus, modern high-kinematics experiments require a relativistic approach. We reformulate conventional tools into a light-front-quantized framework, utilizing density functional theory and similarity renormalization group techniques. Our calculations successfully reproduce nuclear binding energies, shell structure, and SRC physics. However, we show that a purely nucleonic description fails to fully capture inclusive electron-nucleus data or the plateaus at high Bjorken-$x_B$. This demonstrates the critical importance of inelastic final-state interactions currently omitted by standard SRC phenomenology.

Artificial intelligences (AIs) are increasingly capable of emotionally engaging with humans to the point of forming intimate relationships. Yet, current studies on romantic love toward AI lack statistically validated instruments to measure romantic love toward AI, hindering empirical research. To address this gap, we reinterpreted Lee's love styles theory in the AI context and developed the Love Attitudes Scale toward AI (LAS-AI). The resulting 24-item, six-factor scale was validated across four phases using three independent samples (N = 899), demonstrating strong psychometric properties. The findings further revealed that people primarily seek practical, passionate, and companionship-based relationships with AI (i.e., Pragma, Eros, and Storge), showing little interest in a playful or noncommittal approach (i.e., Ludus). We also provided an initial exploration of the similarities and differences between romantic love with humans and AI. The LAS-AI offers a robust tool for future research on human-AI romantic relationships, with prolific implications.

We study how a one-layer attention-only transformer develops relevant structures while learning to sort lists of numbers. At the end of training, the model organizes its attention heads in two main modes that we refer to as vocabulary-splitting and copy-suppression. Both represent simpler modes than having multiple heads handle overlapping ranges of numbers. Interestingly, vocabulary-splitting is present regardless of whether we use weight decay, a common regularization technique thought to drive simplification, supporting the thesis that neural networks naturally prefer simpler solutions. We relate copy-suppression to a mechanism in GPT-2 and investigate its functional role in our model. Guided by insights from a developmental analysis of the model, we identify features in the training data that drive the model's final acquired solution. This provides a concrete example of how the training data shape the internal organization of transformers, paving the way for future studies that could help us better understand how LLMs develop their internal structures.

This paper examines how institutional belonging shapes long-term development by comparing Spain and Uruguay, two small democracies with similar historical endowments whose trajectories diverged sharply after the 1960s. While Spain integrated into dense European institutional architectures, Uruguay remained embedded within the Latin American governance regime, characterized by weaker coordination and lower institutional coherence. To assess how alternative institutional embeddings could have altered these paths, the study develops a generative counterfactual framework grounded in economic complexity, institutional path dependence, and a Wasserstein GAN trained on data from 1960-2020. The resulting Expected Developmental Shift (EDS) quantifies structural gains or losses from hypothetical re-embedding in different institutional ecosystems. Counterfactual simulations indicate that Spain would have experienced significant developmental decline under a Latin American configuration, while Uruguay would have achieved higher complexity and resilience within a European regime. These findings suggest that development is not solely determined by domestic reforms but emerges from a country's st

Throughout mathematics there are constructions where an object is obtained as a limit of an infinite sequence. Typically, the objects in the sequence improve as the sequence progresses, and the ideal is reached at the limit. I introduce a view that understands this as a development process by which a dynamic mathematical object develops teleologically. In particular, this paper elaborates and clarifies the intuition that such constructions operate on a single dynamic object that maintains its identity throughout the process, and that each step consists in a transformation of this dynamic object, rather than in a genesis of an entirely new static object. This view is supported by a general philosophical discussion, and by a formal modal first-order framework of development processes. In order to exhibit the ubiquity of such processes in mathematics, and showcase the advantages of this view, the framework is applied to wide range of examples: The set of real numbers, forcing extensions of models of set theory, non-standard numbers of arithmetic, the reflection theorem schema of set theory, and the revision semantics of truth. Thus, the view proposed promises to yield a unified dynami

Our GBDT (generalised Bäcklund-Darboux transformation) approach is used to construct explicit solutions of the focusing nonlinear Schrödinger (NLS) equation in the case of the exponential seed $a \exp\{2 i (cx +dt)\}$. The corresponding Baker-Akhiezer functions and evolution of the Weyl functions are obtained as well. In particular, the solutions, which appear in the study of rogue waves, step-like solutions and $N$-modulation solutions of the NLS equation are considered. This work is an essential development of our joint work with Rien Kaashoek and Israel Gohberg, where the seed was trivial, as well as several other of our previous works.

