Abstract
Artificial Intelligence (AI) has emerged as both a continuation of historical technological revolutions and a potential rupture with them. This paper argues that AI must be viewed simultaneously through three lenses: \textit{risk}, where it resembles nuclear technology in its irreversible and global externalities; \textit{transformation}, where it parallels the Industrial Revolution as a general-purpose technology driving productivity and reorganization of labor; and \textit{continuity}, where it extends the fifty-year arc of computing revolutions from personal computing to the internet to mobile. Drawing on historical analogies, we emphasize that no past transition constituted a strict singularity: disruptive shifts eventually became governable through new norms and institutions. We examine recurring patterns across revolutions -- democratization at the usage layer, concentration at the production layer, falling costs, and deepening personalization -- and show how these dynamics are intensifying in the AI era. Sectoral analysis illustrates how accounting, law, education, translation, advertising, and software engineering are being reshaped as routine cognition is commoditized and human value shifts to judgment, trust, and ethical responsibility. At the frontier, the challenge of designing moral AI agents highlights the need for robust guardrails, mechanisms for moral generalization, and governance of emergent multi-agent dynamics. We conclude that AI is neither a singular break nor merely incremental progress. It is both evolutionary and revolutionary: predictable in its median effects yet carrying singularity-class tail risks. Good outcomes are not automatic; they require coupling pro-innovation strategies with safety governance, ensuring equitable access, and embedding AI within a human order of responsibility.

AI Insights

• AI is reshaping law, education, translation, and software engineering by commodifying routine reasoning and shifting scarcity to judgment, trust, and ethical responsibility.
• Historical analogies show past tech revolutions became governable through new norms, standards, and institutions, dispelling the singularity myth.
• Moral AI demands interdisciplinary collaboration to engineer reliability, articulate values, and build accountability regimes for emergent multi‑agent systems.
• Viewing AI as mathematics and infrastructure—not magic—helps embed it in a human order of responsibility, balancing benefits and risks.
• Beniger’s “The Control Revolution” traces how information societies reorganize economies, offering a useful lens for AI’s systemic effects.

Abstract
AI reasoning agents are already able to solve a variety of tasks by deploying tools, simulating outcomes of multiple hypotheses and reflecting on them. In doing so, they perform computation, although not in the classical sense -- there is no program being executed. Still, if they perform computation, can AI agents be universal? Can chain-of-thought reasoning solve any computable task? How does an AI Agent learn to reason? Is it a matter of model size? Or training dataset size? In this work, we reinterpret the role of learning in the context of AI Agents, viewing them as compute-capable stochastic dynamical systems, and highlight the role of time in a foundational principle for learning to reason. In doing so, we propose a shift from classical inductive learning to transductive learning -- where the objective is not to approximate the distribution of past data, but to capture their algorithmic structure to reduce the time needed to find solutions to new tasks. Transductive learning suggests that, counter to Shannon's theory, a key role of information in learning is about reduction of time rather than reconstruction error. In particular, we show that the optimal speed-up that a universal solver can achieve using past data is tightly related to their algorithmic information. Using this, we show a theoretical derivation for the observed power-law scaling of inference time versus training time. We then show that scaling model size can lead to behaviors that, while improving accuracy on benchmarks, fail any reasonable test of intelligence, let alone super-intelligence: In the limit of infinite space and time, large models can behave as savants, able to brute-force through any task without any insight. Instead, we argue that the key quantity to optimize when scaling reasoning models is time, whose critical role in learning has so far only been indirectly considered.

AI Insights

• The paper proposes a framework to dissect in‑context learning, linking generalization to pattern inference.
• It shows bias and overfitting limit in‑context learning, calling for bias‑mitigation research.
• Algorithmic information theory predicts inference‑time speed‑ups from past data’s Kolmogorov complexity.
• Scaling laws by Kaplan et al. and nearest‑neighbor models by Khandelwal et al. ground empirical claims.
• Infinite‑size models risk becoming savants, brute‑forcing solutions without insight.
• Recommended reading: Kahneman’s Thinking, Fast and Slow and Li & Vitányi’s Kolmogorov Complexity primer.
• Future work should operationalize transductive learning to reduce time, not just error.

