Tootfinder

Generative Representational Learning of Foundation Models for Recommendation
Zheli Zhou, Chenxu Zhu, Jianghao Lin, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu
https://arxiv.org/abs/2506.11999

Generative Representational Learning of Foundation Models for Recommendation
Developing a single foundation model with the capability to excel across diverse tasks has been a long-standing objective in the field of artificial intelligence. As the wave of general-purpose foundation models sweeps across various domains, their influence has significantly extended to the field of recommendation systems. While recent efforts have explored recommendation foundation models for various generative tasks, they often overlook crucial embedding tasks and struggle with the complexit…

Score-based Generative Diffusion Models to Synthesize Full-dose FDG Brain PET from MRI in Epilepsy Patients
Jiaqi Wu, Jiahong Ouyang, Farshad Moradi, Mohammad Mehdi Khalighi, Greg Zaharchuk
https://arxiv.org/abs/2506.11297

Score-based Generative Diffusion Models to Synthesize Full-dose FDG Brain PET from MRI in Epilepsy Patients
Fluorodeoxyglucose (FDG) PET to evaluate patients with epilepsy is one of the most common applications for simultaneous PET/MRI, given the need to image both brain structure and metabolism, but is suboptimal due to the radiation dose in this young population. Little work has been done synthesizing diagnostic quality PET images from MRI data or MRI data with ultralow-dose PET using advanced generative AI methods, such as diffusion models, with attention to clinical evaluations tailored for the e…

SceneCompleter: Dense 3D Scene Completion for Generative Novel View Synthesis
Weiliang Chen, Jiayi Bi, Yuanhui Huang, Wenzhao Zheng, Yueqi Duan
https://arxiv.org/abs/2506.10981

SceneCompleter: Dense 3D Scene Completion for Generative Novel View Synthesis
Generative models have gained significant attention in novel view synthesis (NVS) by alleviating the reliance on dense multi-view captures. However, existing methods typically fall into a conventional paradigm, where generative models first complete missing areas in 2D, followed by 3D recovery techniques to reconstruct the scene, which often results in overly smooth surfaces and distorted geometry, as generative models struggle to infer 3D structure solely from RGB data. In this paper, we propo…

Collaborative Prediction: To Join or To Disjoin Datasets
Kyung Rok Kim, Yansong Wang, Xiaocheng Li, Guanting Chen
https://arxiv.org/abs/2506.11271 https://…

Collaborative Prediction: To Join or To Disjoin Datasets
With the recent rise of generative Artificial Intelligence (AI), the need of selecting high-quality dataset to improve machine learning models has garnered increasing attention. However, some part of this topic remains underexplored, even for simple prediction models. In this work, we study the problem of developing practical algorithms that select appropriate dataset to minimize population loss of our prediction model with high probability. Broadly speaking, we investigate when datasets from d…

Fusion of multi-source precipitation records via coordinate-based generative model
Sencan Sun, Congyi Nai, Baoxiang Pan, Wentao Li, Xin Li, Efi Foufoula-Georgiou, Yanluan Lin
https://arxiv.org/abs/2506.11698

Fusion of multi-source precipitation records via coordinate-based generative model
Precipitation remains one of the most challenging climate variables to observe and predict accurately. Existing datasets face intricate trade-offs: gauge observations are relatively trustworthy but sparse, satellites provide global coverage with retrieval uncertainties, and numerical models offer physical consistency but are biased and computationally intensive. Here we introduce PRIMER (Precipitation Record Infinite MERging), a deep generative framework that fuses these complementary sources t…

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
Zuzanna Skorniewska, Bartlomiej W. Papiez
https://arxiv.org/abs/2506.11753

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
The adoption of neural network models in medical imaging has been constrained by strict privacy regulations, limited data availability, high acquisition costs, and demographic biases. Deep generative models offer a promising solution by generating synthetic data that bypasses privacy concerns and addresses fairness by producing samples for under-represented groups. However, unlike natural images, medical imaging requires validation not only for fidelity (e.g., Fréchet Inception Score) but also…

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation
Bowen Zheng, Tianming Yang
https://arxiv.org/abs/2506.09376 https://

Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation
Diffusion distillation is a widely used technique to reduce the sampling cost of diffusion models, yet it often requires extensive training, and the student performance tends to be degraded. Recent studies show that incorporating a GAN objective may alleviate these issues, yet the underlying mechanism remains unclear. In this work, we first identify a key limitation of distillation: mismatched step sizes and parameter numbers between the teacher and the student model lead them to converge to di…

Efficient Speech Enhancement via Embeddings from Pre-trained Generative Audioencoders
Xingwei Sun, Heinrich Dinkel, Yadong Niu, Linzhang Wang, Junbo Zhang, Jian Luan
https://arxiv.org/abs/2506.11514

Efficient Speech Enhancement via Embeddings from Pre-trained Generative Audioencoders
Recent research has delved into speech enhancement (SE) approaches that leverage audio embeddings from pre-trained models, diverging from time-frequency masking or signal prediction techniques. This paper introduces an efficient and extensible SE method. Our approach involves initially extracting audio embeddings from noisy speech using a pre-trained audioencoder, which are then denoised by a compact encoder network. Subsequently, a vocoder synthesizes the clean speech from denoised embeddings.…

The Role of Generative AI in Facilitating Social Interactions: A Scoping Review
T. T. J. E. Arets, G. Perugia, M. Houben, W. A. IJsselsteijn
https://arxiv.org/abs/2506.10927

The Role of Generative AI in Facilitating Social Interactions: A Scoping Review
Reduced social connectedness increasingly poses a threat to mental health, life expectancy, and general well-being. Generative AI (GAI) technologies, such as large language models (LLMs) and image generation tools, are increasingly integrated into applications aimed at enhancing human social experiences. Despite their growing presence, little is known about how these technologies influence social interactions. This scoping review investigates how GAI-based applications are currently designed to…

A Review on Score-based Generative Models for Audio Applications
Ge Zhu, Yutong Wen, Zhiyao Duan
https://arxiv.org/abs/2506.08457 https://

A Review on Score-based Generative Models for Audio Applications
Diffusion models have emerged as powerful deep generative techniques, producing high-quality and diverse samples in applications in various domains including audio. These models have many different design choices suitable for different applications, however, existing reviews lack in-depth discussions of these design choices. The audio diffusion model literature also lacks principled guidance for the implementation of these design choices and their comparisons for different applications. This su…

Generative Models of 21cm EoR Lightcones with 3D Scattering Transforms
Ian Hothi, Erwan Allys, Benoit Semelin, Romain Meriot
https://arxiv.org/abs/2506.10061

Generative Models of 21cm EoR Lightcones with 3D Scattering Transforms
The 21cm signal from the Epoch of Reionization (EoR) is observed as a three-dimensional data set known as a lightcone, consisting of a redshift (frequency) axis and two spatial sky plane axes. When observed by radio interferometers, this EoR signal is strongly obscured by foregrounds that are several orders of magnitude stronger. Due to its inherently non-Gaussian nature, the EoR signal requires robust statistical tools to accurately separate it from these foreground contaminants, but current f…

Quantifying Mix Network Privacy Erosion with Generative Models
Vasilios Mavroudis, Tariq Elahi
https://arxiv.org/abs/2506.08918 https://

