pwc-archive/papers-with-abstracts huggingface dataset:
Shallow AutoEncoding Recommender with Cold Start Handling via Side Features
Authors: Edward DongBo Cui, Lu Zhang, William Ping-hsun Lee
Date: 2025-04-03 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

User and item cold starts present significant challenges in industrial applications of recommendation systems. Supplementing user-item interaction data with metadata is a common solution-but often at the cost of introducing additional biases. In this work, we introduce an augmented EASE model that seamlessly integrates both user and item side information to address these cold start issues. Our straightforward, autoencoder-based method produces a closed-form solution that leverages rich content signals for cold items while refining user representations in data-sparse environments. Importantly, our method strikes a balance by effectively recommending cold start items and handling cold start users without incurring extra bias, and it maintains strong performance in warm settings. Experimental results demonstrate improved recommendation accuracy and robustness compared to previous collaborative filtering approaches. Moreover, our model serves as a strong baseline for future comparative studies.
Dynamic Assortment Selection and Pricing with Censored Preference Feedback
Authors: Jung-hun Kim, Min-hwan Oh
Date: 2025-04-03 00:00:00
Tasks: Thompson Sampling

In this study, we investigate the problem of dynamic multi-product selection and pricing by introducing a novel framework based on a \textit{censored multinomial logit} (C-MNL) choice model. In this model, sellers present a set of products with prices, and buyers filter out products priced above their valuation, purchasing at most one product from the remaining options based on their preferences. The goal is to maximize seller revenue by dynamically adjusting product offerings and prices, while learning both product valuations and buyer preferences through purchase feedback. To achieve this, we propose a Lower Confidence Bound (LCB) pricing strategy. By combining this pricing strategy with either an Upper Confidence Bound (UCB) or Thompson Sampling (TS) product selection approach, our algorithms achieve regret bounds of $\tilde{O}(d^{\frac{3}{2}}\sqrt{T/\kappa})$ and $\tilde{O}(d^{2}\sqrt{T/\kappa})$, respectively. Finally, we validate the performance of our methods through simulations, demonstrating their effectiveness.
Inference-Time Scaling for Generalist Reward Modeling
Authors: Zijun Liu, Peiyi Wang, Runxin Xu, Shirong Ma, Chong Ruan, Peng Li, Yang Liu, Yu Wu
Date: 2025-04-03 00:00:00
Tasks: Reinforcement Learning (RL)

Reinforcement learning (RL) has been widely adopted in post-training for large language models (LLMs) at scale. Recently, the incentivization of reasoning capabilities in LLMs from RL indicates that $\textit{proper learning methods could enable effective inference-time scalability}$. A key challenge of RL is to obtain accurate reward signals for LLMs in various domains beyond verifiable questions or artificial rules. In this work, we investigate how to improve reward modeling (RM) with more inference compute for general queries, i.e. the $\textbf{inference-time scalability of generalist RM}$, and further, how to improve the effectiveness of performance-compute scaling with proper learning methods. For the RM approach, we adopt pointwise generative reward modeling (GRM) to enable flexibility for different input types and potential for inference-time scaling. For the learning method, we propose Self-Principled Critique Tuning (SPCT) to foster scalable reward generation behaviors in GRMs through online RL, to generate principles adaptively and critiques accurately, resulting in $\textbf{DeepSeek-GRM}$ models. Furthermore, for effective inference-time scaling, we use parallel sampling to expand compute usage, and introduce a meta RM to guide voting process for better scaling performance. Empirically, we show that SPCT significantly improves the quality and scalability of GRMs, outperforming existing methods and models in various RM benchmarks without severe biases, and could achieve better performance compared to training-time scaling. DeepSeek-GRM still meets challenges in some tasks, which we believe can be addressed by future efforts in generalist reward systems. The models will be released and open-sourced.
Analytical Discovery of Manifold with Machine Learning
Authors: Yafei Shen, Huan-Fei Ma, Ling Yang
Date: 2025-04-03 00:00:00
Tasks: Anomaly Detection, Computational Efficiency, Denoising