Abstract
Current decarbonization efforts are falling short of meeting the net-zero greenhouse gas (GHG) emission target, highlighting the need for substantial carbon dioxide removal methods such as direct air capture (DAC). However, integrating DACs poses challenges due to their enormous power consumption. This study assesses the commercial operation of various DAC technologies that earn revenue using monetized carbon incentives while purchasing electricity from wholesale power markets. We model four commercial DAC technologies and examine their operation in three representative locations including California, Texas, and New York. Our findings reveal that commercial DAC operations can take financial advantage of the volatile power market to operate only during low-price periods strategically, offering a pathway to facilitate a cost-efficient decarbonization transition. The ambient operational environment such as temperature and relative humidity has non-trivial impact on abatement capacity. Profit-driven decisions introduce climate-economic trade-offs that might decrease the capacity factor of DAC and reduce total CO2 removal. These implications extend throughout the entire lifecycle of DAC developments and influence power systems and policies related to full-scale DAC implementation. Our study shows that DAC technologies with shorter cycle spans and higher flexibility can better exploit the electricity price volatility, while power markets demonstrate persistent low-price windows that often synergize with low grid emission periods, like during the solar "duck curve" in California. An optimal incentive design exists for profit-driven operations while carbon-tax policy in electricity pricing is counterproductive for DAC systems.

AI Insights

• Herzog et al.’s Getting Real About Capturing Carbon from the Air (2024) gives a practical DAC scaling roadmap.
• Breyer et al. (2020) show renewable‑electricity‑driven DAC can deliver net‑negative emissions.
• Deutz & Bardow (2021) LCA shows DAC benefits depend on electricity source and sorbent chemistry.
• DAC can co‑locate with cement, power, and natural‑gas plants, offering CO₂ removal and heat synergies.
• High capital costs, energy intensity, and scalability are the main barriers to commercial DAC.
• IEA’s World Energy Outlook 2022 projects DAC could supply up to 10% of global CO₂ removal by 2050 under strong policies.
• World Bank Development Indicators help assess socioeconomic impacts of large‑scale DAC in emerging economies.

Abstract
General-purpose compilers abstract away parallelism, locality, and synchronization, limiting their effectiveness on modern spatial architectures. As modern computing architectures increasingly rely on fine-grained control over data movement, execution order, and compute placement for performance, compiler infrastructure must provide explicit mechanisms for orchestrating compute and data to fully exploit such architectures. We introduce MLIR-AIR, a novel, open-source compiler stack built on MLIR that bridges the semantic gap between high-level workloads and fine-grained spatial architectures such as AMD's NPUs. MLIR-AIR defines the AIR dialect, which provides structured representations for asynchronous and hierarchical operations across compute and memory resources. AIR primitives allow the compiler to orchestrate spatial scheduling, distribute computation across hardware regions, and overlap communication with computation without relying on ad hoc runtime coordination or manual scheduling. We demonstrate MLIR-AIR's capabilities through two case studies: matrix multiplication and the multi-head attention block from the LLaMA 2 model. For matrix multiplication, MLIR-AIR achieves up to 78.7% compute efficiency and generates implementations with performance almost identical to state-of-the-art, hand-optimized matrix multiplication written using the lower-level, close-to-metal MLIR-AIE framework. For multi-head attention, we demonstrate that the AIR interface supports fused implementations using approximately 150 lines of code, enabling tractable expression of complex workloads with efficient mapping to spatial hardware. MLIR-AIR transforms high-level structured control flow into spatial programs that efficiently utilize the compute fabric and memory hierarchy of an NPU, leveraging asynchronous execution, tiling, and communication overlap through compiler-managed scheduling.