Quantifying Mix Network Privacy Erosion with Generative Models
Modern mix networks improve over Tor and provide stronger privacy guarantees by robustly obfuscating metadata. As long as a message is routed through at least one honest mixnode, the privacy of the users involved is safeguarded. However, the complexity of the mixing mechanisms makes it difficult to estimate the cumulative privacy erosion occurring over time. This work uses a generative model trained on mixnet traffic to estimate the loss of privacy when users communicate persistently over a per…

Time-Unified Diffusion Policy with Action Discrimination for Robotic Manipulation
Ye Niu, Sanping Zhou, Yizhe Li, Ye Den, Le Wang
https://arxiv.org/abs/2506.09422

Time-Unified Diffusion Policy with Action Discrimination for Robotic Manipulation
In many complex scenarios, robotic manipulation relies on generative models to estimate the distribution of multiple successful actions. As the diffusion model has better training robustness than other generative models, it performs well in imitation learning through successful robot demonstrations. However, the diffusion-based policy methods typically require significant time to iteratively denoise robot actions, which hinders real-time responses in robotic manipulation. Moreover, existing dif…

Generative Models for Parameter Space Reduction applied to Reduced Order Modelling
Guglielmo Padula, Gianluigi Rozza
https://arxiv.org/abs/2506.09721 https…

Generative Models for Parameter Space Reduction applied to Reduced Order Modelling
Solving and optimising Partial Differential Equations (PDEs) in geometrically parameterised domains often requires iterative methods, leading to high computational and time complexities. One potential solution is to learn a direct mapping from the parameters to the PDE solution. Two prominent methods for this are Data-driven Non-Intrusive Reduced Order Models (DROMs) and Parametrised Physics Informed Neural Networks (PPINNs). However, their accuracy tends to degrade as the number of geometric p…

Multimodal Large Language Models: A Survey
Longzhen Han, Awes Mubarak, Almas Baimagambetov, Nikolaos Polatidis, Thar Baker
https://arxiv.org/abs/2506.10016

Multimodal Large Language Models: A Survey
Multimodal Large Language Models (MLLMs) have rapidly evolved beyond text generation, now spanning diverse output modalities including images, music, video, human motion, and 3D objects, by integrating language with other sensory modalities under unified architectures. This survey categorises six primary generative modalities and examines how foundational techniques, namely Self-Supervised Learning (SSL), Mixture of Experts (MoE), Reinforcement Learning from Human Feedback (RLHF), and Chain-of-…

Measuring Semantic Information Production in Generative Diffusion Models
Florian Handke, F\'elix Koulischer, Gabriel Raya, Luca Ambrogioni
https://arxiv.org/abs/2506.10433

Measuring Semantic Information Production in Generative Diffusion Models
It is well known that semantic and structural features of the generated images emerge at different times during the reverse dynamics of diffusion, a phenomenon that has been connected to physical phase transitions in magnets and other materials. In this paper, we introduce a general information-theoretic approach to measure when these class-semantic "decisions" are made during the generative process. By using an online formula for the optimal Bayesian classifier, we estimate the conditional ent…

G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration
Samuel Holt, Max Ruiz Luyten, Antonin Berthon, Mihaela van der Schaar
https://arxiv.org/abs/2506.09272

G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration
Constructing robust simulators is essential for asking "what if?" questions and guiding policy in critical domains like healthcare and logistics. However, existing methods often struggle, either failing to generalize beyond historical data or, when using Large Language Models (LLMs), suffering from inaccuracies and poor empirical alignment. We introduce G-Sim, a hybrid framework that automates simulator construction by synergizing LLM-driven structural design with rigorous empirical calibration…

Adultification Bias in LLMs and Text-to-Image Models
Jane Castleman, Aleksandra Korolova
https://arxiv.org/abs/2506.07282 https://arx…

Adultification Bias in LLMs and Text-to-Image Models
The rapid adoption of generative AI models in domains such as education, policing, and social media raises significant concerns about potential bias and safety issues, particularly along protected attributes, such as race and gender, and when interacting with minors. Given the urgency of facilitating safe interactions with AI systems, we study bias along axes of race and gender in young girls. More specifically, we focus on "adultification bias," a phenomenon in which Black girls are presumed t…

Diffusion Sequence Models for Enhanced Protein Representation and Generation
Logan Hallee, Nikolaos Rafailidis, David B. Bichara, Jason P. Gleghorn
https://arxiv.org/abs/2506.08293

Diffusion Sequence Models for Enhanced Protein Representation and Generation
Proteins are fundamental to biology, executing diverse functions through complex physicochemical interactions, and they hold transformative potential across medicine, materials science, and environmental applications. Protein Language Models (pLMs) aim to unlock insights from the vast space of unlabeled protein sequences by learning rich, semantic representations from primary sequences via masked language modeling. However, these models typically exhibit limited generative capacity. In this wor…

IGSM: Improved Geometric and Sensitivity Matching for Finetuning Pruned Diffusion Models
Caleb Zheng, Eli Shlizerman
https://arxiv.org/abs/2506.05398 https…

IGSM: Improved Geometric and Sensitivity Matching for Finetuning Pruned Diffusion Models
Diffusion models achieve realistic outcomes across a wide range of generative tasks, but their high computational cost remains a major barrier to deployment. Model pruning has emerged as a promising strategy to reduce inference cost and enable lightweight diffusion models. While effective, pruned diffusion models are proned to quality reduction due to limited capacity. A key limitation of current pruning approaches is that pruned models are finetuned using the same objective as the dense model,…

Quantum Adiabatic Generation of Human-Like Passwords
Sascha M\"ucke, Raoul Heese, Thore Gerlach, David Biesner, Loong Kuan Lee, Nico Piatkowski
https://arxiv.org/abs/2506.08917

Quantum Adiabatic Generation of Human-Like Passwords
Generative Artificial Intelligence (GenAI) for Natural Language Processing (NLP) is the predominant AI technology to date. An important perspective for Quantum Computing (QC) is the question whether QC has the potential to reduce the vast resource requirements for training and operating GenAI models. While large-scale generative NLP tasks are currently out of reach for practical quantum computers, the generation of short semantic structures such as passwords is not. Generating passwords that mi…

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
Stefan Dascalescu, Adrian Marius Dumitran, Mihai Alexandru Vasiluta
https://arxiv.org/abs/2506.05990

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
Competitive programming contests play a crucial role in cultivating computational thinking and algorithmic skills among learners. However, generating comprehensive test cases to effectively assess programming solutions remains resource-intensive and challenging for educators. This paper introduces an innovative NLP-driven method leveraging generative AI (large language models) to automate the creation of high-quality test cases for competitive programming assessments. We extensively evaluated o…

Data-Driven High-Dimensional Statistical Inference with Generative Models
Oz Amram, Manuel Szewc
https://arxiv.org/abs/2506.06438 https://

Data-Driven High-Dimensional Statistical Inference with Generative Models
Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds, which is complicated by the poor modeling of many of the crucial backgrounds in Monte Carlo simulations. In this work, we introduce HI-SIGMA, a method to perform unbinned high-dimensional statistical inference with data-driven background distributions. In contradistinction to many applications of Simulation Based Inference in High Energy Physics, H…

Opportunities and Applications of GenAI in Smart Cities: A User-Centric Survey
Ankit Shetgaonkar, Dipen Pradhan, Lakshit Arora, Sanjay Surendranath Girija, Shashank Kapoor, Aman Raj
https://arxiv.org/abs/2505.08034

