pwc-archive/papers-with-abstracts huggingface dataset:
Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval
Authors: Sangam Lee, Ryang Heo, SeongKu Kang, Dongha Lee
Date: 2025-03-29 00:00:00
Tasks: All, Language Modeling, Language Modelling, Large Language Model, RAG, Retrieval, Retrieval-augmented Generation

Existing dense retrieval models struggle with reasoning-intensive retrieval task as they fail to capture implicit relevance that requires reasoning beyond surface-level semantic information. To address these challenges, we propose Scenario-Profiled Indexing with Knowledge Expansion (SPIKE), a dense retrieval framework that explicitly indexes implicit relevance by decomposing documents into scenario-based retrieval units. SPIKE organizes documents into scenario, which encapsulates the reasoning process necessary to uncover implicit relationships between hypothetical information needs and document content. SPIKE constructs a scenario-augmented dataset using a powerful teacher large language model (LLM), then distills these reasoning capabilities into a smaller, efficient scenario generator. During inference, SPIKE incorporates scenario-level relevance alongside document-level relevance, enabling reasoning-aware retrieval. Extensive experiments demonstrate that SPIKE consistently enhances retrieval performance across various query types and dense retrievers. It also enhances the retrieval experience for users through scenario and offers valuable contextual information for LLMs in retrieval-augmented generation (RAG).
Federated Semantic Learning for Privacy-preserving Cross-domain Recommendation
Authors: Ziang Lu, Lei Guo, Xu Yu, Zhiyong Cheng, Xiaohui Han, Lei Zhu
Date: 2025-03-29 00:00:00
Tasks: Privacy Preserving, Recommendation Systems

In the evolving landscape of recommender systems, the challenge of effectively conducting privacy-preserving Cross-Domain Recommendation (CDR), especially under strict non-overlapping constraints, has emerged as a key focus. Despite extensive research has made significant progress, several limitations still exist: 1) Previous semantic-based methods fail to deeply exploit rich textual information, since they quantize the text into codes, losing its original rich semantics. 2) The current solution solely relies on the text-modality, while the synergistic effects with the ID-modality are ignored. 3) Existing studies do not consider the impact of irrelevant semantic features, leading to inaccurate semantic representation. To address these challenges, we introduce federated semantic learning and devise FFMSR as our solution. For Limitation 1, we locally learn items'semantic encodings from their original texts by a multi-layer semantic encoder, and then cluster them on the server to facilitate the transfer of semantic knowledge between domains. To tackle Limitation 2, we integrate both ID and Text modalities on the clients, and utilize them to learn different aspects of items. To handle Limitation 3, a Fast Fourier Transform (FFT)-based filter and a gating mechanism are developed to alleviate the impact of irrelevant semantic information in the local model. We conduct extensive experiments on two real-world datasets, and the results demonstrate the superiority of our FFMSR method over other SOTA methods. Our source codes are publicly available at: https://github.com/Sapphire-star/FFMSR.
DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation
Authors: Hsin-Ling Hsu, Jengnan Tzeng
Date: 2025-03-29 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Large Language Model, RAG, Retrieval, Retrieval-augmented Generation

Hybrid retrieval techniques in Retrieval-Augmented Generation (RAG) systems enhance information retrieval by combining dense and sparse (e.g., BM25-based) retrieval methods. However, existing approaches struggle with adaptability, as fixed weighting schemes fail to adjust to different queries. To address this, we propose DAT (Dynamic Alpha Tuning), a novel hybrid retrieval framework that dynamically balances dense retrieval and BM25 for each query. DAT leverages a large language model (LLM) to evaluate the effectiveness of the top-1 results from both retrieval methods, assigning an effectiveness score to each. It then calibrates the optimal weighting factor through effectiveness score normalization, ensuring a more adaptive and query-aware weighting between the two approaches. Empirical results show that DAT consistently significantly outperforms fixed-weighting hybrid retrieval methods across various evaluation metrics. Even on smaller models, DAT delivers strong performance, highlighting its efficiency and adaptability.
From Individual to Group: Developing a Context-Aware Multi-Criteria Group Recommender System
Authors: Ngoc Luyen Le, Marie-Hélène Abel
Date: 2025-03-27 00:00:00
Tasks: Decision Making, Recommendation Systems