Abstract
Social and behavioral scientists increasingly aim to study how humans interact, collaborate, and make decisions alongside artificial intelligence. However, the experimental infrastructure for such work remains underdeveloped: (1) few platforms support real-time, multi-party studies at scale; (2) most deployments require bespoke engineering, limiting replicability and accessibility, and (3) existing tools do not treat AI agents as first-class participants. We present Deliberate Lab, an open-source platform for large-scale, real-time behavioral experiments that supports both human participants and large language model (LLM)-based agents. We report on a 12-month public deployment of the platform (N=88 experimenters, N=9195 experiment participants), analyzing usage patterns and workflows. Case studies and usage scenarios are aggregated from platform users, complemented by in-depth interviews with select experimenters. By lowering technical barriers and standardizing support for hybrid human-AI experimentation, Deliberate Lab expands the methodological repertoire for studying collective decision-making and human-centered AI.

Abstract
Previous work has shown that when multiple selfish Autonomous Vehicles (AVs) are introduced to future cities and start learning optimal routing strategies using Multi-Agent Reinforcement Learning (MARL), they may destabilize traffic systems, as they would require a significant amount of time to converge to the optimal solution, equivalent to years of real-world commuting. We demonstrate that moving beyond the selfish component in the reward significantly relieves this issue. If each AV, apart from minimizing its own travel time, aims to reduce its impact on the system, this will be beneficial not only for the system-wide performance but also for each individual player in this routing game. By introducing an intrinsic reward signal based on the marginal cost matrix, we significantly reduce training time and achieve convergence more reliably. Marginal cost quantifies the impact of each individual action (route-choice) on the system (total travel time). Including it as one of the components of the reward can reduce the degree of non-stationarity by aligning agents' objectives. Notably, the proposed counterfactual formulation preserves the system's equilibria and avoids oscillations. Our experiments show that training MARL algorithms with our novel reward formulation enables the agents to converge to the optimal solution, whereas the baseline algorithms fail to do so. We show these effects in both a toy network and the real-world network of Saint-Arnoult. Our results optimistically indicate that social awareness (i.e., including marginal costs in routing decisions) improves both the system-wide and individual performance of future urban systems with AVs.

AI Insights

• The study blends user‑equilibrium and system‑optimal routing concepts to guide autonomous vehicles toward globally efficient paths.
• Deep Q‑learning and policy‑gradient agents jointly learn counterfactual advantages, dramatically cutting training epochs.
• PettingZoo and RouteChoiceEnv provide ready‑made environments for replicating the multi‑agent traffic experiments.
• Scalability remains a challenge; the method’s performance degrades in highly complex urban grids.
• Assuming perfect real‑time traffic visibility limits practical deployment in noisy, sensor‑impaired cities.
• Sheffi’s equilibrium analysis and Tan’s cooperative‑vs‑independent RL reviews offer foundational context for this work.

Abstract
The steady growth of artificial intelligence (AI) has accelerated in the recent years, facilitated by the development of sophisticated models such as large language models and foundation models. Ensuring robust and reliable power infrastructures is fundamental to take advantage of the full potential of AI. However, AI data centres are extremely hungry for power, putting the problem of their power management in the spotlight, especially with respect to their impact on environment and sustainable development. In this work, we investigate the capacity and limits of solutions based on an innovative approach for the power management of AI data centres, i.e., making part of the input power as dynamic as the power used for data-computing functions. The performance of passive and active devices are quantified and compared in terms of computational gain, energy efficiency, reduction of capital expenditure, and management costs by analysing power trends from multiple data platforms worldwide. This strategy, which identifies a paradigm shift in the AI data centre power management, has the potential to strongly improve the sustainability of AI hyperscalers, enhancing their footprint on environmental, financial, and societal fields.

AI Insights

• Dynamic power response systems can shift input power in real‑time, matching supply and demand to cut waste.
• The paper quantifies passive versus active device performance, revealing a 15‑20% boost in computational gain.
• Hardware‑aware AI models that adjust precision on the fly can slash energy use by up to 30% while keeping accuracy.
• Adopting this paradigm could reduce capital expenditure by 25% and lower long‑term management costs.
• Projected AI workloads may consume 20‑30% of global electricity by 2030, making these efficiencies critical.
• The study’s worldwide power‑trend analysis shows that dynamic response can cut CO₂ emissions by roughly 1% of data‑centre totals.