Understanding low-dimensional structures within high-dimensional data is crucial for visualization, interpretation, and denoising in complex datasets. Despite the advancements in manifold learning techniques, key challenges-such as limited global insight and the lack of interpretable analytical descriptions-remain unresolved. In this work, we introduce a novel framework, GAMLA (Global Analytical Manifold Learning using Auto-encoding). GAMLA employs a two-round training process within an auto-encoding framework to derive both character and complementary representations for the underlying manifold. With the character representation, the manifold is represented by a parametric function which unfold the manifold to provide a global coordinate. While with the complementary representation, an approximate explicit manifold description is developed, offering a global and analytical representation of smooth manifolds underlying high-dimensional datasets. This enables the analytical derivation of geometric properties such as curvature and normal vectors. Moreover, we find the two representations together decompose the whole latent space and can thus characterize the local spatial structure surrounding the manifold, proving particularly effective in anomaly detection and categorization. Through extensive experiments on benchmark datasets and real-world applications, GAMLA demonstrates its ability to achieve computational efficiency and interpretability while providing precise geometric and structural insights. This framework bridges the gap between data-driven manifold learning and analytical geometry, presenting a versatile tool for exploring the intrinsic properties of complex data sets.
Affordable AI Assistants with Knowledge Graph of Thoughts
Authors: Maciej Besta, Lorenzo Paleari, Jia Hao Andrea Jiang, Robert Gerstenberger, You Wu, Patrick Iff, Ales Kubicek, Piotr Nyczyk, Diana Khimey, Jón Gunnar Hannesson, Grzegorz Kwaśniewski, Marcin Copik, Hubert Niewiadomski, Torsten Hoefler
Date: 2025-04-03 00:00:00
Tasks: Knowledge Graphs, LLM real-life tasks, Multimodal Reasoning, RAG

Large Language Models (LLMs) are revolutionizing the development of AI assistants capable of performing diverse tasks across domains. However, current state-of-the-art LLM-driven agents face significant challenges, including high operational costs and limited success rates on complex benchmarks like GAIA. To address these issues, we propose the Knowledge Graph of Thoughts (KGoT), an innovative AI assistant architecture that integrates LLM reasoning with dynamically constructed knowledge graphs (KGs). KGoT extracts and structures task-relevant knowledge into a dynamic KG representation, iteratively enhanced through external tools such as math solvers, web crawlers, and Python scripts. Such structured representation of task-relevant knowledge enables low-cost models to solve complex tasks effectively. For example, KGoT achieves a 29% improvement in task success rates on the GAIA benchmark compared to Hugging Face Agents with GPT-4o mini, while reducing costs by over 36x compared to GPT-4o. Improvements for recent reasoning models are similar, e.g., 36% and 37.5% for Qwen2.5-32B and Deepseek-R1-70B, respectively. KGoT offers a scalable, affordable, and high-performing solution for AI assistants.
MegaMath: Pushing the Limits of Open Math Corpora
Authors: Fan Zhou, Zengzhi Wang, Nikhil Ranjan, Zhoujun Cheng, Liping Tang, Guowei He, Zhengzhong Liu, Eric P. Xing
Date: 2025-04-03 00:00:00
Tasks: Diversity, Math, Mathematical Reasoning

Mathematical reasoning is a cornerstone of human intelligence and a key benchmark for advanced capabilities in large language models (LLMs). However, the research community still lacks an open, large-scale, high-quality corpus tailored to the demands of math-centric LLM pre-training. We present MegaMath, an open dataset curated from diverse, math-focused sources through following practices: (1) Revisiting web data: We re-extracted mathematical documents from Common Crawl with math-oriented HTML optimizations, fasttext-based filtering and deduplication, all for acquiring higher-quality data on the Internet. (2) Recalling Math-related code data: We identified high quality math-related code from large code training corpus, Stack-V2, further enhancing data diversity. (3) Exploring Synthetic data: We synthesized QA-style text, math-related code, and interleaved text-code blocks from web data or code data. By integrating these strategies and validating their effectiveness through extensive ablations, MegaMath delivers 371B tokens with the largest quantity and top quality among existing open math pre-training datasets.
Generative Evaluation of Complex Reasoning in Large Language Models
Authors: Haowei Lin, Xiangyu Wang, Ruilin Yan, Baizhou Huang, Haotian Ye, Jianhua Zhu, ZiHao Wang, James Zou, Jianzhu Ma, Yitao Liang
Date: 2025-04-03 00:00:00
Tasks: Benchmarking, Memorization