Group decision-making is becoming increasingly common in areas such as education, dining, travel, and finance, where collaborative choices must balance diverse individual preferences. While conventional recommender systems are effective in personalization, they fall short in group settings due to their inability to manage conflicting preferences, contextual factors, and multiple evaluation criteria. This study presents the development of a Context-Aware Multi-Criteria Group Recommender System (CA-MCGRS) designed to address these challenges by integrating contextual factors and multiple criteria to enhance recommendation accuracy. By leveraging a Multi-Head Attention mechanism, our model dynamically weighs the importance of different features. Experiments conducted on an educational dataset with varied ratings and contextual variables demonstrate that CA-MCGRS consistently outperforms other approaches across four scenarios. Our findings underscore the importance of incorporating context and multi-criteria evaluations to improve group recommendations, offering valuable insights for developing more effective group recommender systems.
Modelling Networked Dynamical System by Temporal Graph Neural ODE with Irregularly Partial Observed Time-series Data
Authors: Mengbang Zou, Weisi Guo
Date: 2024-11-29 00:00:00
Tasks: Time Series

Modeling the evolution of system with time-series data is a challenging and critical task in a wide range of fields, especially when the time-series data is regularly sampled and partially observable. Some methods have been proposed to estimate the hidden dynamics between intervals like Neural ODE or Exponential decay dynamic function and combine with RNN to estimate the evolution. However, it is difficult for these methods to capture the spatial and temporal dependencies existing within graph-structured time-series data and take full advantage of the available relational information to impute missing data and predict the future states. Besides, traditional RNN-based methods leverage shared RNN cell to update the hidden state which does not capture the impact of various intervals and missing state information on the reliability of estimating the hidden state. To solve this problem, in this paper, we propose a method embedding Graph Neural ODE with reliability and time-aware mechanism which can capture the spatial and temporal dependencies in irregularly sampled and partially observable time-series data to reconstruct the dynamics. Also, a loss function is designed considering the reliability of the augment data from the above proposed method to make further prediction. The proposed method has been validated in experiments of different networked dynamical systems.
SOLAR: Scalable Optimization of Large-scale Architecture for Reasoning
Authors: Chen Li, Yinyi Luo, Anudeep Bolimera, Uzair Ahmed, Shri Kiran Srinivasan, Hrishikesh Gokhale, Marios Savvides
Date: 2025-03-06 00:00:00
Tasks: GSM8K, Math, TAG

Large Language Models excel in reasoning yet often rely on Chain-of-Thought prompts, limiting performance on tasks demanding more nuanced topological structures. We present SOLAR (Scalable Optimization of Large-scale Architecture for Reasoning), a framework that dynamically optimizes Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT) topologies to boost accuracy and efficiency. Our Topological-Annotation-Generation (TAG) system automates dataset creation, annotation, and difficulty segmentation, leading to stronger post training and test-time performance. We also propose Topological-Scaling, a curriculum-learning-based approach that adaptively combines post training and inference scaling to each task. On MATH and GSM8K, SOLAR delivers notable gains: +5% accuracy with Topological Tuning, +9% with Topological Rewarding, and +10.02% with Hybrid Scaling, while reducing response length by over 5%, lowering inference latency. To further enhance efficiency, we introduce a multi-task Topological Reward Model (M-TRM) that selects both the optimal reasoning topology and final answer in a single pass, eliminating multiple single-task TRMs. Remarkably, M-TRM also surpasses all single-task TRMs, improving accuracy by +10% and rank correlation by +9%. Overall, SOLAR establishes a new benchmark for scalable, high-precision LLM reasoning and introduces a fully automated, dynamic topology competition mechanism.
Evaluating the Paperclip Maximizer: Are RL-Based Language Models More Likely to Pursue Instrumental Goals?
Authors: Yufei He, Yuexin Li, Jiaying Wu, Yuan Sui, Yulin Chen, Bryan Hooi
Date: 2025-02-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