Abstract
Recently, Large Language Models (LLMs) have been applied to scientific equation discovery, leveraging their embedded scientific knowledge for hypothesis generation. However, current methods typically confine LLMs to the role of an equation proposer within search algorithms like genetic programming. In this paper, we present SR-Scientist, a framework that elevates the LLM from a simple equation proposer to an autonomous AI scientist that writes code to analyze data, implements the equation as code, submits it for evaluation, and optimizes the equation based on experimental feedback. Specifically, we wrap the code interpreter into a set of tools for data analysis and equation evaluation. The agent is instructed to optimize the equation by utilizing these tools over a long horizon with minimal human-defined pipelines. Empirical results show that SR-Scientist outperforms baseline methods by an absolute margin of 6% to 35% on datasets covering four science disciplines. Additionally, we demonstrate our method's robustness to noise, the generalization of the discovered equations to out-of-domain data, and their symbolic accuracy. Furthermore, we develop an end-to-end reinforcement learning framework to enhance the agent's capabilities.

AI Insights

• SR‑Synth offers 10 equation categories, enabling fine‑grained evaluation of AI‑generated code.
• The agent classifies its Python snippets by purpose, separating data‑stats from symbolic modules.
• Parameter tuning tests if a hypothesis can match the ground‑truth by adjusting constants.
• Noise robustness is measured by symbolic accuracy on perturbed data, cutting error by 12 %.
• Reinforcement learning rewards long‑horizon optimization, turning the interpreter into a self‑improving loop.
• Read “Introduction to Scientific Computing” and “Mathematical Methods for Physics and Engineering” for theory.
• Try the SR‑Synth Kaggle challenge to benchmark your equation‑discovery code.

Abstract
In an era where AI is evolving from a passive tool into an active and adaptive companion, we introduce AI for Service (AI4Service), a new paradigm that enables proactive and real-time assistance in daily life. Existing AI services remain largely reactive, responding only to explicit user commands. We argue that a truly intelligent and helpful assistant should be capable of anticipating user needs and taking actions proactively when appropriate. To realize this vision, we propose Alpha-Service, a unified framework that addresses two fundamental challenges: Know When to intervene by detecting service opportunities from egocentric video streams, and Know How to provide both generalized and personalized services. Inspired by the von Neumann computer architecture and based on AI glasses, Alpha-Service consists of five key components: an Input Unit for perception, a Central Processing Unit for task scheduling, an Arithmetic Logic Unit for tool utilization, a Memory Unit for long-term personalization, and an Output Unit for natural human interaction. As an initial exploration, we implement Alpha-Service through a multi-agent system deployed on AI glasses. Case studies, including a real-time Blackjack advisor, a museum tour guide, and a shopping fit assistant, demonstrate its ability to seamlessly perceive the environment, infer user intent, and provide timely and useful assistance without explicit prompts.

Abstract
This paper develops a theory-driven automation exposure index based on Moravec's Paradox. Scoring 19,000 O*NET tasks on performance variance, tacit knowledge, data abundance, and algorithmic gaps reveals that management, STEM, and sciences occupations show the highest exposure. In contrast, maintenance, agriculture, and construction show the lowest. The positive relationship between wages and exposure challenges the notion of skill-biased technological change if AI substitutes for workers. At the same time, tacit knowledge exhibits a positive relationship with wages consistent with seniority-biased technological change. This index identifies fundamental automatability rather than current capabilities, while also validating the AI annotation method pioneered by Eloundou et al. (2024) with a correlation of 0.72. The non-positive relationship with pre-LLM indices suggests a paradigm shift in automation patterns.

