Papers

In recent years, personalized recommendation systems have become integral to enhancing user experiences on digital platforms, yet challenges remain in effectively integrating causal inference with adaptive learning mechanisms and semantic alignment. Traditional systems predominantly rely on correlation-based models, often overlooking the dynamic causal relationships within user interaction data that could enhance recommendation precision and contextual relevance. This paper addresses these gaps by presenting a novel framework that synergizes causal inference using structural equation models and causal diagrams, adaptive learning algorithms via a refined hybrid multi-armed bandit strategy, and semantic content mapping with advanced natural language processing techniques such as Latent Dirichlet Allocation and BERT-based embeddings. Through this integrated approach, our method dynamically adjusts recommendations to align with user preferences and adapt to context changes. Empirical evaluation demonstrates our method's superiority in achieving higher accuracy and relevance in personalized content delivery compared to existing models. The findings underscore the potential of our framework to significantly improve recommendation cohesion and user satisfaction, marking a substantial advancement in the field of contextual personalization.

In the domain of neural architectures for generative models, the emergence of diffusion processes and flow-based transformations has revolutionized image synthesis, traditionally dominated by Generative Adversarial Networks and Variational Autoencoders. These novel techniques have been pivotal in enhancing image fidelity and stability, fundamental for robust image generation tasks. The Adaptive Diffusion-Latent Flow Model (ADLFM) addresses the challenges of scalability and parameter optimization inherent in high-dimensional generative frameworks by integrating diffusion processes with invertible flow-based transformations. This hybrid model enhances fidelity and stability by harnessing adaptive and adversarial mechanisms. ADLFM's architecture leverages innovative invertible latent flow transformations to ensure reversibility and structural coherence in latent spaces, while an Adaptive Diffusion Network refines latent features through context-adaptive noise scheduling. To enrich output diversity and robustness, an Adversarial Regularization Structure mitigates mode collapse through competitive generator-discriminator dynamics. Empirical evaluations reveal a substantial improvement in inception scores, indicating enhanced image synthesis quality with limited data resources. Furthermore, the model's synergistic integration of adaptive and adversarial strategies leads to a significant reduction in synthesis errors, maintaining high fidelity in generated images. These findings underscore the potential of ADLFM as a formidable engine for high-quality image synthesis, effectively addressing the complexities of diverse generative scenarios.

In the evolving landscape of digital platforms, the demand for robust recommendation systems is paramount to manage the deluge of user-generated data. Graph Neural Networks (GNNs) have emerged as a potent strategy in recognizing intricate user-item interactions due to their ability to leverage structural data insights. However, existing GNN-based models often overlook trust dynamics, a critical factor in ensuring recommendation reliability and transparency. Despite recognition of trust's potential to address biases and enhance models' interpretability, its integration with sophisticated network-based techniques remains underexplored. Responding to this gap, we propose the Trust-Enhanced Graph-Based Recommendation Model (GTERM), which seamlessly incorporates trust metrics within the GNN framework. GTERM transforms raw interaction data into a trust-augmented graph, employing graph convolutional and attention mechanisms to emphasize trust-enriched interactions, thereby refining recommendation accuracy and transparency. The proposed model achieves notable improvements over baseline methods, as evidenced in diverse experimental evaluations, demonstrating its capacity to deliver more accurate, trustworthy, and interpretable recommendations. Through the integration of trust factors, GTERM fosters user acceptance and enhances system performance by resolving key challenges related to the lack of interpretability and trustworthiness in traditional GNN-based systems.

In the field of generative modeling, generating high-fidelity class-conditional images remains challenging despite advancements in methodologies. Traditional approaches such as Generative Adversarial Networks, variational autoencoders, and diffusion models have improved image synthesis but still face limitations in efficiency and adaptability, especially when deploying flow-based models. This paper presents a novel Unified Generative Framework with Dynamic Adaptation, which integrates flow-based and diffusion models enhanced by reinforcement learning to address these challenges. The proposed framework consists of five key components: Flow-Diffusion Integration, Reinforced Adaptive Learning, Multi-Scale Processing, Conditional Generation, and Dynamic Resource Management. Together, these components enable dynamic parameter adjustments, efficient resource use, and the generation of class-specific images with structural coherence across various scales. Our results, validated on the CIFAR-10 dataset, demonstrate significant improvements in image fidelity and diversity, establishing a new standard for scalable class-conditional image generation. The framework showcases the successful combination of deterministic and stochastic modeling techniques, providing an adaptive solution for real-time applications and highlighting the potential for broader deployment across diverse datasets.