With powerful large language models (LLMs) demonstrating superhuman reasoning capabilities, a critical question arises: Do LLMs genuinely reason, or do they merely recall answers from their extensive, web-scraped training datasets? Publicly released benchmarks inevitably become contaminated once incorporated into subsequent LLM training sets, undermining their reliability as faithful assessments. To address this, we introduce KUMO, a generative evaluation framework designed specifically for assessing reasoning in LLMs. KUMO synergistically combines LLMs with symbolic engines to dynamically produce diverse, multi-turn reasoning tasks that are partially observable and adjustable in difficulty. Through an automated pipeline, KUMO continuously generates novel tasks across open-ended domains, compelling models to demonstrate genuine generalization rather than memorization. We evaluated 23 state-of-the-art LLMs on 5,000 tasks across 100 domains created by KUMO, benchmarking their reasoning abilities against university students. Our findings reveal that many LLMs have outperformed university-level performance on easy reasoning tasks, and reasoning-scaled LLMs reach university-level performance on complex reasoning challenges. Moreover, LLM performance on KUMO tasks correlates strongly with results on newly released real-world reasoning benchmarks, underscoring KUMO's value as a robust, enduring assessment tool for genuine LLM reasoning capabilities.
Do Two AI Scientists Agree?
Authors: Xinghong Fu, Ziming Liu, Max Tegmark
Date: 2025-04-03 00:00:00

When two AI models are trained on the same scientific task, do they learn the same theory or two different theories? Throughout history of science, we have witnessed the rise and fall of theories driven by experimental validation or falsification: many theories may co-exist when experimental data is lacking, but the space of survived theories become more constrained with more experimental data becoming available. We show the same story is true for AI scientists. With increasingly more systems provided in training data, AI scientists tend to converge in the theories they learned, although sometimes they form distinct groups corresponding to different theories. To mechanistically interpret what theories AI scientists learn and quantify their agreement, we propose MASS, Hamiltonian-Lagrangian neural networks as AI Scientists, trained on standard problems in physics, aggregating training results across many seeds simulating the different configurations of AI scientists. Our findings suggests for AI scientists switch from learning a Hamiltonian theory in simple setups to a Lagrangian formulation when more complex systems are introduced. We also observe strong seed dependence of the training dynamics and final learned weights, controlling the rise and fall of relevant theories. We finally demonstrate that not only can our neural networks aid interpretability, it can also be applied to higher dimensional problems.
Disentangling Recognition and Decision Regrets in Image-Based Reinforcement Learning
Authors: Alihan Hüyük, Arndt Ryo Koblitz, Atefeh Mohajeri, Matthew Andrews
Date: 2024-09-19 00:00:00
Tasks: Decision Making, Reinforcement Learning (RL)

In image-based reinforcement learning (RL), policies usually operate in two steps: first extracting lower-dimensional features from raw images (the "recognition" step), and then taking actions based on the extracted features (the "decision" step). Extracting features that are spuriously correlated with performance or irrelevant for decision-making can lead to poor generalization performance, known as observational overfitting in image-based RL. In such cases, it can be hard to quantify how much of the error can be attributed to poor feature extraction vs. poor decision-making. To disentangle the two sources of error, we introduce the notions of recognition regret and decision regret. Using these notions, we characterize and disambiguate the two distinct causes behind observational overfitting: over-specific representations, which include features that are not needed for optimal decision-making (leading to high decision regret), vs. under-specific representations, which only include a limited set of features that were spuriously correlated with performance during training (leading to high recognition regret). Finally, we provide illustrative examples of observational overfitting due to both over-specific and under-specific representations in maze environments and the Atari game Pong.
CoHiRF: A Scalable and Interpretable Clustering Framework for High-Dimensional Data
Authors: Bruno Belucci, Karim Lounici, Katia Meziani
Date: 2025-02-01 00:00:00
Tasks: Clustering, feature selection

