arXiv subjects

Daniel Silver

Publications and source records attributed to Daniel Silver.

17 recordsLinked to original sources

arXiv2026

Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory

As AI systems are deployed across increasingly diverse social contexts, alignment can no longer be framed as the optimization of a single, unified set of values. Instead, systems must be able to recognize, represent, and respond to multiple legitimate perspectives. This has led to growing interest in pluralistic alignment, which seeks to move beyond one-size-fits-all models of appropriate behaviour. However, current approaches often lack a clear account of how values are socially organized, contested, and coordinated in practice. In this paper, we argue that social theory provides essential conceptual and design resources for addressing these challenges. Drawing on established traditions in sociology, we show how perspectives can be understood as structured by roles, shaped through interaction, and distributed across fields of power and expertise. We translate these insights into concrete implications for AI system design, including role-based representations, structured coordination among perspectives, and context-sensitive evaluation. For agentic systems, this requires aligning not only final outputs, but also the role activations, deliberative traces, aggregation rules, and feedback loops through which those outputs are produced. Our contribution is to reposition pluralistic alignment as a problem of socially grounded coordination rather than output diversification. We outline a design space for systems that engage multiple perspectives in structured and accountable ways, and we identify directions for future work to implement and empirically evaluate these approaches in real-world settings.

cs.AI↗

arXiv2026

Persona Prompting in Multimodal Urban Perception: Descriptive Convergence and Interpretive Variation

This study examines how persona prompting shapes language generated by two multimodal large language models in urban perception, a setting for examining subjective interpretations of shared visual evidence. We organize outputs into three functional levels: descriptive grounding (captions), intermediate semantic layer (perception tags), and interpretive framing (justifications). Using approximately 60,000 persona-conditioned annotations per model from Qwen3-VL-8B and Gemma-4-E4B-it, we find that captions converge strongly across persona profiles and show only small attribute-associated differences. Justifications vary substantially more: economic status produces the largest difference in both models, with political orientation and personality also prominent. Paired image-level comparisons confirm larger justification than caption differences for these three attributes. For perception tags, personas sharing the same attribute level produce more similar tag sets than personas with different attribute levels, with the largest separation observed for economic status. Exploratory topic analysis further reveals persona-specific evaluative emphasis. Across models, profile-pair similarity patterns are strongly correlated for all three output types, although agreement is lowest for justifications. Overall, persona prompting affects interpretive framing more strongly than descriptive grounding.

cs.CL↗

arXiv2026

Stable Behavior, Limited Variation: Persona Validity in LLM Agents for Urban Sentiment Perception

Large Language Models (LLMs) are increasingly used as proxies for human perception in urban analysis, yet it remains unclear whether persona prompting produces meaningful and reproducible behavioral diversity. We investigate whether distinct personas influence urban sentiment judgments generated by multimodal LLMs. Using a factorial set of personas spanning gender, economic status, political orientation, and personality, we instantiate multiple agents per persona to evaluate urban scene images from the PerceptSent dataset and assess both within-persona consistency and cross-persona variation. Results show strong convergence among agents sharing a persona, indicating stable and reproducible behavior. However, cross-persona differentiation is limited: economic status and personality induce statistically detectable but practically modest variation, while gender shows no measurable effect and political orientation only negligible impact. Agents also exhibit an extremity bias, collapsing intermediate sentiment categories common in human annotations. As a result, performance remains strong on coarse-grained polarity tasks but degrades as sentiment resolution increases, suggesting that simple label-based persona prompting does not capture fine-grained perceptual judgments. To isolate the contribution of persona conditioning, we additionally evaluate the same model without personas. Surprisingly, the no-persona model sometimes matches or exceeds persona-conditioned agreement with human labels across all task variants, suggesting that simple label-based persona prompting may add limited annotation value in this setting.

cs.CL↗

arXiv2025

A Relational Model of Neighborhood Mobility: The Role of Amenities and Cultural Alignment

Why are some neighborhoods strongly connected while others remain isolated? Although standard explanations focus on demographics, economics, and geography, movement across the city may also depend on cultural styles and amenity mix. This study proposes a relational, cross-national model in which local culture and amenity mix alignment creates a "soft infrastructure" of urban mobility, i.e., symbolic cues and functional features that shape expectations about the character of places. Using ~650 million Google Places reviews to measure co-visitation between U.S. ZIP codes and ~30 million Canadian change-of-address to track residential mobility, results show that neighborhoods with similar cultural styles and amenities are significantly more connected. These effects persist even after controlling for race, income, education, politics, housing costs, and distance. Urban cohesion and segregation depend not only on who lives where or how far apart neighborhoods are, but on the shared cultural and material ecologies that structure movement across the city.

