This study explores how AI-powered digital innovations are reshaping organisational accountability in a transnational governance context. As AI systems increasingly mediate decision-making in domains such as auditing and financial reporting, traditional mechanisms of accountability, based on control, transparency, and auditability, are being destabilised. We integrate the Technology Acceptance Model (TAM), Actor-Network Theory (ANT), and institutional theory to examine how organisations adopt AI technologies in response to regulatory, ethical, and cultural pressures that transcend national boundaries. We argue that accountability is co-constructed within global socio-technical networks, shaped not only by user perceptions but also by governance logics and normative expectations. Extending TAM, we incorporate compliance and legitimacy as key factors in perceived usefulness and usability. Drawing on ANT, we reconceptualise accountability as a relational and emergent property of networked assemblages. We propose two organisational strategies including internal governance reconfiguration and external actor-network engagement to foster responsible, legitimate, and globally accepted AI adoption in the accounting domain.

Most commonly used language models (LMs) are instruction-tuned and aligned using a combination of fine-tuning and reinforcement learning, causing them to refuse users requests deemed harmful by the model. However, jailbreak prompts can often bypass these refusal mechanisms and elicit harmful responses. In this work, we study the extent to which information accessed via jailbreak prompts is decodable using linear probes trained on LM hidden states. We show that a great deal of initially refused information is linearly decodable. For example, across models, the response of a jailbroken LM for the average IQ of a country can be predicted by a linear probe with Pearson correlations exceeding $0.8$. Surprisingly, we find that probes trained on base models (which do not refuse) sometimes transfer to their instruction-tuned versions and are capable of revealing information that jailbreaks decode generatively, suggesting that the internal representations of many refused properties persist from base LMs through instruction-tuning. Importantly, we show that this information is not merely "leftover" in instruction-tuned models, but is actively used by them: we find that probe-predicted values correlate with LM generated pairwise comparisons, indicating that the information decoded by our probes align with suppressed generative behavior that may be expressed more subtly in other downstream tasks. Overall, our results suggest that instruction-tuning does not wholly eliminate or even relocate harmful information in representation space-they merely suppress its direct expression, leaving it both linearly accessible and indirectly influential in downstream behavior.

Alternative Assets tokenization is transforming non-traditional financial instruments are represented and traded on the web. However, ensuring trustworthiness in web-based tokenized ecosystems poses significant challenges, from verifying off-chain asset data to enforcing regulatory compliance. This paper proposes an AI-governed agent architecture that integrates intelligent agents with blockchain to achieve web-trustworthy tokenization of alternative assets. In the proposed architecture, autonomous agents orchestrate the tokenization process (asset verification, valuation, compliance checking, and lifecycle management), while an AI-driven governance layer monitors agent behavior and enforces trust through adaptive policies and cryptoeconomic incentives. We demonstrate that this approach enhances transparency, security, and compliance in asset tokenization, addressing key concerns around data authenticity and fraud. A case study on tokenizing real estate assets illustrates how the architecture mitigates risks (e.g., fraudulent listings and money laundering) through real-time AI anomaly detection and on-chain enforcement. Our evaluation and analysis suggest that combining AI governance with multi-agent systems and blockchain can significantly bolster trust in tokenized asset ecosystems. This work offers a novel framework for trustworthy asset tokenization on the web and provides insights for practitioners aiming to deploy secure, compliant tokenization platforms.

The Cross-Entropy Method (CEM) is a widely adopted trajectory optimizer in model-based reinforcement learning (MBRL), but its unimodal sampling strategy often leads to premature convergence in multimodal landscapes. In this work, we propose Bregman Centroid Guided CEM ($\mathcal{BC}$-EvoCEM), a lightweight enhancement to ensemble CEM that leverages $\textit{Bregman centroids}$ for principled information aggregation and diversity control. $\textbf{$\mathcal{BC}$-EvoCEM}$ computes a performance-weighted Bregman centroid across CEM workers and updates the least contributing ones by sampling within a trust region around the centroid. Leveraging the duality between Bregman divergences and exponential family distributions, we show that $\textbf{$\mathcal{BC}$-EvoCEM}$ integrates seamlessly into standard CEM pipelines with negligible overhead. Empirical results on synthetic benchmarks, a cluttered navigation task, and full MBRL pipelines demonstrate that $\textbf{$\mathcal{BC}$-EvoCEM}$ enhances both convergence and solution quality, providing a simple yet effective upgrade for CEM.