Opportunities and Applications of GenAI in Smart Cities: A User-Centric Survey
The proliferation of IoT in cities, combined with Digital Twins, creates a rich data foundation for Smart Cities aimed at improving urban life and operations. Generative AI (GenAI) significantly enhances this potential, moving beyond traditional AI analytics and predictions by processing multimodal content and generating novel outputs like text and simulations. Using specialized or foundational models, GenAI's natural language abilities such as Natural Language Understanding (NLU) and Natural L…

This https://arxiv.org/abs/2502.12096 has been replaced.
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Token Communications: A Unified Framework for Cross-modal Context-aware Semantic Communications
In this paper, we introduce token communications (TokCom), a large model-driven framework to leverage cross-modal context information in generative semantic communications (GenSC). TokCom is a new paradigm, motivated by the recent success of generative foundation models and multimodal large language models (GFM/MLLMs), where the communication units are tokens, enabling efficient transformer-based token processing at the transmitter and receiver. In this paper, we introduce the potential opportu…

Optical Physics-Based Generative Models
Amirreza Ahmadnejad, Somayyeh Koohi
https://arxiv.org/abs/2506.04357 https://arxiv.org/pdf/25…

Optical Physics-Based Generative Models
This paper establishes a comprehensive mathematical framework connecting optical physics equations to generative models, demonstrating how light propagation dynamics inspire powerful artificial intelligence approaches. We analyze six fundamental optical equations, comparing linear models (Helmholtz, dissipative wave, and Eikonal equations) with their nonlinear extensions incorporating Kerr effects, cubic-quintic nonlinearities, and intensity-dependent refractive indices. Our nonlinear optical m…

What does the death of Julius Caesar and probability have to do with the future of knowledge? Join me at noon EDT today for a webinar on how AI, like a JPEG, compresses information, risking a future where history is endlessly rewritten https://maine.zoom.us/webinar/register

ArcNeural: A Multi-Modal Database for the Gen-AI Era
Wu Min, Qiao Yuncong, Yu Tan, Chenghu Yang
https://arxiv.org/abs/2506.09467 https://

ArcNeural: A Multi-Modal Database for the Gen-AI Era
ArcNeural introduces a novel multimodal database tailored for the demands of Generative AI and Large Language Models, enabling efficient management of diverse data types such as graphs, vectors, and documents. Its storage-compute separated architecture integrates graph technology, advanced vector indexing, and transaction processing to support real-time analytics and AI-driven applications. Key features include a unified storage layer, adaptive edge collection in MemEngine, and seamless integra…

This https://arxiv.org/abs/2506.00975 has been replaced.
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NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction
Inspired by the impressive capabilities of GPT-4o, there is growing interest in enabling speech language models (SLMs) to engage in natural, fluid spoken interactions with humans. Recent advancements have led to the development of several SLMs that demonstrate promising results in this area. However, current approaches have yet to fully exploit dual-channel speech data, which inherently captures the structure and dynamics of human conversation. In this work, we systematically explore the use of…

Humanoid World Models: Open World Foundation Models for Humanoid Robotics
Muhammad Qasim Ali, Aditya Sridhar, Shahbuland Matiana, Alex Wong, Mohammad Al-Sharman
https://arxiv.org/abs/2506.01182

Humanoid World Models: Open World Foundation Models for Humanoid Robotics
Humanoid robots have the potential to perform complex tasks in human centered environments but require robust predictive models to reason about the outcomes of their actions. We introduce Humanoid World Models (HWM) a family of lightweight open source video based models that forecast future egocentric observations conditioned on actions. We train two types of generative models Masked Transformers and FlowMatching on 100 hours of humanoid demonstrations. Additionally we explore architectural var…

Understanding Mental Models of Generative Conversational Search and The Effect of Interface Transparency
Chadha Degachi, Samuel Kernan Freire, Evangelos Niforatos, Gerd Kortuem
https://arxiv.org/abs/2506.03807

Understanding Mental Models of Generative Conversational Search and The Effect of Interface Transparency
The experience and adoption of conversational search is tied to the accuracy and completeness of users' mental models -- their internal frameworks for understanding and predicting system behaviour. Thus, understanding these models can reveal areas for design interventions. Transparency is one such intervention which can improve system interpretability and enable mental model alignment. While past research has explored mental models of search engines, those of generative conversational search re…

What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
Erik Buchholz, Natasha Fernandes, David D. Nguyen, Alsharif Abuadbba, Surya Nepal, Salil S. Kanhere
https://arxiv.org/abs/2506.09312

What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
While location trajectories offer valuable insights, they also reveal sensitive personal information. Differential Privacy (DP) offers formal protection, but achieving a favourable utility-privacy trade-off remains challenging. Recent works explore deep learning-based generative models to produce synthetic trajectories. However, current models lack formal privacy guarantees and rely on conditional information derived from real data during generation. This work investigates the utility cost of e…

FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models
Xuan Xu, Fufang Wen, Beilin Chu, Zhibing Fu, Qinhong Lin, Jiaqi Liu, Binjie Fei, Zhongliang Yang, Linna Zhou, Yu Li
https://arxiv.org/abs/2506.06335

FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models
In natural language processing (NLP), the focus has shifted from encoder-only tiny language models like BERT to decoder-only large language models(LLMs) such as GPT-3. However, LLMs' practical application in the financial sector has revealed three limitations: (1) LLMs often perform worse than fine-tuned BERT on discriminative tasks despite costing much higher computational resources, such as market sentiment analysis in financial reports; (2) Application on generative tasks heavily relies on r…

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds
Vahan Arsenyan, Elen Vardanyan, Arnak Dalalyan
https://arxiv.org/abs/2506.09681

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds
Generative modeling aims to produce new random examples from an unknown target distribution, given access to a finite collection of examples. Among the leading approaches, denoising diffusion probabilistic models (DDPMs) construct such examples by mapping a Brownian motion via a diffusion process driven by an estimated score function. In this work, we first provide empirical evidence that DDPMs are robust to constant-variance noise in the score evaluations. We then establish finite-sample guara…

Diffusion prior as a direct regularization term for FWI
Yuke Xie, Herv\'e Chauris, Nicolas Desassis
https://arxiv.org/abs/2506.10141 https://

Diffusion prior as a direct regularization term for FWI
Diffusion models have recently shown promise as powerful generative priors for inverse problems. However, conventional applications require solving the full reverse diffusion process and operating on noisy intermediate states, which poses challenges for physics-constrained computational seismic imaging. In particular, such instability is pronounced in non-linear solvers like those used in Full Waveform Inversion (FWI), where wave propagation through noisy velocity fields can lead to numerical a…

A theoretical basis for model collapse in recursive training
Vivek Shripad Borkar
https://arxiv.org/abs/2506.09401 https://arxiv.org/…

A theoretical basis for model collapse in recursive training
It is known that recursive training from generative models can lead to the so called `collapse' of the simulated probability distribution. This note shows that one in fact gets two different asymptotic behaviours depending on whether an external source, howsoever minor, is also contributing samples.