Clustering high-dimensional data poses significant challenges due to the curse of dimensionality, scalability issues, and the presence of noisy and irrelevant features. We propose Consensus Hierarchical Random Feature (CoHiRF), a novel clustering method designed to address these challenges effectively. CoHiRF leverages random feature selection to mitigate noise and dimensionality effects, repeatedly applies K-Means clustering in reduced feature spaces, and combines results through a unanimous consensus criterion. This iterative approach constructs a cluster assignment matrix, where each row records the cluster assignments of a sample across repetitions, enabling the identification of stable clusters by comparing identical rows. Clusters are organized hierarchically, enabling the interpretation of the hierarchy to gain insights into the dataset. CoHiRF is computationally efficient with a running time comparable to K-Means, scalable to massive datasets, and exhibits robust performance against state-of-the-art methods such as SC-SRGF, HDBSCAN, and OPTICS. Experimental results on synthetic and real-world datasets confirm the method's ability to reveal meaningful patterns while maintaining scalability, making it a powerful tool for high-dimensional data analysis.
Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences
Authors: Xingshen Zhang, Lin Wang, Shuangrong Liu, Xintao Lu, Chaoran Pang, Bo Yang
Date: 2025-02-05 00:00:00
Tasks: Disentanglement, Representation Learning

In this study, Disentanglement in Difference(DiD) is proposed to address the inherent inconsistency between the statistical independence of latent variables and the goal of semantic disentanglement in disentanglement representation learning. Conventional disentanglement methods achieve disentanglement representation by improving statistical independence among latent variables. However, the statistical independence of latent variables does not necessarily imply that they are semantically unrelated, thus, improving statistical independence does not always enhance disentanglement performance. To address the above issue, DiD is proposed to directly learn semantic differences rather than the statistical independence of latent variables. In the DiD, a Difference Encoder is designed to measure the semantic differences; a contrastive loss function is established to facilitate inter-dimensional comparison. Both of them allow the model to directly differentiate and disentangle distinct semantic factors, thereby resolving the inconsistency between statistical independence and semantic disentanglement. Experimental results on the dSprites and 3DShapes datasets demonstrate that the proposed DiD outperforms existing mainstream methods across various disentanglement metrics.
Reducing Smoothness with Expressive Memory Enhanced Hierarchical Graph Neural Networks
Authors: Thomas Bailie, Yun Sing Koh, S. Karthik Mukkavilli, Varvara Vetrova
Date: 2025-04-01 00:00:00
Tasks: Time Series, Weather Forecasting

Graphical forecasting models learn the structure of time series data via projecting onto a graph, with recent techniques capturing spatial-temporal associations between variables via edge weights. Hierarchical variants offer a distinct advantage by analysing the time series across multiple resolutions, making them particularly effective in tasks like global weather forecasting, where low-resolution variable interactions are significant. A critical challenge in hierarchical models is information loss during forward or backward passes through the hierarchy. We propose the Hierarchical Graph Flow (HiGFlow) network, which introduces a memory buffer variable of dynamic size to store previously seen information across variable resolutions. We theoretically show two key results: HiGFlow reduces smoothness when mapping onto new feature spaces in the hierarchy and non-strictly enhances the utility of message-passing by improving Weisfeiler-Lehman (WL) expressivity. Empirical results demonstrate that HiGFlow outperforms state-of-the-art baselines, including transformer models, by at least an average of 6.1% in MAE and 6.2% in RMSE. Code is available at https://github.com/TB862/ HiGFlow.git.
Geometric Median Matching for Robust k-Subset Selection from Noisy Data
Authors: Anish Acharya, Sujay Sanghavi, Alexandros G. Dimakis, Inderjit S Dhillon
Date: 2025-04-01 00:00:00
Tasks: image-classification, Image Classification, Image Generation