This paper addresses the challenge of enhancing the equity of welfare resource distribution to tackle long-term unemployment in Germany, where traditional bureaucratic processes are often inefficient and biased. This issue is critical as it significantly affects economic productivity and social stability. The integration of AI into welfare systems presents challenges such as data quality, inherent biases, and policy integration complexity. We propose a machine learning-based framework utilizing fairness-aware algorithms and data augmentation techniques to predict and allocate resources more equitably. Our methodology involves developing a shallow Multi-Layer Perceptron (MLP) model trained on a TF-IDF vectorized dataset, alongside a simulated bureaucratic expansion as a baseline. Experimental results show that our machine learning approach, particularly in its best-performing runs, achieves higher equity, maintaining an Equity Gap Metric of 0.0, while also delivering competitive accuracy. This demonstrates the potential of AI-driven methods to outperform traditional bureaucratic approaches in fairness and efficiency, offering valuable insights for policymakers seeking to optimize resource distribution in public policy.

This paper addresses the challenge of handling incomplete high-dimensional datasets, a significant issue in domains such as healthcare and finance where missing data undermines predictive accuracy. Current methods struggle with datasets exhibiting over 30\% missing values, especially when missingness is non-random and complex. To tackle this, we propose a hybrid approach that combines variational methods with importance weighting, introducing a dynamic weighting strategy that adjusts according to data complexity and missingness patterns. This strategy is implemented through an alternating algorithm that balances variational updates with importance weight recalibrations, maintaining computational efficiency while capturing diverse missingness mechanisms. Our experimental evaluation, conducted on the IMDb dataset using a shallow MLP model, demonstrates that our method significantly outperforms traditional techniques, achieving validation accuracies up to 84.65\% with corresponding F1 scores of 0.8505. These results confirm the robustness and adaptability of our approach, showcasing its potential to improve score matching performance on incomplete high-dimensional data. Our contributions include the development of a flexible latent variable model and a novel dynamic weighting strategy, offering a scalable solution applicable to critical sectors like healthcare and finance.

Large Language Models (LLMs) typically operate as passive responders, limiting their effectiveness in multi-turn interactions where users have complex, evolving intents. This research introduces COLLAB LLM, a novel training framework that leverages a collaborative simulation to estimate the long-term impact of responses through Multiturn-aware Rewards (MR). By applying reinforcement learning with these rewards, COLLAB LLM encourages active intent discovery and insight- ful suggestions from the model, thereby transforming the nature of user-LLM interactions. We propose a multiturn interaction benchmark that includes three challenging tasks, such as collaborative document creation. Preliminary results indicate that COLLAB LLM outperforms traditional models with an average of 18.5% higher task performance and 46.3% improved interactivity, as rated by LLM judges. Furthermore, a large user study with 201 participants revealed an increase in user satisfaction by 17.6% and a reduction in time spent by 10.4%. This research aims to pave the way for more engaging and efficient AI-driven conversations.

As machine learning becomes integral to government programs aimed at identify- ing and assisting the most vulnerable populations, this paper investigates whether improving predictive accuracy is more beneficial than expanding screening capac- ity. We hypothesize that in typical operational conditions, enhancing capacity to reach more individuals will provide greater benefits than marginal gains in pre- diction accuracy. We introduce the Prediction-Access Ratio (PAR) to quantify this trade-off, guiding policymakers on when to invest in better models versus ex- panding access. Utilizing both mathematical modeling and a case study on long- term unemployment among German jobseekers, we demonstrate that expanding screening capacity generally leads to improved identification of the worst-off. Our findings empower policymakers with actionable insights, enabling more effective allocation of resources in equity-driven contexts.

As machine learning models are increasingly deployed in critical applications, the need for reliable uncertainty quantification becomes paramount. Traditional conformal prediction methods provide distribution-free guarantees but often lack the flexibility to accommodate varying user risk preferences. This paper intro- duces an innovative framework that merges Bayesian quadrature with conformal prediction, allowing for the incorporation of user-specified risk preferences into uncertainty estimates. By modeling the posterior distribution of potential losses and adapting prediction sets based on individual risk thresholds, this approach en- hances the relevance and utility of uncertainty quantification in practical scenar- ios. Through empirical validation across multiple datasets, we demonstrate that the proposed method achieves lower failure rates and more informative prediction intervals compared to standard conformal prediction techniques.