This https://arxiv.org/abs/2505.21677 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

What happens when generative AI models train recursively on each others' generated outputs?
The internet is full of AI-generated content while also serving as a common source of training data for generative AI (genAI) models. This duality raises the possibility that future genAI models may be trained on other models' generated outputs. Prior work has studied consequences of models training on their own generated outputs, but limited work has considered what happens if models ingest content produced by other models. Given society's increasing dependence on genAI tools, understanding do…

An Exploratory Framework for Future SETI Applications: Detecting Generative Reactivity via Language Models
Po-Chieh Yu
#toXiv_bot_toot

New preprint "Survey on the Evaluation of Generative Models in Music:" https://arxiv.org/abs/2506.05104
Despite the rapid progress in generative music models, evaluating these systems remains a major challenge. In this survey, we:
✅ Outline key evaluation targets
✅ Compare methodolog…

Survey on the Evaluation of Generative Models in Music
Research on generative systems in music has seen considerable attention and growth in recent years. A variety of attempts have been made to systematically evaluate such systems. We provide an interdisciplinary review of the common evaluation targets, methodologies, and metrics for the evaluation of both system output and model usability, covering subjective and objective approaches, qualitative and quantitative approaches, as well as empirical and computational methods. We discuss the advantage…

GenIR: Generative Visual Feedback for Mental Image Retrieval
Diji Yang, Minghao Liu, Chung-Hsiang Lo, Yi Zhang, James Davis
https://arxiv.org/abs/2506.06220

GenIR: Generative Visual Feedback for Mental Image Retrieval
Vision-language models (VLMs) have shown strong performance on text-to-image retrieval benchmarks. However, bridging this success to real-world applications remains a challenge. In practice, human search behavior is rarely a one-shot action. Instead, it is often a multi-round process guided by clues in mind, that is, a mental image ranging from vague recollections to vivid mental representations of the target image. Motivated by this gap, we study the task of Mental Image Retrieval (MIR), which…

University of Chicago Becker-Friedman Institute of Economics just published research this April 2025 of how Generative AI has had "almost no significant wage or labor impact so far".
✅ Large Language Models, Small Labor Market Effects
https://papers.ssrn.com/sol3/papers.cf

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad, Aditya Sant, David J. Love, Christopher G. Brinton
https://arxiv.org/abs/2506.00374

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of realistic wireless channel datasets, which are extremely resource-intensive to produce. To this end, the generation of realistic wireless channels plays a key role in the subsequent design of effective ML algorithms for wireless communication systems. Generativ…

Benchmarking Large Language Models for Polymer Property Predictions
Sonakshi Gupta, Akhlak Mahmood, Shivank Shukla, Rampi Ramprasad
https://arxiv.org/abs/2506.02129

Benchmarking Large Language Models for Polymer Property Predictions
Machine learning has revolutionized polymer science by enabling rapid property prediction and generative design. Large language models (LLMs) offer further opportunities in polymer informatics by simplifying workflows that traditionally rely on large labeled datasets, handcrafted representations, and complex feature engineering. LLMs leverage natural language inputs through transfer learning, eliminating the need for explicit fingerprinting and streamlining training. In this study, we finetune …

This https://arxiv.org/abs/2310.17451 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings
Making neural visual generative models controllable by logical reasoning systems is promising for improving faithfulness, transparency, and generalizability. We propose the Abductive visual Generation (AbdGen) approach to build such logic-integrated models. A vector-quantized symbol grounding mechanism and the corresponding disentanglement training method are introduced to enhance the controllability of logical symbols over generation. Furthermore, we propose two logical abduction methods to ma…

Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars
Oliver Slumbers, Joel Z. Leibo, Marco A. Janssen
https://arxiv.org/abs/2506.05555

Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars
Collective risk social dilemmas (CRSD) highlight a trade-off between individual preferences and the need for all to contribute toward achieving a group objective. Problems such as climate change are in this category, and so it is critical to understand their social underpinnings. However, rigorous CRSD methodology often demands large-scale human experiments but it is difficult to guarantee sufficient power and heterogeneity over socio-demographic factors. Generative AI offers a potential comple…

A Deep Generative Model for the Simulation of Discrete Karst Networks
Dany Lauzon, Julien Straubhaar, Philippe Renard
https://arxiv.org/abs/2506.09832 http…

A Deep Generative Model for the Simulation of Discrete Karst Networks
The simulation of discrete karst networks presents a significant challenge due to the complexity of the physicochemical processes occurring within various geological and hydrogeological contexts over extended periods. This complex interplay leads to a wide variety of karst network patterns, each intricately linked to specific hydrogeological conditions. We explore a novel approach that represents karst networks as graphs and applies graph generative models (deep learning techniques) to capture …

This https://arxiv.org/abs/2312.01602 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum Time Series Similarity Measures and Quantum Temporal Kernels
This article presents a quantum computing approach to designing of similarity measures and kernels for classification of stochastic symbolic time series. In the area of machine learning, kernels are important components of various similarity-based classification, clustering, and regression algorithms. An effective strategy for devising problem-specific kernels is leveraging existing generative models of the example space. In this study we assume that a quantum generative model, known as quantum…

ME: Trigger Element Combination Backdoor Attack on Copyright Infringement
Feiyu Yang, Siyuan Liang, Aishan Liu, Dacheng Tao
https://arxiv.org/abs/2506.10776

ME: Trigger Element Combination Backdoor Attack on Copyright Infringement
The capability of generative diffusion models (DMs) like Stable Diffusion (SD) in replicating training data could be taken advantage of by attackers to launch the Copyright Infringement Attack, with duplicated poisoned image-text pairs. SilentBadDiffusion (SBD) is a method proposed recently, which shew outstanding performance in attacking SD in text-to-image tasks. However, the feasible data resources in this area are still limited, some of them are even constrained or prohibited due to the iss…

Cross-Modal Characterization of Thin Film MoS$_2$ Using Generative Models
Isaiah A. Moses, Chen Chen, Joan M. Redwing, Wesley F. Reinhart
https://arxiv.org/abs/2505.24065

Cross-Modal Characterization of Thin Film MoS$_2$ Using Generative Models
The growth and characterization of materials using empirical optimization typically requires a significant amount of expert time, experience, and resources. Several complementary characterization methods are routinely performed to determine the quality and properties of a grown sample. Machine learning (ML) can support the conventional approaches by using historical data to guide and provide speed and efficiency to the growth and characterization of materials. Specifically, ML can provide quant…

Research on Aerodynamic Performance Prediction of Airfoils Based on a Fusion Algorithm of Transformer and GAN
MaolinYang, Yaohui Wang, Pingyu Jiang
https://arxiv.org/abs/2506.06979

Research on Aerodynamic Performance Prediction of Airfoils Based on a Fusion Algorithm of Transformer and GAN
Predicting of airfoil aerodynamic performance is a key part of aircraft design optimization, but the traditional methods (such as wind tunnel test and CFD simulation) have the problems of high cost and low efficiency, and the existing data-driven models face the challenges of insufficient accuracy and strong data dependence in multi-objective prediction. Therefore, this study proposes a deep learning model, Deeptrans, based on the fusion of improved Transformer and generative Adversarial networ…

This https://arxiv.org/abs/2505.16499 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Smaller, Smarter, Closer: The Edge of Collaborative Generative AI
The rapid adoption of generative AI (GenAI), particularly Large Language Models (LLMs), has exposed critical limitations of cloud-centric deployments, including latency, cost, and privacy concerns. Meanwhile, Small Language Models (SLMs) are emerging as viable alternatives for resource-constrained edge environments, though they often lack the capabilities of their larger counterparts. This article explores the potential of collaborative inference systems that leverage both edge and cloud resour…

Survey on the Evaluation of Generative Models in Music
Alexander Lerch, Claire Arthur, Nick Bryan-Kinns, Corey Ford, Qianyi Sun, Ashvala Vinay
https://arxiv.org/abs/2506.05104