cs.SI↗

arXiv2025

CoordFlow: Coordinate Flow for Pixel-wise Neural Video Representation

In the field of video compression, the pursuit for better quality at lower bit rates remains a long-lasting goal. Recent developments have demonstrated the potential of Implicit Neural Representation (INR) as a promising alternative to traditional transform-based methodologies. Video INRs can be roughly divided into frame-wise and pixel-wise methods according to the structure the network outputs. While the pixel-based methods are better for upsampling and parallelization, frame-wise methods demonstrated better performance. We introduce CoordFlow, a novel pixel-wise INR for video compression. It yields state-of-the-art results compared to other pixel-wise INRs and on-par performance compared to leading frame-wise techniques. The method is based on the separation of the visual information into visually consistent layers, each represented by a dedicated network that compensates for the layer's motion. When integrated, a byproduct is an unsupervised segmentation of video sequence. Objects motion trajectories are implicitly utilized to compensate for visual-temporal redundancies. Additionally, the proposed method provides inherent video upsampling, stabilization, inpainting, and denoising capabilities.

cs.CV↗

arXiv2024

Pathways on the Image Manifold: Image Editing via Video Generation

Recent advances in image editing, driven by image diffusion models, have shown remarkable progress. However, significant challenges remain, as these models often struggle to follow complex edit instructions accurately and frequently compromise fidelity by altering key elements of the original image. Simultaneously, video generation has made remarkable strides, with models that effectively function as consistent and continuous world simulators. In this paper, we propose merging these two fields by utilizing image-to-video models for image editing. We reformulate image editing as a temporal process, using pretrained video models to create smooth transitions from the original image to the desired edit. This approach traverses the image manifold continuously, ensuring consistent edits while preserving the original image's key aspects. Our approach achieves state-of-the-art results on text-based image editing, demonstrating significant improvements in both edit accuracy and image preservation. Visit our project page at https://rotsteinnoam.github.io/Frame2Frame.

cs.CV↗

arXiv2024

Qompose: A Technique to Select Optimal Algorithm- Specific Layout for Neutral Atom Quantum Architectures

As quantum computing architecture matures, it is important to investigate new technologies that lend unique advantages. In this work, we propose, Qompose, a neutral atom quantum computing framework for efficiently composing quantum circuits on 2-D topologies of neutral atoms. Qompose selects an efficient topology for any given circuit in order to optimize for length of execution through efficient parallelism and for overall fidelity. our extensive evaluation demonstrates the Qompose is effective for a large collection of randomly-generated quantum circuits and a range of real-world benchmarks including VQE, ISING, and QAOA.

quant-ph↗

arXiv2024

OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative Adversarial Networks on NISQ-Era Machines

Driven by swift progress in hardware capabilities, quantum machine learning has emerged as a research area of interest. Recently, quantum image generation has produced promising results. However, prior quantum image generation techniques rely on classical neural networks, limiting their quantum potential and image quality. To overcome this, we introduce OrganiQ, the first quantum GAN capable of producing high-quality images without using classical neural networks.

quant-ph↗

arXiv2024

Bubble reachers and uncivil discourse in polarized online public sphere

Early optimism saw possibilities for social media to renew democratic discourse, marked by hopes for individuals from diverse backgrounds to find opportunities to learn from and interact with others different from themselves. This optimism quickly waned as social media seemed to breed ideological homophily marked by "filter bubble" or "echo chambers." A typical response to the sense of fragmentation has been to encourage exposure to more cross-partisan sources of information. But do outlets that reach across partisan lines in fact generate more civil discourse? And does the civility of discourse hosted by such outlets vary depending on the political context in which they operate? To answer these questions, we identified bubble reachers, users who distribute content that reaches other users with diverse political opinions in recent presidential elections in Brazil, where populism has deep roots in the political culture, and Canada, where the political culture is comparatively moderate. Given that background, this research studies unexplored properties of content shared by bubble reachers, specifically the quality of conversations and comments it generates. We examine how ideologically neutral bubble reachers differ from ideologically partisan accounts in the level of uncivil discourse they provoke, and explore how this varies in the context of the two countries considered. Our results suggest that while ideologically neutral bubble reachers support less uncivil discourse in Canada, the opposite relationship holds in Brazil. Even non-political content by ideologically neutral bubble reachers elicits a considerable amount of uncivil discourse in Brazil. This indicates that bubble reaching and incivility are moderated by the national political context. Our results complicate the simple hypothesis of a universal impact of neutral bubble reachers across contexts.

cs.SI↗

arXiv2024

Using Graph Neural Networks to Predict Local Culture

Urban research has long recognized that neighbourhoods are dynamic and relational. However, lack of data, methodologies, and computer processing power have hampered a formal quantitative examination of neighbourhood relational dynamics. To make progress on this issue, this study proposes a graph neural network (GNN) approach that permits combining and evaluating multiple sources of information about internal characteristics of neighbourhoods, their past characteristics, and flows of groups among them, potentially providing greater expressive power in predictive models. By exploring a public large-scale dataset from Yelp, we show the potential of our approach for considering structural connectedness in predicting neighbourhood attributes, specifically to predict local culture. Results are promising from a substantive and methodologically point of view. Substantively, we find that either local area information (e.g. area demographics) or group profiles (tastes of Yelp reviewers) give the best results in predicting local culture, and they are nearly equivalent in all studied cases. Methodologically, exploring group profiles could be a helpful alternative where finding local information for specific areas is challenging, since they can be extracted automatically from many forms of online data. Thus, our approach could empower researchers and policy-makers to use a range of data sources when other local area information is lacking.