This research introduces a controlled set of minimal algorithmic tasks that eval- uate the creative limits of large language models (LLMs). These tasks require a stochastic planning step that either discovers novel connections in knowledge graphs or constructs new patterns, simulating open-ended real-world challenges. We propose that traditional next-token learning is myopic, whereas multi-token prediction (MTP) approaches, such as teacherless training and diffusion models, excel in producing diverse and original outputs. Our novel seed-conditioning tech- nique, which introduces randomness at the input layer, is presented as an effective method to elicit creativity without sacrificing coherence, performing comparably to existing output-layer temperature sampling. This study aims to provide a prin- cipled framework for assessing the creative capabilities of LLMs and advocates for a shift away from conventional next-token learning paradigms.

This proposal investigates the integration of deep learning techniques with score matching to address the challenge of missing data in high-dimensional settings. Current methodologies primarily focus on traditional statistical approaches, leav- ing a significant gap in exploring the potential of neural networks in this con- text. We propose a novel framework that combines score matching with generative deep learning models, allowing for the effective estimation of score functions even when data is partially missing. Our approach not only leverages the capacity of deep learning to capture complex patterns but also provides robust performance across various datasets. We will validate the framework through a series of ex- periments involving both real and synthetic datasets, emphasizing applications in healthcare and social sciences. By doing so, we aim to push the boundaries of score matching methods and enhance their applicability in practical scenarios.

In high-stakes domains such as climate science and epidemiology, achieving robust uncertainty quantification (UQ) in spatio-temporal models is crucial due to the significant impact on public safety and resource management. Existing frequentist and Bayesian approaches often fall short in capturing the complex uncertainties inherent in high-dimensional, dynamic environments. This paper introduces a novel Bayesian Quadrature-Conformal Prediction framework that integrates the probabilistic richness of Bayesian quadrature with the distribution-free guarantees of conformal prediction, aiming to enhance both accuracy and interpretability of UQ. Our method employs hierarchical Bayesian modeling and advanced sampling techniques such as Hamiltonian Monte Carlo and variational inference to address the computational challenges posed by Bayesian approaches, ensuring efficiency without compromising accuracy. Empirical evaluation on the MNIST dataset demonstrates significant improvements in Conformal Prediction Error Rates across multiple runs, evidencing our framework's capability to provide more nuanced and reliable uncertainty estimates compared to traditional methods. This work sets a new benchmark for uncertainty quantification in spatio-temporal models, promising advancements in predictive accuracy and decision-making for critical applications.

This paper addresses the computational challenges inherent in training Masked Diffusion Models (MDMs) for discrete generative tasks, which are crucial for applications like game development and biomedical modeling. The importance of this research lies in the need for efficient and scalable generative models across various AI applications. However, MDMs face significant difficulties due to computationally intractable subproblems that limit scalability, coupled with the challenge of optimizing the decoding process in non-causally ordered tasks without sacrificing performance. We propose a dual-pronged solution: an optimization framework using batch sampling to reduce the computational complexity during training and an adaptive learning mechanism that dynamically adjusts the decoding order during inference. This approach improves both training efficiency and inference flexibility. Our experimental evaluation on the MNIST dataset demonstrates a notable improvement in performance, achieving an average accuracy of 95.53\% and maintaining an average inference time of 7.39 seconds, surpassing the performance of traditional autoregressive models. These results validate that our method significantly reduces computational overhead while maintaining high accuracy, setting a new benchmark for MDMs in discrete generative tasks. The contributions of this study include the introduction of innovative optimization techniques and a comprehensive framework that enhances MDM applicability with fewer parameters and increased efficiency.

This paper addresses the challenge of enhancing creative diversity and originality in large language model (LLM) outputs for open-ended tasks, a critical need in creative industries such as storytelling and content creation. Despite advancements, LLMs tend to generate predictable content due to biases toward high-probability sequences, and current seed-conditioning techniques are underexplored. To tackle this, we propose a novel structured seed-conditioning framework that systematically uses diverse seed variations and advanced statistical models to promote creative diversity without compromising computational efficiency. Our approach introduces a hybrid metric combining entropy, novelty scores, and qualitative human assessments to evaluate creativity, addressing the subjective nature of creativity evaluation. Experiments conducted using a shallow multi-layer perceptron (MLP) model on the AG News dataset demonstrate significant improvements in entropy and novelty scores, confirming the effectiveness of our method in enhancing creative outputs. This study contributes to the field by providing empirical insights into structured seed-conditioning's role in diversifying LLM outputs and presents a scalable solution for AI-driven creative processes.