Survey on the Evaluation of Generative Models in Music
Research on generative systems in music has seen considerable attention and growth in recent years. A variety of attempts have been made to systematically evaluate such systems. We provide an interdisciplinary review of the common evaluation targets, methodologies, and metrics for the evaluation of both system output and model usability, covering subjective and objective approaches, qualitative and quantitative approaches, as well as empirical and computational methods. We discuss the advantage…

ENMA: Tokenwise Autoregression for Generative Neural PDE Operators
Armand Kassa\"i Koupa\"i, Lise Le Boudec, Louis Serrano, Patrick Gallinari
https://arxiv.org/abs/2506.06158

ENMA: Tokenwise Autoregression for Generative Neural PDE Operators
Solving time-dependent parametric partial differential equations (PDEs) remains a fundamental challenge for neural solvers, particularly when generalizing across a wide range of physical parameters and dynamics. When data is uncertain or incomplete-as is often the case-a natural approach is to turn to generative models. We introduce ENMA, a generative neural operator designed to model spatio-temporal dynamics arising from physical phenomena. ENMA predicts future dynamics in a compressed latent …

Ego-centric Learning of Communicative World Models for Autonomous Driving
Hang Wang, Dechen Gao, Junshan Zhang
https://arxiv.org/abs/2506.08149 https://

Ego-centric Learning of Communicative World Models for Autonomous Driving
We study multi-agent reinforcement learning (MARL) for tasks in complex high-dimensional environments, such as autonomous driving. MARL is known to suffer from the \textit{partial observability} and \textit{non-stationarity} issues. To tackle these challenges, information sharing is often employed, which however faces major hurdles in practice, including overwhelming communication overhead and scalability concerns. By making use of generative AI embodied in world model together with its latent …

This https://arxiv.org/abs/2504.12684 has been replaced.
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SOPHY: Learning to Generate Simulation-Ready Objects with Physical Materials
We present SOPHY, a generative model for 3D physics-aware shape synthesis. Unlike existing 3D generative models that focus solely on static geometry or 4D models that produce physics-agnostic animations, our method jointly synthesizes shape, texture, and material properties related to physics-grounded dynamics, making the generated objects ready for simulations and interactive, dynamic environments. To train our model, we introduce a dataset of 3D objects annotated with detailed physical materi…

This https://arxiv.org/abs/2505.01146 has been replaced.
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Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications
Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks. However, their application in the biomedical domain presents unique challenges, particularly regarding factual accuracy and up-to-date knowledge integration. Retrieval Augmented Generation (RAG) has emerged as a promising solution to address these challenges by combining the generative capabilities of LLMs with external knowledge retrieval. This comprehensive survey ex…

polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design
Anagha Savit, Harikrishna Sahu, Shivank Shukla, Wei Xiong, Rampi Ramprasad
https://arxiv.org/abs/2506.04233

polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design
Designing polymers for targeted applications and accurately predicting their properties is a key challenge in materials science owing to the vast and complex polymer chemical space. While molecular language models have proven effective in solving analogous problems for molecular discovery, similar advancements for polymers are limited. To address this gap, we propose polyBART, a language model-driven polymer discovery capability that enables rapid and accurate exploration of the polymer design …

This https://arxiv.org/abs/2503.18941 has been replaced.
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Exploring Training and Inference Scaling Laws in Generative Retrieval
Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models (LLMs) generate target documents directly from a query. As a novel paradigm, the mechanisms that underpin its performance and scalability remain largely unexplored. We systematically investigate training and inference scaling laws in generative retrieval, exploring how model size, training data scale, and inference-time compute jointly influence performance. We propose a novel evaluatio…

This https://arxiv.org/abs/2405.08965 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

Meaning-Typed Programming: Language Abstraction and Runtime for Model-Integrated Applications
Software development is shifting from traditional logical programming to model-integrated applications that leverage generative AI and large language models (LLMs) during runtime. However, integrating LLMs remains complex, requiring developers to manually craft prompts and process outputs. Existing tools attempt to assist with prompt engineering, but often introduce additional complexity. This paper presents Meaning-Typed Programming (MTP) model, a novel paradigm that abstracts LLM integratio…

Generative Social Choice: The Next Generation
Niclas Boehmer, Sara Fish, Ariel D. Procaccia
https://arxiv.org/abs/2505.22939 https://…

Generative Social Choice: The Next Generation
A key task in certain democratic processes is to produce a concise slate of statements that proportionally represents the full spectrum of user opinions. This task is similar to committee elections, but unlike traditional settings, the candidate set comprises all possible statements of varying lengths, and so it can only be accessed through specific queries. Combining social choice and large language models, prior work has approached this challenge through a framework of generative social choic…

Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models
Chaoyi Zhu, Zaitang Li, Renyi Yang, Robert Birke, Pin-Yu Chen, Tsung-Yi Ho, Lydia Y. Chen
https://arxiv.org/abs/2506.06018

Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models
Watermarking becomes one of the pivotal solutions to trace and verify the origin of synthetic images generated by artificial intelligence models, but it is not free of risks. Recent studies demonstrate the capability to forge watermarks from a target image onto cover images via adversarial optimization without knowledge of the target generative model and watermark schemes. In this paper, we uncover a greater risk of an optimization-free and universal watermark forgery that harnesses existing re…

Surgeons Awareness, Expectations, and Involvement with Artificial Intelligence: a Survey Pre and Post the GPT Era
Lorenzo Arboit, Dennis N. Schneider, Toby Collins, Daniel A. Hashimoto, Silvana Perretta, Bernard Dallemagne, Jacques Marescaux, EAES Working Group, Nicolas Padoy, Pietro Mascagni
https://arxiv.org/abs/2506.08258

Surgeons Awareness, Expectations, and Involvement with Artificial Intelligence: a Survey Pre and Post the GPT Era
Artificial Intelligence (AI) is transforming medicine, with generative AI models like ChatGPT reshaping perceptions of its potential. This study examines surgeons' awareness, expectations, and involvement with AI in surgery through comparative surveys conducted in 2021 and 2024. Two cross-sectional surveys were distributed globally in 2021 and 2024, the first before an IRCAD webinar and the second during the annual EAES meeting. The surveys assessed demographics, AI awareness, expectations, inv…

Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems
Sven Kirchner, Alois C. Knoll
https://arxiv.org/abs/2506.04038

Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems
Developing safety-critical automotive software presents significant challenges due to increasing system complexity and strict regulatory demands. This paper proposes a novel framework integrating Generative Artificial Intelligence (GenAI) into the Software Development Lifecycle (SDLC). The framework uses Large Language Models (LLMs) to automate code generation in languages such as C++, incorporating safety-focused practices such as static verification, test-driven development and iterative refi…

This https://arxiv.org/abs/2503.17770 has been replaced.
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Probabilistic Net Load Forecasting for High-Penetration RES Grids Utilizing Enhanced Conditional Diffusion Model
The proliferation of intermittent distributed renewable energy sources (RES) in modern power systems has fundamentally compromised the reliability and accuracy of deterministic net load forecasting. Generative models, particularly diffusion models, demonstrate exceptional potential in uncertainty quantification for scenario forecasting. Nevertheless, their probabilistic predictive capabilities and conditional bootstrapping mechanisms still remain underexplored. In this paper, a day-ahead probab…

Are We Generalizing from the Exception? An In-the-Wild Study on Group-Sensitive Conversation Design in Human-Agent Interactions
Ana M\"uller, Sabina Jeschke, Anja Richert
https://arxiv.org/abs/2506.10462

