Summary: The paper proposes a way to incorporate constraints into the learning of generative models through posterior regularization. In doing so, the paper draws connections between posterior regularization and policy optimization. One of the key contributions of this paper is that the constraints are modeled as extrinsic rewards and learned through inverse reinforcement learning. The paper studies an interesting and very practical problem and the contributions are substantial. The writing could definitely be made clearer for Sections 3 and 4, where the overloaded notation is often hard to follow. I have the following questions: 1.

The authors develop a framework allowing VAE type computation on a broad class of probablistic model structures. This is motivated in particular by the idea that some lvs may have a straightforward meaning and have some labels available (e.g. which digit in MNIST/SVHN, what lighting direction in the face data), whereas others are more intangible (e.g. They propose a slightly different approach to the semi-supervised VAE of Kingma et al., by considering the (semi)supervised variables y as LVs forced to specific values for the supervised data samples. This is straightforward in the setting where q(y x) can be calculated directly, and can be handled by importance sampling if integration over z is required to calculate q(y x). Experiments are presented on MNIST, SVHN and a faces image data with variation in lighting according 38 individuals.

This paper introduces a pioneering approach to artificial intelligence research, embodied in our O1 Replication Journey. In response to the announcement of OpenAI's groundbreaking O1 model, we embark on a transparent, real-time exploration to replicate its capabilities while reimagining the process of conducting and communicating AI research. Our methodology addresses critical challenges in modern AI research, including the insularity of prolonged team-based projects, delayed information sharing, and the lack of recognition for diverse contributions. By providing comprehensive, real-time documentation of our replication efforts, including both successes and failures, we aim to foster open science, accelerate collective advancement, and lay the groundwork for AI-driven scientific discovery. Our research progress report diverges significantly from traditional research papers, offering continuous updates, full process transparency, and active community engagement throughout the research journey. Technologically, we proposed the journey learning paradigm, which encourages models to learn not just shortcuts, but the complete exploration process, including trial and error, reflection, and backtracking. With only 327 training samples and without any additional tricks, journey learning outperformed conventional supervised learning by over 8\% on the MATH dataset, demonstrating its extremely powerful potential. We believe this to be the most crucial component of O1 technology that we have successfully decoded. We share valuable resources including technical hypotheses and insights, cognitive exploration maps, custom-developed tools, etc at https://github.com/GAIR-NLP/O1-Journey.

Effective monitoring of freight transportation is essential for advancing sustainable, low-carbon economies. Traditional methods relying on single-modal data and discrete simulations fall short in optimizing intermodal systems holistically. These systems involve interconnected processes that affect shipping time, costs, emissions, and socio-economic factors. Developing digital twins for real-time awareness, predictive analytics, and urban logistics optimization requires extensive efforts in knowledge discovery, data integration, and multi-domain simulation. Recent advancements in generative AI offer new opportunities to streamline digital twin development by automating knowledge discovery and data integration, generating innovative simulation and optimization solutions. These models extend digital twins' capabilities by promoting autonomous workflows for data engineering, analytics, and software development. This paper proposes an innovative paradigm that leverages generative AI to enhance digital twins for urban research and operations. Using freight decarbonization as a case study, we propose a conceptual framework employing transformer-based language models to enhance an urban digital twin through foundation models. We share preliminary results and our vision for more intelligent, autonomous, and general-purpose digital twins for optimizing integrated freight systems from multimodal to synchromodal paradigms.