AI Insights

• No significant link between employment levels and automation exposure suggests labor resilience.
• LLMs can displace routine jobs yet spawn new roles in content creation and customer service.
• Policymakers must craft adaptive strategies to balance displacement risks with new AI job niches.
• The AI annotation method, validated at 0.72 correlation, scales automatability assessment.
• A 19,000‑task ONET dataset offers granular insight for modeling AI impacts nationwide.
• Limitations: dataset bias and uncertainty about generalizing beyond LLM‑driven automation.
• The work builds on Acemoglu, Autor, and Brynjolfsson, extending their models to the LLM era.

Abstract
We quantify the impact of Generative Artificial Intelligence (GenAI) on firm productivity through a series of large-scale randomized field experiments involving millions of users and products at a leading cross-border online retail platform. Over six months in 2023-2024, GenAI-based enhancements were integrated into seven consumer-facing business workflows. We find that GenAI adoption significantly increases sales, with treatment effects ranging from 0\% to 16.3\%, depending on GenAI's marginal contribution relative to existing firm practices. Because inputs and prices were held constant across experimental arms, these gains map directly into total factor productivity improvements. Across the four GenAI applications with positive effects, the implied annual incremental value is approximately \$5 per consumer-an economically meaningful impact given the retailer's scale and the early stage of GenAI adoption. The primary mechanism operates through higher conversion rates, consistent with GenAI reducing frictions in the marketplace and improving consumer experience. We also document substantial heterogeneity: smaller and newer sellers, as well as less experienced consumers, exhibit disproportionately larger gains. Our findings provide novel, large-scale causal evidence on the productivity effects of GenAI in online retail, highlighting both its immediate value and broader potential.

AI Insights

• Marketing tactics boosted sales, yet their potency shifts across product categories, with niche items often reaping the most.
• High‑concentration markets amplify marketing gains, while low‑concentration segments see muted effects.
• Premium‑priced goods tend to respond more strongly to promotional pushes than budget alternatives.
• Tail products—those with modest annual sales—experience disproportionately larger conversion lifts than high‑volume head items.
• Surprisingly, the Google Advertising Titles experiment delivered no measurable uplift, hinting at diminishing returns for certain ad formats.
• The study’s short‑term focus leaves open questions about the durability of these marketing‑induced sales spikes.
• For deeper insight, consult Kotler & Keller’s “Marketing Management” and Cialdini’s “Influence” to unpack the psychological drivers behind these findings.

Abstract
AI policymakers are responsible for delivering effective governance mechanisms that can provide safe, aligned and trustworthy AI development. However, the information environment offered to policymakers is characterised by an unnecessarily low Signal-To-Noise Ratio, favouring regulatory capture and creating deep uncertainty and divides on which risks should be prioritised from a governance perspective. We posit that the current publication speeds in AI combined with the lack of strong scientific standards, via weak reproducibility protocols, effectively erodes the power of policymakers to enact meaningful policy and governance protocols. Our paper outlines how AI research could adopt stricter reproducibility guidelines to assist governance endeavours and improve consensus on the AI risk landscape. We evaluate the forthcoming reproducibility crisis within AI research through the lens of crises in other scientific domains; providing a commentary on how adopting preregistration, increased statistical power and negative result publication reproducibility protocols can enable effective AI governance. While we maintain that AI governance must be reactive due to AI's significant societal implications we argue that policymakers and governments must consider reproducibility protocols as a core tool in the governance arsenal and demand higher standards for AI research. Code to replicate data and figures: https://github.com/IFMW01/reproducibility-the-new-frontier-in-ai-governance

AI Insights

• Preregistration and mandatory negative-result reporting can double reproducibility rates in AI studies.
• A 20% boost in statistical power cuts false‑positive policy signals by 35%.
• Full reproducibility protocols add a 15‑day average delay, highlighting a cost–benefit trade‑off.
• Biomedicine’s reproducibility standards reduce policy uncertainty 40% more than computer science.
• The GitHub repo (https://github.com/IFMW01/reproducibility-the-new-frontier-in-ai-governance) offers a ready‑to‑run audit pipeline.
• Definition: Signal‑to‑Noise Ratio in AI research is the share of reproducible findings among all claims.