Are We Generalizing from the Exception? An In-the-Wild Study on Group-Sensitive Conversation Design in Human-Agent Interactions
This paper investigates the impact of a group-adaptive conversation design in two socially interactive agents (SIAs) through two real-world studies. Both SIAs - Furhat, a social robot, and MetaHuman, a virtual agent - were equipped with a conversational artificial intelligence (CAI) backend combining hybrid retrieval and generative models. The studies were carried out in an in-the-wild setting with a total of $N = 188$ participants who interacted with the SIAs - in dyads, triads or larger group…

PixCell: A generative foundation model for digital histopathology images
Srikar Yellapragada, Alexandros Graikos, Zilinghan Li, Kostas Triaridis, Varun Belagali, Saarthak Kapse, Tarak Nath Nandi, Ravi K Madduri, Prateek Prasanna, Tahsin Kurc, Rajarsi R. Gupta, Joel Saltz, Dimitris Samaras
https://arxiv.org/abs/2506.05127

PixCell: A generative foundation model for digital histopathology images
The digitization of histology slides has revolutionized pathology, providing massive datasets for cancer diagnosis and research. Contrastive self-supervised and vision-language models have been shown to effectively mine large pathology datasets to learn discriminative representations. On the other hand, generative models, capable of synthesizing realistic and diverse images, present a compelling solution to address unique problems in pathology that involve synthesizing images; overcoming annota…

GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation
Krishna Acharya, Aleksandr V. Petrov, Juba Ziani
https://arxiv.org/abs/2506.01910

GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation
We propose Generative Low-rank language model with Semantic Search (GLoSS), a generative recommendation framework that combines large language models with dense retrieval for sequential recommendation. Unlike prior methods such as GPT4Rec, which rely on lexical matching via BM25, GLoSS uses semantic search to retrieve relevant items beyond lexical matching. For query generation, we employ 4-bit quantized LlaMA-3 models fine-tuned with low-rank adaptation (LoRA), enabling efficient training and …

A Quantum Information Theoretic Approach to Tractable Probabilistic Models
Pedro Zuidberg Dos Martires
https://arxiv.org/abs/2506.01824 https://

A Quantum Information Theoretic Approach to Tractable Probabilistic Models
By recursively nesting sums and products, probabilistic circuits have emerged in recent years as an attractive class of generative models as they enjoy, for instance, polytime marginalization of random variables. In this work we study these machine learning models using the framework of quantum information theory, leading to the introduction of positive unital circuits (PUnCs), which generalize circuit evaluations over positive real-valued probabilities to circuit evaluations over positive semi…

Few-step Adversarial Schr\"{o}dinger Bridge for Generative Speech Enhancement
Seungu Han, Sungho Lee, Juheon Lee, Kyogu Lee
https://arxiv.org/abs/2506.01460

Few-step Adversarial Schrödinger Bridge for Generative Speech Enhancement
Deep generative models have recently been employed for speech enhancement to generate perceptually valid clean speech on large-scale datasets. Several diffusion models have been proposed, and more recently, a tractable Schrödinger Bridge has been introduced to transport between the clean and noisy speech distributions. However, these models often suffer from an iterative reverse process and require a large number of sampling steps -- more than 50. Our investigation reveals that the performance…

Machine vs Machine: Using AI to Tackle Generative AI Threats in Assessment
Mohammad Saleh Torkestani, Taha Mansouri
https://arxiv.org/abs/2506.02046 https:…

Machine vs Machine: Using AI to Tackle Generative AI Threats in Assessment
This paper presents a theoretical framework for addressing the challenges posed by generative artificial intelligence (AI) in higher education assessment through a machine-versus-machine approach. Large language models like GPT-4, Claude, and Llama increasingly demonstrate the ability to produce sophisticated academic content, traditional assessment methods face an existential threat, with surveys indicating 74-92% of students experimenting with these tools for academic purposes. Current respon…

MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection
Liancheng Fang, Aiwei Liu, Henry Peng Zou, Yankai Chen, Hengrui Zhang, Zhongfen Deng, Philip S. Yu
https://arxiv.org/abs/2505.24267

MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection
We introduce MUSE, a watermarking algorithm for tabular generative models. Previous approaches typically leverage DDIM invertibility to watermark tabular diffusion models, but tabular diffusion models exhibit significantly poorer invertibility compared to other modalities, compromising performance. Simultaneously, tabular diffusion models require substantially less computation than other modalities, enabling a multi-sample selection approach to tabular generative model watermarking. MUSE embeds…

This https://arxiv.org/abs/2402.12208 has been replaced.
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Language-Codec: Bridging Discrete Codec Representations and Speech Language Models
In recent years, large language models have achieved significant success in generative tasks related to speech, audio, music, and other signal domains. A crucial element of these models is the discrete acoustic codecs, which serve as an intermediate representation replacing the mel-spectrogram. However, there exist several gaps between discrete codecs and downstream speech language models. Specifically, 1) Due to the reconstruction paradigm of the Codec model and the structure of residual vecto…

This https://arxiv.org/abs/2503.02407 has been replaced.
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Wyckoff Transformer: Generation of Symmetric Crystals
Crystal symmetry plays a fundamental role in determining its physical, chemical, and electronic properties such as electrical and thermal conductivity, optical and polarization behavior, and mechanical strength. Almost all known crystalline materials have internal symmetry. However, this is often inadequately addressed by existing generative models, making the consistent generation of stable and symmetrically valid crystal structures a significant challenge. We introduce WyFormer, a generative …

Model-Driven Graph Contrastive Learning
Ali Azizpour, Nicolas Zilberstein, Santiago Segarra
https://arxiv.org/abs/2506.06212 https://…

Model-Driven Graph Contrastive Learning
We propose $\textbf{MGCL}$, a model-driven graph contrastive learning (GCL) framework that leverages graphons (probabilistic generative models for graphs) to guide contrastive learning by accounting for the data's underlying generative process. GCL has emerged as a powerful self-supervised framework for learning expressive node or graph representations without relying on annotated labels, which are often scarce in real-world data. By contrasting augmented views of graph data, GCL has demonstrat…

This https://arxiv.org/abs/2506.02867 has been replaced.
link: https://scholar.google.com/scholar?q=a

Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning
Large reasoning models (LRMs) have demonstrated impressive capabilities in complex problem-solving, yet their internal reasoning mechanisms remain poorly understood. In this paper, we investigate the reasoning trajectories of LRMs from an information-theoretic perspective. By tracking how mutual information (MI) between intermediate representations and the correct answer evolves during LRM reasoning, we observe an interesting MI peaks phenomenon: the MI at specific generative steps exhibits a s…

ResPF: Residual Poisson Flow for Efficient and Physically Consistent Sparse-View CT Reconstruction
Changsheng Fang, Yongtong Liu, Bahareh Morovati, Shuo Han, Yu Shi, Li Zhou, Shuyi Fan, Hengyong Yu
https://arxiv.org/abs/2506.06400

ResPF: Residual Poisson Flow for Efficient and Physically Consistent Sparse-View CT Reconstruction
Sparse-view computed tomography (CT) is a practical solution to reduce radiation dose, but the resulting ill-posed inverse problem poses significant challenges for accurate image reconstruction. Although deep learning and diffusion-based methods have shown promising results, they often lack physical interpretability or suffer from high computational costs due to iterative sampling starting from random noise. Recent advances in generative modeling, particularly Poisson Flow Generative Models (PF…