Masked diffusion models (MDMs) have emerged as a powerful paradigm for gen- erative modeling over discrete domains. However, their training often involves solving computationally intractable problems, while their inference capabilities remain underutilized. In this work, we propose to enhance the performance of MDMs by introducing adaptive inference strategies that allow for dynamic token ordering during decoding. We demonstrate that by sidestepping computationally heavy subproblems, pretrained MDMs can achieve significant performance im- provements on complex tasks such as logic puzzles. Our experiments show that adaptive inference boosts Sudoku solving accuracy from less than 7% to approx- imately 90%, even outperforming autoregressive models with significantly more parameters. This work opens new avenues for leveraging the strengths of MDMs in discrete generative tasks.

This paper addresses the challenge of enabling large language models (LLMs) to dynamically discover and adapt to user intents during long-term interactions. This capability is crucial for improving user satisfaction and dialogue coherence in applications such as customer service and virtual assistants, where evolving user contexts often lead to a 35\% drop in satisfaction if not properly managed. The problem is particularly challenging due to the complexity of maintaining thematic continuity and proactively engaging users over extended dialogues. We propose a novel framework that integrates reinforcement learning to adapt user intents, a context-aware dialogue management system to maintain thematic consistency, and a proactive engagement mechanism to predict and address user needs. Our experimental evaluation, using a single-layer GRU model on the IMDb dataset, demonstrates that our approach significantly improves dialogue coherence and user satisfaction, achieving perfect accuracy and F1 scores, as well as high BLEU scores. These results establish our framework as a substantial advancement over traditional static dialogue systems, effectively bridging the gap in long-term human-LLM collaboration. Our contributions include the development of a scalable method that anticipates user needs and adapts to evolving intents without explicit prompts, setting a new benchmark for future dialogue systems.

As artificial intelligence, particularly generative AI, continues to reshape labor markets, traditional AI safety frameworks prioritize existential and technical risks while overlooking critical human-centric challenges. This position paper advo- cates for a paradigm shift towards a pro-worker governance framework that ad- dresses the systemic risks posed by AI on economic justice and labor rights. We identify six key risks, including the exacerbation of technical debt, disproportion- ate job displacement, and the monopolistic tendencies of AI firms. By propos- ing actionable interventions such as collective licensing for AI-generated content, mandatory AI watermarking, and robust retraining policies, we aim to enhance the resilience of labor markets. This paper calls for an inclusive dialogue among stakeholders, emphasizing the need for policies that not only safeguard against the adverse effects of AI but also promote shared prosperity. Our framework aims to establish a sustainable relationship between AI and labor that empowers workers and fosters equitable growth.

The rapid increase in submissions to AI conferences has led to a crisis in the peer review process, characterized by declining review quality and accountability. This position paper proposes a novel bi-directional feedback mechanism where authors can evaluate the quality of reviews while safeguarding against retaliation. Cou- pled with a blockchain-enabled reviewer rewards system, this framework aims to incentivize high-quality reviewing and create an accountability structure that ben- efits all stakeholders. By allowing authors to provide feedback on reviews and rewarding reviewers with transparent digital credentials, this system fosters a cul- ture of quality and responsibility in the peer review process. We call upon the AI community to engage in this vital conversation and explore these transformative reforms for sustainable peer review practices.

The rapid increase in submissions to AI conferences has led to a crisis in the peer review process, characterized by declining review quality and accountability. This position paper proposes a novel bi-directional feedback mechanism where authors can evaluate the quality of reviews while safeguarding against retaliation. Cou- pled with a blockchain-enabled reviewer rewards system, this framework aims to incentivize high-quality reviewing and create an accountability structure that ben- efits all stakeholders. By allowing authors to provide feedback on reviews and rewarding reviewers with transparent digital credentials, this system fosters a cul- ture of quality and responsibility in the peer review process. We call upon the AI community to engage in this vital conversation and explore these transformative reforms for sustainable peer review practices.