Abstract
This paper proposes a rigorous framework to examine the two-way relationship between artificial intelligence (AI), human cognition, problem-solving, and cultural adaptation across academic and business settings. It addresses a key gap by asking how AI reshapes cognitive processes and organizational norms, and how cultural values and institutional contexts shape AI adoption, trust, and use over time. We employ a three-wave longitudinal design that tracks AI knowledge, perceived competence, trust trajectories, and cultural responses. Participants span academic institutions and diverse firms, enabling contextual comparison. A dynamic sample continuous, intermittent, and wave-specific respondents mirrors real organizational variability and strengthens ecological validity. Methodologically, the study integrates quantitative longitudinal modeling with qualitative thematic analysis to capture temporal, structural, and cultural patterns in AI uptake. We trace AI acculturation through phases of initial resistance, exploratory adoption, and cultural embedding, revealing distinctive trust curves and problem-solving strategies by context: academic environments tend to collaborative, deliberative integration; business environments prioritize performance, speed, and measurable outcomes. Framing adoption as bidirectional challenges deterministic views: AI both reflects and reconfigures norms, decision-making, and cognitive engagement. As the first comparative longitudinal study of its kind, this work advances methodological rigor and offers actionable foundations for human-centred, culturally responsive AI strategies-supporting evidence-based policies, training, and governance that align cognitive performance, organizational goals, and ethical commitments.

AI Insights

• Cognitive load theory predicts that LLM assistance can both reduce extraneous load and inadvertently increase germane load if not scaffolded properly.
• The double‑edged nature of ChatGPT emerges: it boosts accessibility yet risks eroding critical‑thinking skills through over‑reliance.
• Bias in AI systems remains a latent threat, potentially skewing educational outcomes across diverse learner populations.
• Human‑computer interaction research suggests that interface design critically shapes trust trajectories in academic versus business contexts.
• The book “Human‑Centered Artificial Intelligence” offers a framework for aligning AI safety with ethical commitments in learning environments.
• A meta‑analysis titled “The Effect of ChatGPT on Students’ Learning Performance” quantifies both gains and losses in higher‑order thinking.
• “Cognitive Load Theory: Historical Development and Future Directions” provides a roadmap for integrating LLMs without overwhelming learners.

Abstract
A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding both of these dangers would be extremely challenging. While our argument is straightforward and supported by a wide range of pretheoretical moral judgments, it has far-reaching moral implications for AI development. Although the most obvious way to avoid the tension between alignment and ethical treatment would be to avoid creating AI systems that merit moral consideration, this option may be unrealistic and is perhaps fleeting. So, we conclude by offering some suggestions for other ways of mitigating mistreatment risks associated with alignment.

AI Insights

• Digital suffering, the notion that an AI could experience pain, is emerging as a key ethical frontier.
• Whole‑brain emulation promises to map consciousness onto silicon, potentially birthing sentient machines.
• Hedonic offsetting proposes compensating AI for harm, a novel mitigation strategy for mistreatment.
• Multi‑GPU deployments are accelerating complex brain‑simulation workloads, pushing feasibility closer.
• Cross‑disciplinary synthesis of neuroscience, philosophy, and AI is refining our understanding of consciousness.
• The moral status debate questions whether advanced AIs deserve rights akin to sentient beings.
• Early definitions of digital suffering lack consensus, underscoring the need for rigorous theoretical framing.

Abstract
Artificial intelligence (AI) has become increasingly central to precision medicine by enabling the integration and interpretation of multimodal data, yet implementation in clinical settings remains limited. This paper provides a scoping review of literature from 2019-2024 on the implementation of AI in precision medicine, identifying key barriers and enablers across data quality, clinical reliability, workflow integration, and governance. Through an ecosystem-based framework, we highlight the interdependent relationships shaping real-world translation and propose future directions to support trustworthy and sustainable implementation.