The long-standing vision of intelligent cities is to create efficient, livable, and sustainable urban environments using big data and artificial intelligence technologies. Recently, the advent of Large Language Models (LLMs) has opened new ways toward realizing this vision. With powerful semantic understanding and reasoning capabilities, LLMs can be deployed as intelligent agents capable of autonomously solving complex problems across domains. In this article, we focus on Urban LLM Agents, which are LLM-powered agents that are semi-embodied within the hybrid cyber-physical-social space of cities and used for system-level urban decision-making. First, we introduce the concept of urban LLM agents, discussing their unique capabilities and features. Second, we survey the current research landscape from the perspective of agent workflows, encompassing urban sensing, memory management, reasoning, execution, and learning. Third, we categorize the application domains of urban LLM agents into five groups: urban planning, transportation, environment, public safety, and urban society, presenting representative works in each group. Finally, we discuss trustworthiness and evaluation issues that are critical for real-world deployment, and identify several open problems for future research. This survey aims to establish a foundation for the emerging field of urban LLM agents and to provide a roadmap for advancing the intersection of LLMs and urban intelligence. A curated list of relevant papers and open-source resources is maintained and continuously updated at https://github.com/usail-hkust/Awesome-Urban-LLM-Agents.

We investigate how Large Language Models (LLMs) behave when simulating political discourse on social media. Leveraging 21 million interactions on X during the 2024 U.S. presidential election, we construct LLM agents based on 1,186 real users, prompting them to reply to politically salient tweets under controlled conditions. Agents are initialized either with minimal ideological cues (Zero Shot) or recent tweet history (Few Shot), allowing one-to-one comparisons with human replies. We evaluate three model families (Gemini, Mistral, and DeepSeek) across linguistic style, ideological consistency, and toxicity. We find that richer contextualization improves internal consistency but also amplifies polarization, stylized signals, and harmful language. We observe an emergent distortion that we call "generation exaggeration": a systematic amplification of salient traits beyond empirical baselines. Our analysis shows that LLMs do not emulate users, they reconstruct them. Their outputs, indeed, reflect internal optimization dynamics more than observed behavior, introducing structural biases that compromise their reliability as social proxies. This challenges their use in content moderation, deliberative simulations, and policy modeling.

Topic model and document-clustering evaluations either use automated metrics that align poorly with human preferences or require expert labels that are intractable to scale. We design a scalable human evaluation protocol and a corresponding automated approximation that reflect practitioners' real-world usage of models. Annotators -- or an LLM-based proxy -- review text items assigned to a topic or cluster, infer a category for the group, then apply that category to other documents. Using this protocol, we collect extensive crowdworker annotations of outputs from a diverse set of topic models on two datasets. We then use these annotations to validate automated proxies, finding that the best LLM proxies are statistically indistinguishable from a human annotator and can therefore serve as a reasonable substitute in automated evaluations. Package, web interface, and data are at https://github.com/ahoho/proxann

Semantic data representations are critical in artificial intelligence, significantly enhancing model performance in tasks like transfer and zero-shot learning (Lake et al., 2017). Central to this effort is to disentangle latent representations in generative models--representations where each latent dimension corresponds to an independent underlying factor of variation in the data. Disentanglement is achieved by leveraging statistical properties of the latent space and the dataset, enabling models where changes in one latent dimension affect only its corresponding factor without impacting others. This not only improves model interpretability but also enhances robustness against adversarial attacks (Yang et al., 2021). For a comprehensive review of disentanglement and its statistical underpinnings, see Wang et al. (2023). Datasets encountered in scientific research are often heterogeneous in modalities, fidelities, and accuracy where a particular entity or a state may be simultaneously associated with multiple images, graphs, vectors, scalar parameters, or labels with various associated measurement uncertainties.

This thesis contributes to the theoretical understanding of local update algorithms, especially Local SGD, in distributed and federated optimization under realistic models of data heterogeneity. A central focus is on the bounded second-order heterogeneity assumption, which is shown to be both necessary and sufficient for local updates to outperform centralized or mini-batch methods in convex and non-convex settings. The thesis establishes tight upper and lower bounds in several regimes for various local update algorithms and characterizes the min-max complexity of multiple problem classes. At its core is a fine-grained consensus-error-based analysis framework that yields sharper finite-time convergence bounds under third-order smoothness and relaxed heterogeneity assumptions. The thesis also extends to online federated learning, providing fundamental regret bounds under both first-order and bandit feedback. Together, these results clarify when and why local updates offer provable advantages, and the thesis serves as a self-contained guide for analyzing Local SGD in heterogeneous environments.

Water consumption is an increasingly critical dimension of computing sustainability, especially as AI workloads rapidly scale. However, current water impact assessment often overlooks where and when water stress is more severe. To fill in this gap, we present SCARF, the first general framework that evaluates water impact of computing by factoring in both spatial and temporal variations in water stress. SCARF calculates an Adjusted Water Impact (AWI) metric that considers both consumption volume and local water stress over time. Through three case studies on LLM serving, datacenters, and semiconductor fabrication plants, we show the hidden opportunities for reducing water impact by optimizing location and time choices, paving the way for water-sustainable computing. The code is available at https://github.com/jojacola/SCARF.