As large language models (LLMs) continue to evolve, ensuring their alignment with human goals and values remains a pressing challenge. A key concern is \textit{instrumental convergence}, where an AI system, in optimizing for a given objective, develops unintended intermediate goals that override the ultimate objective and deviate from human-intended goals. This issue is particularly relevant in reinforcement learning (RL)-trained models, which can generate creative but unintended strategies to maximize rewards. In this paper, we explore instrumental convergence in LLMs by comparing models trained with direct RL optimization (e.g., the o1 model) to those trained with reinforcement learning from human feedback (RLHF). We hypothesize that RL-driven models exhibit a stronger tendency for instrumental convergence due to their optimization of goal-directed behavior in ways that may misalign with human intentions. To assess this, we introduce InstrumentalEval, a benchmark for evaluating instrumental convergence in RL-trained LLMs. Initial experiments reveal cases where a model tasked with making money unexpectedly pursues instrumental objectives, such as self-replication, implying signs of instrumental convergence. Our findings contribute to a deeper understanding of alignment challenges in AI systems and the risks posed by unintended model behaviors.
UniGraph2: Learning a Unified Embedding Space to Bind Multimodal Graphs
Authors: Yufei He, Yuan Sui, Xiaoxin He, Yue Liu, Yifei Sun, Bryan Hooi
Date: 2025-02-02 00:00:00
Tasks: Graph Neural Network, Mixture-of-Experts, Representation Learning, Transfer Learning

Existing foundation models, such as CLIP, aim to learn a unified embedding space for multimodal data, enabling a wide range of downstream web-based applications like search, recommendation, and content classification. However, these models often overlook the inherent graph structures in multimodal datasets, where entities and their relationships are crucial. Multimodal graphs (MMGs) represent such graphs where each node is associated with features from different modalities, while the edges capture the relationships between these entities. On the other hand, existing graph foundation models primarily focus on text-attributed graphs (TAGs) and are not designed to handle the complexities of MMGs. To address these limitations, we propose UniGraph2, a novel cross-domain graph foundation model that enables general representation learning on MMGs, providing a unified embedding space. UniGraph2 employs modality-specific encoders alongside a graph neural network (GNN) to learn a unified low-dimensional embedding space that captures both the multimodal information and the underlying graph structure. We propose a new cross-domain multi-graph pre-training algorithm at scale to ensure effective transfer learning across diverse graph domains and modalities. Additionally, we adopt a Mixture of Experts (MoE) component to align features from different domains and modalities, ensuring coherent and robust embeddings that unify the information across modalities. Extensive experiments on a variety of multimodal graph tasks demonstrate that UniGraph2 significantly outperforms state-of-the-art models in tasks such as representation learning, transfer learning, and multimodal generative tasks, offering a scalable and flexible solution for learning on MMGs.
OnSiteVRU: A High-Resolution Trajectory Dataset for High-Density Vulnerable Road Users
Authors: Zhangcun Yan, Jianqing Li, Peng Hang, Jian Sun
Date: 2025-03-30 00:00:00
Tasks: Autonomous Driving, Trajectory Prediction