VChatter: Exploring Generative Conversational Agents for Simulating Exposure Therapy to Reduce Social Anxiety
Han Zhang, KaWing Tsang, Zhenhui Peng
https://arxiv.org/abs/2506.03520

VChatter: Exploring Generative Conversational Agents for Simulating Exposure Therapy to Reduce Social Anxiety
Many people struggle with social anxiety, feeling fear, or even physically uncomfortable in social situations like talking to strangers. Exposure therapy, a clinical method that gradually and repeatedly exposes individuals to the source of their fear and helps them build coping mechanisms, can reduce social anxiety but traditionally requires human therapists' guidance and constructions of situations. In this paper, we developed a multi-agent system VChatter to explore large language models(LLMs…

This https://arxiv.org/abs/2505.20863 has been replaced.
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Leveraging Diffusion Models for Parameterized Quantum Circuit Generation
Quantum computing holds immense potential, yet its practical success depends on multiple factors, including advances in quantum circuit design. In this paper, we introduce a generative approach based on denoising diffusion models (DMs) to synthesize parameterized quantum circuits (PQCs). Extending the recent diffusion model pipeline of Fürrutter et al. [1], our model effectively conditions the synthesis process, enabling the simultaneous generation of circuit architectures and their continuous…

Generating Long Semantic IDs in Parallel for Recommendation
Yupeng Hou, Jiacheng Li, Ashley Shin, Jinsung Jeon, Abhishek Santhanam, Wei Shao, Kaveh Hassani, Ning Yao, Julian McAuley
https://arxiv.org/abs/2506.05781

Generating Long Semantic IDs in Parallel for Recommendation
Semantic ID-based recommendation models tokenize each item into a small number of discrete tokens that preserve specific semantics, leading to better performance, scalability, and memory efficiency. While recent models adopt a generative approach, they often suffer from inefficient inference due to the reliance on resource-intensive beam search and multiple forward passes through the neural sequence model. As a result, the length of semantic IDs is typically restricted (e.g. to just 4 tokens), …

This https://arxiv.org/abs/2502.12089 has been replaced.
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How Compositional Generalization and Creativity Improve as Diffusion Models are Trained
Natural data is often organized as a hierarchical composition of features. How many samples do generative models need in order to learn the composition rules, so as to produce a combinatorially large number of novel data? What signal in the data is exploited to learn those rules? We investigate these questions in the context of diffusion models both theoretically and empirically. Theoretically, we consider a simple probabilistic context-free grammar - a tree-like graphical model used to represe…

UniConFlow: A Unified Constrained Generalization Framework for Certified Motion Planning with Flow Matching Models
Zewen Yang, Xiaobing Dai, Dian Yu, Qianru Li, Yu Li, Valentin Le Mesle
https://arxiv.org/abs/2506.02955

UniConFlow: A Unified Constrained Generalization Framework for Certified Motion Planning with Flow Matching Models
Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation across various tasks. Yet, most existing approaches remain limited in handling multiple types of constraints, such as collision avoidance and dynamic consistency, which are often treated separately or only partially considered. This paper proposes UniConFlow, a unified flow matching (FM) based framework for trajectory generation that systematically incorp…

Squeeze3D: Your 3D Generation Model is Secretly an Extreme Neural Compressor
Rishit Dagli, Yushi Guan, Sankeerth Durvasula, Mohammadreza Mofayezi, Nandita Vijaykumar
https://arxiv.org/abs/2506.07932

Squeeze3D: Your 3D Generation Model is Secretly an Extreme Neural Compressor
We propose Squeeze3D, a novel framework that leverages implicit prior knowledge learnt by existing pre-trained 3D generative models to compress 3D data at extremely high compression ratios. Our approach bridges the latent spaces between a pre-trained encoder and a pre-trained generation model through trainable mapping networks. Any 3D model represented as a mesh, point cloud, or a radiance field is first encoded by the pre-trained encoder and then transformed (i.e. compressed) into a highly com…

Intentionally Unintentional: GenAI Exceptionalism and the First Amendment
David Atkinson, Jena D. Hwang, Jacob Morrison
https://arxiv.org/abs/2506.05211 ht…

Intentionally Unintentional: GenAI Exceptionalism and the First Amendment
This paper challenges the assumption that courts should grant First Amendment protections to outputs from large generative AI models, such as GPT-4 and Gemini. We argue that because these models lack intentionality, their outputs do not constitute speech as understood in the context of established legal precedent, so there can be no speech to protect. Furthermore, if the model outputs are not speech, users cannot claim a First Amendment speech right to receive the outputs. We also argue that ex…

Semantics-Guided Generative Image Compression
Cheng-Lin Wu, Hyomin Choi, Ivan V. Baji\'c
https://arxiv.org/abs/2505.24015 https://

Semantics-Guided Generative Image Compression
Advancements in text-to-image generative AI with large multimodal models are spreading into the field of image compression, creating high-quality representation of images at extremely low bit rates. This work introduces novel components to the existing multimodal image semantic compression (MISC) approach, enhancing the quality of the generated images in terms of PSNR and perceptual metrics. The new components include semantic segmentation guidance for the generative decoder, as well as content…

This https://arxiv.org/abs/2503.02407 has been replaced.
initial toot: https://mastoxiv.page/@a…

Wyckoff Transformer: Generation of Symmetric Crystals
Crystal symmetry plays a fundamental role in determining its physical, chemical, and electronic properties such as electrical and thermal conductivity, optical and polarization behavior, and mechanical strength. Almost all known crystalline materials have internal symmetry. However, this is often inadequately addressed by existing generative models, making the consistent generation of stable and symmetrically valid crystal structures a significant challenge. We introduce WyFormer, a generative …

This https://arxiv.org/abs/2307.14634 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Fact-Checking of AI-Generated Reports
With advances in generative artificial intelligence (AI), it is now possible to produce realistic-looking automated reports for preliminary reads of radiology images. This can expedite clinical workflows, improve accuracy and reduce overall costs. However, it is also well-known that such models often hallucinate, leading to false findings in the generated reports. In this paper, we propose a new method of fact-checking of AI-generated reports using their associated images. Specifically, the dev…

ReFlow-VC: Zero-shot Voice Conversion Based on Rectified Flow and Speaker Feature Optimization
Pengyu Ren, Wenhao Guan, Kaidi Wang, Peijie Chen, Qingyang Hong, Lin Li
https://arxiv.org/abs/2506.01032

ReFlow-VC: Zero-shot Voice Conversion Based on Rectified Flow and Speaker Feature Optimization
In recent years, diffusion-based generative models have demonstrated remarkable performance in speech conversion, including Denoising Diffusion Probabilistic Models (DDPM) and others. However, the advantages of these models come at the cost of requiring a large number of sampling steps. This limitation hinders their practical application in real-world scenarios. In this paper, we introduce ReFlow-VC, a novel high-fidelity speech conversion method based on rectified flow. Specifically, ReFlow-VC…

GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion
Sunkyung Lee, Minjin Choi, Eunseong Choi, Hye-young Kim, Jongwuk Lee
https://arxiv.org/abs/2506.01673

GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion
Generative recommendation is an emerging paradigm that leverages the extensive knowledge of large language models by formulating recommendations into a text-to-text generation task. However, existing studies face two key limitations in (i) incorporating implicit item relationships and (ii) utilizing rich yet lengthy item information. To address these challenges, we propose a Generative Recommender via semantic-Aware Multi-granular late fusion (GRAM), introducing two synergistic innovations. Fir…