AcademiaOS is a first attempt to automate grounded theory development in qualitative research with large language models. Using recent large language models' language understanding, generation, and reasoning capabilities, AcademiaOS codes curated qualitative raw data such as interview transcripts and develops themes and dimensions to further develop a grounded theoretical model, affording novel insights. A user study (n=19) suggests that the system finds acceptance in the academic community and exhibits the potential to augment humans in qualitative research. AcademiaOS has been made open-source for others to build upon and adapt to their use cases.

As Large Language Models (LLMs), including ChatGPT and analogous systems, continue to advance, their robust natural language processing capabilities and diverse applications have garnered considerable attention. Nonetheless, despite the increasing acknowledgment of the convergence of Artificial Intelligence (AI) and Software Engineering (SE), there is a lack of studies involving the impact of this convergence on the practices and perceptions of software developers. Understanding how software developers perceive and engage with AI tools, such as ChatGPT, is essential for elucidating the impact and potential challenges of incorporating AI-driven tools in the software development process. In this paper, we conducted a survey with 207 software developers to understand the impact of ChatGPT on software quality, productivity, and job satisfaction. Furthermore, the study delves into developers' expectations regarding future adaptations of ChatGPT, concerns about potential job displacement, and perspectives on regulatory interventions.

Moldable development supports decision-making by making software systems explainable. This is done by making it cheap to add numerous custom tools to your software, turning it into a live, explorable domain model. Based on several years of experience of applying moldable development to both open-source and industrial systems, we have identified several mutually supporting patterns to explain how moldable development works in practice. This paper targets (i) readers curious to learn about moldable development, (ii) current users of the Glamorous Toolkit moldable IDE wanting to learn best practices, and (iii) developers interested in applying moldable development using other platforms and technology.

Despite decades of research, developing correct and scalable concurrent programs is still challenging. Network functions (NFs) are not an exception. This paper presents NFork, a system that helps NF domain experts to productively develop concurrent NFs by abstracting away concurrency from developers. The key scheme behind NFork's design is to exploit NF characteristics to overcome the limitations of prior work on concurrency programming. Developers write NFs as sequential programs, and during runtime, NFork performs transparent parallelization by processing packets in different cores. Exploiting NF characteristics, NFork leverages transactional memory and develops efficient concurrent data structures to achieve scalability and guarantee the absence of concurrency bugs. Since NFork manages concurrency, it further provides (i) a profiler that reveals the root causes of scalability bottlenecks inherent to the NF's semantics and (ii) actionable recipes for developers to mitigate these root causes by relaxing the NF's semantics. We show that NFs developed with NFork achieve competitive scalability with those in Cisco VPP [16], and NFork's profiler and recipes can effectively aid develop

This paper motivates and develops a framework for understanding how the socio-technical systems surrounding AI development interact with social welfare. It introduces the concept of ``signaling'' from evolutionary game theory and demonstrates how it can enhance existing theory and practice surrounding the evaluation and governance of AI systems.

Software applications play an increasingly critical role in various aspects of our lives, from communication and entertainment to business and healthcare. As these applications become more pervasive, the importance of considering human values in software development has gained significant attention. In this preliminary study, we investigate developers's perceptions and experiences related to human values, with a focus on the human value of transparency. We interviewed five experienced developers and conducted thematic analysis to explore how developers perceive transparency, violations of transparency, and the process of fixing reported violations of transparency. Our findings reveal the significance of transparency as a fundamental value in software development, with developers recognising its importance for building trust, promoting accountability, and fostering ethical practices. Developers recognise the negative consequences of the violation of the human value of transparency and follow a systematic process to fix reported violations. This includes investigation, root cause analysis, corrective action planning, collaborative problem-solving, and testing and verification. These