Bibliometric analysis is essential for understanding research trends, scope, and impact in urban science, especially in high-impact journals, such Nature Portfolios. However, traditional methods, relying on keyword searches and basic NLP techniques, often fail to uncover valuable insights not explicitly stated in article titles or keywords. These approaches are unable to perform semantic searches and contextual understanding, limiting their effectiveness in classifying topics and characterizing studies. In this paper, we address these limitations by leveraging Generative AI models, specifically transformers and Retrieval-Augmented Generation (RAG), to automate and enhance bibliometric analysis. We developed a technical workflow that integrates a vector database, Sentence Transformers, a Gaussian Mixture Model (GMM), Retrieval Agent, and Large Language Models (LLMs) to enable contextual search, topic ranking, and characterization of research using customized prompt templates. A pilot study analyzing 223 urban science-related articles published in Nature Communications over the past decade highlights the effectiveness of our approach in generating insightful summary statistics on the quality, scope, and characteristics of papers in high-impact journals. This study introduces a new paradigm for enhancing bibliometric analysis and knowledge retrieval in urban research, positioning an AI agent as a powerful tool for advancing research evaluation and understanding.

Evaluating diffusion-based image-editing models is a crucial task in the field of Generative AI. Specifically, it is imperative to assess their capacity to execute diverse editing tasks while preserving the image content and realism. While recent developments in generative models have opened up previously unheard-of possibilities for image editing, conducting a thorough evaluation of these models remains a challenging and open task. The absence of a standardized evaluation benchmark, primarily due to the inherent need for a post-edit reference image for evaluation, further complicates this issue. Currently, evaluations often rely on established models such as CLIP or require human intervention for a comprehensive understanding of the performance of these image editing models. Our benchmark, PixLens, provides a comprehensive evaluation of both edit quality and latent representation disentanglement, contributing to the advancement and refinement of existing methodologies in the field.

This paper presents an alternative to variational autoencoders and other generative models with latent variables that rely on the wake-sleep algorithm for training. The main problem with the wake-sleep algorithm is its bias: the recognition model has different conditional independencies than the generative model and it's trained to optimize a different objective. DDC-HM solves this by instead working with sufficient statistics, and using those to implicitly define the maximum entropy distribution consistent with those statistics. The measurements chosen are random functions. The methods are evaluated on synthetic data and two small vision datasets (image patches and MNIST), comparing against two baselines using the MMD metric. I don't know the related work well enough to evaluate the novelty with confidence.

When investors poured 6.6 billion into OpenAI last week, they seemed largely unbothered by the latest drama, which recently saw the company's chief technology officer, Mira Murati, along with chief research officer Bob McCrew and Barret Zoph, a vice president of research, abruptly quit. And yet those three departures were just the latest in an ongoing exodus of key technical talent. Over the past few years, OpenAI has lost several researchers who played crucial roles in developing the algorithms, techniques, and infrastructure that helped make it the world leader in AI as well as a household name. Several other ex-OpenAI employees who spoke to WIRED said that an ongoing shift to a more commercial focus continues to be a source of friction. "People who like to do research are being forced to do product," says one former employee who works at a rival AI company but has friends at OpenAI. This person says some of their contacts at the firm have reached out in recent weeks to inquire about jobs.

The paper proposes a novel problem formulation for lossy compression, namely distribution-preserving lossy compression (DPLC). For a rate constrained lossy compression scheme, for large enough rate of the compression scheme, it is possible to (almost) exactly reconstruct the original signal from its compressed version. However, as the rate gets smaller, the reconstructed signal necessarily has very high distortion. The DPLC formulation aims to alleviate this issue by enforcing an additional constraint during the design of the encoder and the decoder of the compression scheme. The constraint requires that irrespective of the rate of the compression the distribution of the reconstructed signal is the same as that of the original signal.

It's too soon to say how the spate of deals between AI companies and publishers will shake out. OpenAI has already scored one clear win, though: Its web crawlers aren't getting blocked by top news outlets at the rate they once were. The generative AI boom sparked a gold rush for data--and a subsequent data-protection rush (for most news websites, anyway) in which publishers sought to block AI crawlers and prevent their work from becoming training data without consent. When Apple debuted a new AI agent this summer, for example, a slew of top news outlets swiftly opted out of Apple's web scraping using the Robots Exclusion Protocol, or robots.txt, the file that allows webmasters to control bots. There are so many new AI bots on the scene that it can feel like playing whack-a-mole to keep up.