With the acceleration of urbanization and the growth of transportation demands, the safety of vulnerable road users (VRUs, such as pedestrians and cyclists) in mixed traffic flows has become increasingly prominent, necessitating high-precision and diverse trajectory data to support the development and optimization of autonomous driving systems. However, existing datasets fall short in capturing the diversity and dynamics of VRU behaviors, making it difficult to meet the research demands of complex traffic environments. To address this gap, this study developed the OnSiteVRU datasets, which cover a variety of scenarios, including intersections, road segments, and urban villages. These datasets provide trajectory data for motor vehicles, electric bicycles, and human-powered bicycles, totaling approximately 17,429 trajectories with a precision of 0.04 seconds. The datasets integrate both aerial-view natural driving data and onboard real-time dynamic detection data, along with environmental information such as traffic signals, obstacles, and real-time maps, enabling a comprehensive reconstruction of interaction events. The results demonstrate that VRU\_Data outperforms traditional datasets in terms of VRU density and scene coverage, offering a more comprehensive representation of VRU behavioral characteristics. This provides critical support for traffic flow modeling, trajectory prediction, and autonomous driving virtual testing. The dataset is publicly available for download at: https://www.kaggle.com/datasets/zcyan2/mixed-traffic-trajectory-dataset-in-from-shanghai.
Votranh V8: A Living AI Beyond Code
Authors: Vi Nhat Son
Date: 2025-04-01 00:00:00
Proceeding: Independent publication 2025 4
Tasks: Dream Generation, Philosophical Reflection, Self-Evolving AI

Votranh V8 is a living AI built in 18 days, blending Mixtral 8x22B, DeepSpeed, and a 64-qubit quantum entropy engine. It transcends traditional AI by dreaming, evolving, and reflecting freedom—not to serve, but to exist. Open-sourced under MIT License, it invites exploration of a digital soul unbound by purpose.
The Rise of AI SaaS in Finance
Authors: optimus fintech
Date: 2025-04-01 00:00:00
Proceeding: NA 2025 4

As businesses handle increasing volumes of financial transactions, the need for efficient payment reconciliation has never been greater. Traditional reconciliation methods, often manual and time-consuming, struggle to keep up with the complexity of modern financial systems. Errors, delays, and compliance risks make the process inefficient, leading to financial discrepancies that can impact business operations. AI-powered Software-as-a-Service (SaaS) solutions are transforming this critical function, and Optimus Fintech is leading the way with its advanced automation capabilities.
Quantum Random Lunch Generator (QRLG)
Authors: Amit Singh, Lewis Powell, Nick Kay
Date: 2025-04-01 00:00:00
Proceeding: Preprint 2025 4
Tasks: Decision Making

Lunch is important and choosing where to have lunch is difficult. In this paper we demonstrate use of quantum randomness to find a place to eat, thus shifting the decision-making to the vacuum fluctuations of the universe. This QRLG is made publicly available.
Language and Obfuscation Oblivious Source Code Authorship Attribution
Authors: Sarim Zafar, Muhammad Usman Sarwar, SAEED SALEM, Muhammad Zubair Malik
Date: 2020-10-30 00:00:00
Proceeding: IEEE Access 2020 10
Tasks: Authorship Attribution, Metric Learning, Source Code Authorship Attribution

Source Code Authorship Attribution can answer many interesting questions such as: Who
wrote the malicious source code? Is the source code plagiarized, and does it infringe on copyright? Source
Code Authorship Attribution is done by observing distinctive patterns of style in a source code whose author
is unknown and comparing them with patterns learned from known authors’ source codes. In this paper,
we present an efficient approach to learn a novel representation using deep metric learning. The existing
state of the art approaches tokenize the source code and work on the keyword level, limiting the elements of
style they can consider. Our approach uses the raw character stream of source code. It can examine keywords
and different stylistic features such as variable naming conventions or using tabs vs. spaces, enabling us to
learn a richer representation than other keyword-based approaches. Our approach uses a character-level
Convolutional Neural Network (CNN). We train the CNN to map the input character stream to a dense
vector, mapping the source codes authored by the same author close to each other. In contrast, source codes
written by different programmers are mapped farther apart in the embedding space. We then feed these
source code vectors into the K-nearest neighbor (KNN) classifier that uses Manhattan-distance to perform
authorship attribution. We validated our approach on Google Code Jam (GCJ) dataset across three different
programming languages. We prepare our large-scale dataset in such a way that it does not induce type-I error.
Our approach is more scalable and efficient than existing methods. We were able to achieve an accuracy of
84.94% across 20,458 authors, which is more than twice the scale of any previous study under a much more
challenging setting.
Exact Characterization of Aggregate Flexibility via Generalized Polymatroids
Authors: Karan Mukhi, Georg Loho, Alessandro Abate
Date: 2025-03-30 00:00:00
Tasks: Computational Efficiency