This study examines the relationship between globalization and income inequality, utilizing panel data spanning from 1992 to 2020. Globalization is measured by the World Bank global-link indicators such as FDI, Remittance, Trade Openness, and Migration while income inequality is measured by Gini Coefficient and the median income of 50% of the population. The fixed effect panel data analysis provides empirical evidence indicating that globalization tends to reduce income inequality, though its impact varies between developed and developing countries. The analysis reveals a strong negative correlation between net foreign direct investment (FDI) inflows and inequality in developing countries, while no such relationship was found for developed countries.The relationship holds even if we consider an alternative measure of inequality. However, when dividing countries by developed and developing groups, no statistically significant relationship was observed. Policymakers can use these findings to support efforts to increase FDI, trade, tourism, and migration to promote growth and reduce income inequality.

We present an overview of the design and first proof-of-concept implementation for AIDA, an autonomous intelligent developer agent that develops software from scratch. AIDA takes a software requirements specification and uses reasoning over a semantic knowledge graph to interpret the requirements, then designs and writes software to satisfy them. AIDA uses both declarative and procedural knowledge in the core domains of data, algorithms, and code, plus some general knowledge. The reasoning codebase uses this knowledge to identify needed components, then designs and builds the necessary information structures around them that become the software. These structures, the motivating requirements, and the resulting source code itself are all new knowledge that are added to the knowledge graph, becoming available for future reasoning. In this way, AIDA also learns as she writes code and becomes more efficient when writing subsequent code.

Humans can make predictions on various time scales and hierarchical levels. Thereby, the learning of event encodings seems to play a crucial role. In this work we model the development of hierarchical predictions via autonomously learned latent event codes. We present a hierarchical recurrent neural network architecture, whose inductive learning biases foster the development of sparsely changing latent state that compress sensorimotor sequences. A higher level network learns to predict the situations in which the latent states tend to change. Using a simulated robotic manipulator, we demonstrate that the system (i) learns latent states that accurately reflect the event structure of the data, (ii) develops meaningful temporal abstract predictions on the higher level, and (iii) generates goal-anticipatory behavior similar to gaze behavior found in eye-tracking studies with infants. The architecture offers a step towards the autonomous learning of compressed hierarchical encodings of gathered experiences and the exploitation of these encodings to generate adaptive behavior.

We present a macro-scale description of quasi-periodically developed flow in channels, which relies on double volume-averaging. We show that quasi-developed macro-scale flow is characterized by velocity modes which decay exponentially in the main flow direction. We prove that the closure force can be represented by an exact permeability tensor consisting of two parts. The first part, which is due to the developed macro-scale flow, is uniform everywhere, except in the side-wall region, where it is affected by the macro-scale velocity profile and its slip length. The second part expresses the resistance against the velocity mode, so it decays exponentially as the flow develops. It satisfies a specific closure problem on a transversal row of the array. From these properties, we assess the validity of the classical closure problem for the volume-averaged flow equations. We show that all its underlying assumptions are partly violated by an exponentially vanishing error during flow development. Furthermore, we show that it modifies the eigenvalues, modes, and onset point of quasi-developed flow, when it is applied to reconstruct the macro-scale flow. The former theoretical aspects are il

The laser induced plasma in liquid hasn't been studied enough. In liquid, the laser induced plasma may be able to resolve the hazardous material called the environment material. Then, the plasma produced in liquid by the laser light is studied and the plasma development is observed by a streak camera. The ultra pure water or the ultra pure water with a melted NaCl is used as a test liquid. The liquid plasma is produced by the fundamental wave of YAG laser. When NaCl concentration is varied, the plasma development behavior is obserbed by streak camera. The liquid plasma develops backward. The plasma is produced from many seeds and It consists of a group of plasmas. However, the liquid plasma produced by second harmonic wave of YAG laser develops as a single plasma. The development mechanism is investigated from the growth rate of backward plasma. The backward plasma develops by breakdown wave and radiation supported shock wave.