cs.LG↗

arXiv2023

QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers

Quantum computers can theoretically have significant acceleration over classical computers; but, the near-future era of quantum computing is limited due to small number of qubits that are also error prone. Quilt is a framework for performing multi-class classification task designed to work effectively on current error-prone quantum computers. Quilt is evaluated with real quantum machines as well as with projected noise levels as quantum machines become more noise-free. Quilt demonstrates up to 85% multi-class classification accuracy with the MNIST dataset on a five-qubit system.

quant-ph↗

arXiv2023

SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers

Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs. However, these efforts have yet to solve unsupervised similarity detection tasks due to the challenge of porting them to run on quantum computers. To overcome this challenge, we propose SLIQ, the first open-sourced work for resource-efficient quantum similarity detection networks, built with practical and effective quantum learning and variance-reducing algorithms.

quant-ph↗

arXiv2023

MosaiQ: Quantum Generative Adversarial Networks for Image Generation on NISQ Computers

Quantum machine learning and vision have come to the fore recently, with hardware advances enabling rapid advancement in the capabilities of quantum machines. Recently, quantum image generation has been explored with many potential advantages over non-quantum techniques; however, previous techniques have suffered from poor quality and robustness. To address these problems, we introduce, MosaiQ, a high-quality quantum image generation GAN framework that can be executed on today's Near-term Intermediate Scale Quantum (NISQ) computers.

quant-ph↗

arXiv2023

Toward Privacy in Quantum Program Execution On Untrusted Quantum Cloud Computing Machines for Business-sensitive Quantum Needs

Quantum computing is an emerging paradigm that has shown great promise in accelerating large-scale scientific, optimization, and machine-learning workloads. With most quantum computing solutions being offered over the cloud, it has become imperative to protect confidential and proprietary quantum code from being accessed by untrusted and/or adversarial agents. In response to this challenge, we propose SPYCE, which is the first known solution to obfuscate quantum code and output to prevent the leaking of any confidential information over the cloud. SPYCE implements a lightweight, scalable, and effective solution based on the unique principles of quantum computing to achieve this task.

quant-ph↗

arXiv2022

CHARTER: Identifying the Most-Critical Gate Operations in Quantum Circuits via Amplified Gate Reversibility

When quantum programs are executed on noisy intermediate-scale quantum (NISQ) computers, they experience hardware noise; consequently, the program outputs are often erroneous. To mitigate the adverse effects of hardware noise, it is necessary to understand the effect of hardware noise on the program output and more fundamentally, understand the impact of hardware noise on specific regions within a quantum program. Identifying and optimizing regions that are more noise-sensitive is the key to expanding the capabilities of NISQ computers. Toward achieving that goal, we propose CHARTER, a novel technique to pinpoint specific gates and regions within a quantum program that are the most affected by the hardware noise and that have the highest impact on the program output. Using CHARTER's methodology, programmers can obtain a precise understanding of how different components of their code affect the output and optimize those components without the need for non-scalable quantum simulation on classical computers.

quant-ph↗

arXiv2022

Changing the Scene: applying four models of social evolution to the scenescape

This paper elaborates a multi-model approach to studying how local scenes change. We refer to this as the "4 D's" of scene change: development, differentiation, defense, and diffusion. Each posits somewhat distinct change processes, and has its own tradition of theory and empirical research, which we briefly review. After summarizing some major trends in scenes and amenities in the US context, for each change model, we present some initial findings, discussing data and methods throughout. Our overall goal is to point toward new research arcs on change models of scenes, and to give some clear examples and directions for how to think about and collect data to understand what makes some scenes change, others not, why, and in what directions.

cs.SI↗

arXiv2021

Reaching the bubble may not be enough: news media role in online political polarization

Politics in different countries show diverse degrees of polarization, which tends to be stronger on social media, given how easy it became to connect and engage with like-minded individuals on the web. A way of reducing polarization would be by distributing cross-partisan news among individuals with distinct political orientations, i.e., ``reaching the bubbles''. This study investigates whether this holds in the context of nationwide elections in Brazil and Canada. We collected politics-related tweets shared during the 2018 Brazilian presidential election and the 2019 Canadian federal election. Next, we proposed an updated centrality metric that enables identifying highly central bubble reachers, nodes that can distribute content among users with diverging political opinions - a fundamental metric for the proposed study. After that, we analyzed how users engage with news content shared by bubble reachers, its source, and its topics, considering its political orientation. Among other results, we found that, even though news media disseminate content that interests different sides of the political spectrum, users tend to engage considerably more with content that aligns with their political orientation, regardless of the topic.

cs.SI↗