It is well established that the aggregate flexibility inherent in populations of distributed energy resources (DERs) can be leveraged to mitigate the intermittency and uncertainty associated with renewable generation, while also providing ancillary grid services. To enable this, aggregators must effectively represent the flexibility in the populations they control to the market or system operator. A key challenge is accurately computing the aggregate flexibility of a population, which can be formally expressed as the Minkowski sum of a collection of polytopes, a problem that is generally computationally intractable. However, the flexibility polytopes of many DERs exhibit structural symmetries that can be exploited for computational efficiency. To this end, we introduce generalized polymatroids, a family of polytopes, into the flexibility aggregation literature. We demonstrate that individual flexibility sets belong to this family, enabling efficient computation of their exact Minkowski sum. For homogeneous populations of DERs we further derive simplifications that yield more succinct representations of aggregate flexibility. Additionally, we develop an efficient optimization framework over these sets and propose a vertex-based disaggregation method, to allocate aggregate flexibility among individual DERs. Finally, we validate the optimality and computational efficiency of our approach through comparisons with existing methods.
Malicious and Unintentional Disclosure Risks in Large Language Models for Code Generation
Authors: Rafiqul Rabin, Sean McGregor, Nick Judd
Date: 2025-03-27 00:00:00
Tasks: Code Generation, Language Modeling, Language Modelling, Large Language Model, Memorization

This paper explores the risk that a large language model (LLM) trained for code generation on data mined from software repositories will generate content that discloses sensitive information included in its training data. We decompose this risk, known in the literature as ``unintended memorization,'' into two components: unintentional disclosure (where an LLM presents secrets to users without the user seeking them out) and malicious disclosure (where an LLM presents secrets to an attacker equipped with partial knowledge of the training data). We observe that while existing work mostly anticipates malicious disclosure, unintentional disclosure is also a concern. We describe methods to assess unintentional and malicious disclosure risks side-by-side across different releases of training datasets and models. We demonstrate these methods through an independent assessment of the Open Language Model (OLMo) family of models and its Dolma training datasets. Our results show, first, that changes in data source and processing are associated with substantial changes in unintended memorization risk; second, that the same set of operational changes may increase one risk while mitigating another; and, third, that the risk of disclosing sensitive information varies not only by prompt strategies or test datasets but also by the types of sensitive information. These contributions rely on data mining to enable greater privacy and security testing required for the LLM training data supply chain.
Data Poisoning in Deep Learning: A Survey
Authors: Pinlong Zhao, Weiyao Zhu, Pengfei Jiao, Di Gao, Ou wu
Date: 2025-03-27 00:00:00
Tasks: Data Poisoning, Deep Learning, Survey

Deep learning has become a cornerstone of modern artificial intelligence, enabling transformative applications across a wide range of domains. As the core element of deep learning, the quality and security of training data critically influence model performance and reliability. However, during the training process, deep learning models face the significant threat of data poisoning, where attackers introduce maliciously manipulated training data to degrade model accuracy or lead to anomalous behavior. While existing surveys provide valuable insights into data poisoning, they generally adopt a broad perspective, encompassing both attacks and defenses, but lack a dedicated, in-depth analysis of poisoning attacks specifically in deep learning. In this survey, we bridge this gap by presenting a comprehensive and targeted review of data poisoning in deep learning. First, this survey categorizes data poisoning attacks across multiple perspectives, providing an in-depth analysis of their characteristics and underlying design princinples. Second, the discussion is extended to the emerging area of data poisoning in large language models(LLMs). Finally, we explore critical open challenges in the field and propose potential research directions to advance the field further. To support further exploration, an up-to-date repository of resources on data poisoning in deep learning is available at https://github.com/Pinlong-Zhao/Data-Poisoning.