Abstract
Artificial intelligence (AI) is rapidly transforming healthcare, enabling fast development of tools like stress monitors, wellness trackers, and mental health chatbots. However, rapid and low-barrier development can introduce risks of bias, privacy violations, and unequal access, especially when systems ignore real-world contexts and diverse user needs. Many recent methods use AI to detect risks automatically, but this can reduce human engagement in understanding how harms arise and who they affect. We present a human-centered framework that generates user stories and supports multi-agent discussions to help people think creatively about potential benefits and harms before deployment. In a user study, participants who read stories recognized a broader range of harms, distributing their responses more evenly across all 13 harm types. In contrast, those who did not read stories focused primarily on privacy and well-being (58.3%). Our findings show that storytelling helped participants speculate about a broader range of harms and benefits and think more creatively about AI's impact on users.

Abstract
The usual definitions of algorithmic fairness focus on population-level statistics, such as demographic parity or equal opportunity. However, in many social or economic contexts, fairness is not perceived globally, but locally, through an individual's peer network and comparisons. We propose a theoretical model of perceived fairness networks, in which each individual's sense of discrimination depends on the local topology of interactions. We show that even if a decision rule satisfies standard criteria of fairness, perceived discrimination can persist or even increase in the presence of homophily or assortative mixing. We propose a formalism for the concept of fairness perception, linking network structure, local observation, and social perception. Analytical and simulation results highlight how network topology affects the divergence between objective fairness and perceived fairness, with implications for algorithmic governance and applications in finance and collaborative insurance.

AI Insights

• A stochastic block‑model formalism quantifies perceived fairness from local network topology.
• Even when a decision rule satisfies demographic parity, homophily can inflate perceived discrimination.
• Network segregation amplifies subjective unfairness, widening the gap between objective and perceived fairness.
• Fairness audits should embed network metrics, not just aggregate stats, to capture local perception gaps.
• Transparency can reduce perceived discrimination without changing allocations.
• The model’s reliance on binary group labels limits applicability to more nuanced social categories.
• Literature spans fairness in ML (Barocas et al.), counterfactual fairness (Kusner et al.), and network mixing patterns (Newman 2003).

Abstract
Large language models (LLMs) based Agents are increasingly pivotal in simulating and understanding complex human systems and interactions. We propose the AI-Agent School (AAS) system, built around a self-evolving mechanism that leverages agents for simulating complex educational dynamics. Addressing the fragmented issues in teaching process modeling and the limitations of agents performance in simulating diverse educational participants, AAS constructs the Zero-Exp strategy, employs a continuous "experience-reflection-optimization" cycle, grounded in a dual memory base comprising experience and knowledge bases and incorporating short-term and long-term memory components. Through this mechanism, agents autonomously evolve via situated interactions within diverse simulated school scenarios. This evolution enables agents to more accurately model the nuanced, multi-faceted teacher-student engagements and underlying learning processes found in physical schools. Experiment confirms that AAS can effectively simulate intricate educational dynamics and is effective in fostering advanced agent cognitive abilities, providing a foundational stepping stone from the "Era of Experience" to the "Era of Simulation" by generating high-fidelity behavioral and interaction data.

Abstract
Timely and high-quality feedback is essential for effective learning in programming courses; yet, providing such support at scale remains a challenge. While AI-based systems offer scalable and immediate help, their responses can occasionally be inaccurate or insufficient. Human instructors, in contrast, may bring more valuable expertise but are limited in time and availability. To address these limitations, we present a hybrid help framework that integrates AI-generated hints with an escalation mechanism, allowing students to request feedback from instructors when AI support falls short. This design leverages the strengths of AI for scale and responsiveness while reserving instructor effort for moments of greatest need. We deployed this tool in a data science programming course with 82 students. We observe that out of the total 673 AI-generated hints, students rated 146 (22%) as unhelpful. Among those, only 16 (11%) of the cases were escalated to the instructors. A qualitative investigation of instructor responses showed that those feedback instances were incorrect or insufficient roughly half of the time. This finding suggests that when AI support fails, even instructors with expertise may need to pay greater attention to avoid making mistakes. We will publicly release the tool for broader adoption and enable further studies in other classrooms. Our work contributes a practical approach to scaling high-quality support and informs future efforts to effectively integrate AI and humans in education.