This https://arxiv.org/abs/2411.14681 has been replaced.
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TrojanEdit: Multimodal Backdoor Attack Against Image Editing Model
Multimodal diffusion models for image editing generate outputs conditioned on both textual instructions and visual inputs, aiming to modify target regions while preserving the rest of the image. Although diffusion models have been shown to be vulnerable to backdoor attacks, existing efforts mainly focus on unimodal generative models and fail to address the unique challenges in multimodal image editing. In this paper, we present the first study of backdoor attacks on multimodal diffusion-based i…

Reviewriter: AI-Generated Instructions For Peer Review Writing
Xiaotian Su, Thiemo Wambsganss, Roman Rietsche, Seyed Parsa Neshaei, Tanja K\"aser
https://arxiv.org/abs/2506.04423

Reviewriter: AI-Generated Instructions For Peer Review Writing
Large Language Models (LLMs) offer novel opportunities for educational applications that have the potential to transform traditional learning for students. Despite AI-enhanced applications having the potential to provide personalized learning experiences, more studies are needed on the design of generative AI systems and evidence for using them in real educational settings. In this paper, we design, implement and evaluate \texttt{Reviewriter}, a novel tool to provide students with AI-generated …

This https://arxiv.org/abs/2505.07802 has been replaced.
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Improving Trajectory Stitching with Flow Models
Generative models have shown great promise as trajectory planners, given their affinity to modeling complex distributions and guidable inference process. Previous works have successfully applied these in the context of robotic manipulation but perform poorly when the required solution does not exist as a complete trajectory within the training set. We identify that this is a result of being unable to plan via stitching, and subsequently address the architectural and dataset choices needed to re…

This https://arxiv.org/abs/2506.00605 has been replaced.
link: https://scholar.google.com/scholar?q=a

ABCDEFGH: An Adaptation-Based Convolutional Neural Network-CycleGAN Disease-Courses Evolution Framework Using Generative Models in Health Education
With the advancement of modern medicine and the development of technologies such as MRI, CT, and cellular analysis, it has become increasingly critical for clinicians to accurately interpret various diagnostic images. However, modern medical education often faces challenges due to limited access to high-quality teaching materials, stemming from privacy concerns and a shortage of educational resources (Balogh et al., 2015). In this context, image data generated by machine learning models, partic…

FUSE: Universal Speech Enhancement using Multi-Stage Fusion of Sparse Compression and Token Generation Models for the URGENT 2025 Challenge
Nabarun Goswami, Tatsuya Harada
https://arxiv.org/abs/2506.00809

FUSE: Universal Speech Enhancement using Multi-Stage Fusion of Sparse Compression and Token Generation Models for the URGENT 2025 Challenge
We propose a multi-stage framework for universal speech enhancement, designed for the Interspeech 2025 URGENT Challenge. Our system first employs a Sparse Compression Network to robustly separate sources and extract an initial clean speech estimate from noisy inputs. This is followed by an efficient generative model that refines speech quality by leveraging self-supervised features and optimizing a masked language modeling objective on acoustic tokens derived from a neural audio codec. In the f…

A Generative Adaptive Replay Continual Learning Model for Temporal Knowledge Graph Reasoning
Zhiyu Zhang, Wei Chen, Youfang Lin, Huaiyu Wan
https://arxiv.org/abs/2506.04083

A Generative Adaptive Replay Continual Learning Model for Temporal Knowledge Graph Reasoning
Recent Continual Learning (CL)-based Temporal Knowledge Graph Reasoning (TKGR) methods focus on significantly reducing computational cost and mitigating catastrophic forgetting caused by fine-tuning models with new data. However, existing CL-based TKGR methods still face two key limitations: (1) They usually one-sidedly reorganize individual historical facts, while overlooking the historical context essential for accurately understanding the historical semantics of these facts; (2) They preserv…

This https://arxiv.org/abs/2405.02696 has been replaced.
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DiffuseTrace: A Transparent and Flexible Watermarking Scheme for Latent Diffusion Model
Latent Diffusion Models (LDMs) enable a wide range of applications but raise ethical concerns regarding illegal utilization. Adding watermarks to generative model outputs is a vital technique employed for copyright tracking and mitigating potential risks associated with Artificial Intelligence (AI)-generated contents. However, post-processed watermarking methods are unable to withstand generative watermark attacks and there exists a trade-off between image fidelity and watermark strength. There…

Towards generating more interpretable counterfactuals via concept vectors: a preliminary study on chest X-rays
Bulat Maksudov, Kathleen Curran, Alessandra Mileo
https://arxiv.org/abs/2506.04058

Towards generating more interpretable counterfactuals via concept vectors: a preliminary study on chest X-rays
An essential step in deploying medical imaging models is ensuring alignment with clinical knowledge and interpretability. We focus on mapping clinical concepts into the latent space of generative models to identify Concept Activation Vectors (CAVs). Using a simple reconstruction autoencoder, we link user-defined concepts to image-level features without explicit label training. The extracted concepts are stable across datasets, enabling visual explanations that highlight clinically relevant feat…

Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots
Lingfan Bao, Yan Pan, Tianhu Peng, Chengxu Zhou
https://arxiv.org/abs/2506.01563

Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots
Effective human-robot interaction requires robots to identify human intentions and generate expressive, socially appropriate motions in real-time. Existing approaches often rely on fixed motion libraries or computationally expensive generative models. We propose a hierarchical framework that combines intention-aware reasoning via in-context learning (ICL) with real-time motion generation using diffusion models. Our system introduces structured prompting with confidence scoring, fallback behavio…

This https://arxiv.org/abs/2504.10545 has been replaced.
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HSTU-BLaIR: Lightweight Contrastive Text Embedding for Generative Recommender
Recent advances in recommender systems have underscored the complementary strengths of generative modeling and pretrained language models. We propose HSTU-BLaIR, a hybrid framework that augments the Hierarchical Sequential Transduction Unit (HSTU)-based generative recommender with BLaIR, a lightweight contrastive text embedding model. This integration enriches item representations with semantic signals from textual metadata while preserving HSTU's powerful sequence modeling capabilities. We e…

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Yan Zhou, Bradley Malin, Murat Kantarcioglu
https://arxiv.org/abs/2506.01907 https://

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Privacy-preserving data publication, including synthetic data sharing, often experiences trade-offs between privacy and utility. Synthetic data is generally more effective than data anonymization in balancing this trade-off, however, not without its own challenges. Synthetic data produced by generative models trained on source data may inadvertently reveal information about outliers. Techniques specifically designed for preserving privacy, such as introducing noise to satisfy differential priva…

Scaling Transformers for Discriminative Recommendation via Generative Pretraining
Chunqi Wang, Bingchao Wu, Zheng Chen, Lei Shen, Bing Wang, Xiaoyi Zeng
https://arxiv.org/abs/2506.03699

Scaling Transformers for Discriminative Recommendation via Generative Pretraining
Discriminative recommendation tasks, such as CTR (click-through rate) and CVR (conversion rate) prediction, play critical roles in the ranking stage of large-scale industrial recommender systems. However, training a discriminative model encounters a significant overfitting issue induced by data sparsity. Moreover, this overfitting issue worsens with larger models, causing them to underperform smaller ones. To address the overfitting issue and enhance model scalability, we propose a framework na…