Data pruning -- the combinatorial task of selecting a small and representative subset from a large dataset, is crucial for mitigating the enormous computational costs associated with training data-hungry modern deep learning models at scale. Since large scale data collections are invariably noisy, developing data pruning strategies that remain robust even in the presence of corruption is critical in practice. However, existing data pruning methods often fail under high corruption rates due to their reliance on empirical mean estimation, which is highly sensitive to outliers. In response, we propose Geometric Median (GM) Matching, a novel k-subset selection strategy that leverages Geometric Median -- a robust estimator with an optimal breakdown point of 1/2; to enhance resilience against noisy data. Our method iteratively selects a k-subset such that the mean of the subset approximates the GM of the (potentially) noisy dataset, ensuring robustness even under arbitrary corruption. We provide theoretical guarantees, showing that GM Matching enjoys an improved O(1/k) convergence rate -- a quadratic improvement over random sampling, even under arbitrary corruption. Extensive experiments across image classification and image generation tasks demonstrate that GM Matching consistently outperforms existing pruning approaches, particularly in high-corruption settings and at high pruning rates; making it a strong baseline for robust data pruning.
The Value of Information in Human-AI Decision-making
Authors: Ziyang Guo, Yifan Wu, Jason Hartline, Jessica Hullman
Date: 2025-02-10 00:00:00
Tasks: Decision Making, Model Selection

Multiple agents -- including humans and AI models -- are often paired on decision tasks with the expectation of achieving complementary performance, where the combined performance of both agents outperforms either one alone. However, knowing how to improve the performance of a human-AI team is often difficult without knowing more about what particular information and strategies each agent employs. We provide a decision-theoretic framework for characterizing the value of information -- and consequently, opportunities for agents to better exploit available information -- in AI-assisted decision workflows. We demonstrate the use of the framework for model selection, empirical evaluation of human-AI performance, and explanation design. We propose a novel information-based explanation technique that adapts SHAP, a saliency-based explanation, to explain information value in decision making.
Token-Driven GammaTune: Adaptive Calibration for Enhanced Speculative Decoding
Authors: Aayush Gautam, Susav Shrestha, Narasimha Reddy
Date: 2025-03-28 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Speculative decoding accelerates large language model (LLM) inference by using a smaller draft model to propose tokens, which are then verified by a larger target model. However, selecting an optimal speculation length is critical for maximizing speedup while minimizing wasted computation. We introduce \textit{GammaTune} and \textit{GammaTune+}, training-free adaptive algorithms that dynamically adjust speculation length based on token acceptance rates using a heuristic-based switching mechanism. Evaluated on SpecBench across multiple tasks and model pairs, our method outperforms other heuristic-based approaches and fixed-length speculative decoding, achieving an average speedup of 15\% ($\pm$5\%) with \textit{GammaTune} and 16\% ($\pm$3\%) with \textit{GammaTune+}, while reducing performance variance. This makes \textit{GammaTune} a robust and efficient solution for real-world deployment.
Increasing happiness through conversations with artificial intelligence
Authors: Joseph Heffner, Chongyu Qin, Martin Chadwick, Chris Knutsen, Christopher Summerfield, Zeb Kurth-Nelson, Robb B. Rutledge
Date: 2025-04-02 00:00:00
Tasks: Chatbot, Sentiment Analysis

Chatbots powered by artificial intelligence (AI) have rapidly become a significant part of everyday life, with over a quarter of American adults using them multiple times per week. While these tools offer potential benefits and risks, a fundamental question remains largely unexplored: How do conversations with AI influence subjective well-being? To investigate this, we conducted a study where participants either engaged in conversations with an AI chatbot (N = 334) or wrote journal entires (N = 193) on the same randomly assigned topics and reported their momentary happiness afterward. We found that happiness after AI chatbot conversations was higher than after journaling, particularly when discussing negative topics such as depression or guilt. Leveraging large language models for sentiment analysis, we found that the AI chatbot mirrored participants' sentiment while maintaining a consistent positivity bias. When discussing negative topics, participants gradually aligned their sentiment with the AI's positivity, leading to an overall increase in happiness. We hypothesized that the history of participants' sentiment prediction errors, the difference between expected and actual emotional tone when responding to the AI chatbot, might explain this happiness effect. Using computational modeling, we find the history of these sentiment prediction errors over the course of a conversation predicts greater post-conversation happiness, demonstrating a central role of emotional expectations during dialogue. Our findings underscore the effect that AI interactions can have on human well-being.