pwc-archive/papers-with-abstracts huggingface dataset:
Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation
Authors: Qing Yu, Xiaobei Wang, Shuchang Liu, Yandong Bai, Xiaoyu Yang, Xueliang Wang, Chang Meng, Shanshan Wu, Hailan Yang, Huihui Xiao, Xiang Li, Fan Yang, Xiaoqiang Feng, Lantao Hu, Han Li, Kun Gai, Lixin Zou
Date: 2025-05-16 00:00:00
Tasks: General Knowledge, Large Language Model, Learning-To-Rank, Recommendation Systems, TAG, World Knowledge

Recommender systems filter contents/items valuable to users by inferring preferences from user features and historical behaviors. Mainstream approaches follow the learning-to-rank paradigm, which focus on discovering and modeling item topics (e.g., categories), and capturing user preferences on these topics based on historical interactions. However, this paradigm often neglects the modeling of user characteristics and their social roles, which are logical confounders influencing the correlated interest and user preference transition. To bridge this gap, we introduce the user role identification task and the behavioral logic modeling task that aim to explicitly model user roles and learn the logical relations between item topics and user social roles. We show that it is possible to explicitly solve these tasks through an efficient integration framework of Large Language Model (LLM) and recommendation systems, for which we propose TagCF. On the one hand, TagCF exploits the (Multi-modal) LLM's world knowledge and logic inference ability to extract realistic tag-based virtual logic graphs that reveal dynamic and expressive knowledge of users, refining our understanding of user behaviors. On the other hand, TagCF presents empirically effective integration modules that take advantage of the extracted tag-logic information, augmenting the recommendation performance. We conduct both online experiments and offline experiments with industrial and public datasets as verification of TagCF's effectiveness, and we empirically show that the user role modeling strategy is potentially a better choice than the modeling of item topics. Additionally, we provide evidence that the extracted logic graphs are empirically a general and transferable knowledge that can benefit a wide range of recommendation tasks.
From Intent Discovery to Recognition with Topic Modeling and Synthetic Data
Authors: Aaron Rodrigues, Mahmood Hegazy, Azzam Naeem
Date: 2025-05-16 00:00:00
Tasks: Diversity, Intent Discovery, Intent Recognition, Recommendation Systems, Specificity

Understanding and recognizing customer intents in AI systems is crucial, particularly in domains characterized by short utterances and the cold start problem, where recommender systems must include new products or services without sufficient real user data. Customer utterances are characterized by infrequent word co-occurences and high term variability, which poses significant challenges for traditional methods in specifying distinct user needs and preparing synthetic queries. To address this, we propose an agentic LLM framework for topic modeling and synthetic query generation, which accelerates the discovery and recognition of customer intents. We first apply hierarchical topic modeling and intent discovery to expand a human-curated taxonomy from 36 generic user intents to 278 granular intents, demonstrating the potential of LLMs to significantly enhance topic specificity and diversity. Next, to support newly discovered intents and address the cold start problem, we generate synthetic user query data, which augments real utterances and reduces dependency on human annotation, especially in low-resource settings. Topic model experiments show substantial improvements in coherence and relevance after topic expansion, while synthetic data experiments indicate that in-class few-shot prompting significantly improves the quality and utility of synthetic queries without compromising diversity. We also show that LLM-generated intent descriptions and keywords can effectively substitute for human-curated versions when used as context for synthetic query generation. Our research underscores the scalability and utility of LLM agents in topic modeling and highlights the strategic use of synthetic utterances to enhance dataset variability and coverage for intent recognition. We present a comprehensive and robust framework for online discovery and recognition of new customer intents in dynamic domains.
mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
Authors: Chuan Xu, Qiaosheng Chen, Yutong Feng, Gong Cheng
Date: 2025-05-16 00:00:00
Tasks: Information Retrieval, Knowledge Graphs, Question Answering, RAG, Retrieval, Retrieval-augmented Generation, Text Retrieval

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing the capabilities of large language models. However, existing RAG evaluation predominantly focuses on text retrieval and relies on opaque, end-to-end assessments of generated outputs. To address these limitations, we introduce mmRAG, a modular benchmark designed for evaluating multi-modal RAG systems. Our benchmark integrates queries from six diverse question-answering datasets spanning text, tables, and knowledge graphs, which we uniformly convert into retrievable documents. To enable direct, granular evaluation of individual RAG components -- such as the accuracy of retrieval and query routing -- beyond end-to-end generation quality, we follow standard information retrieval procedures to annotate document relevance and derive dataset relevance. We establish baseline performance by evaluating a wide range of RAG implementations on mmRAG.
User-centric Music Recommendations
Authors: Jaime Ramirez Castillo, M. Julia Flores, Ann E. Nicholson
Date: 2025-05-16 00:00:00

This work presents a user-centric recommendation framework, designed as a pipeline with four distinct, connected, and customizable phases. These phases are intended to improve explainability and boost user engagement. We have collected the historical Last.fm track playback records of a single user over approximately 15 years. The collected dataset includes more than 90,000 playbacks and approximately 14,000 unique tracks. From track playback records, we have created a dataset of user temporal contexts (each row is a specific moment when the user listened to certain music descriptors). As music descriptors, we have used community-contributed Last.fm tags and Spotify audio features. They represent the music that, throughout years, the user has been listening to. Next, given the most relevant Last.fm tags of a moment (e.g. the hour of the day), we predict the Spotify audio features that best fit the user preferences in that particular moment. Finally, we use the predicted audio features to find tracks similar to these features. The final aim is to recommend (and discover) tracks that the user may feel like listening to at a particular moment. For our initial study case, we have chosen to predict only a single audio feature target: danceability. The framework, however, allows to include more target variables. The ability to learn the musical habits from a single user can be quite powerful, and this framework could be extended to other users.
On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective
Authors: Donald Loveland, Mingxuan Ju, Tong Zhao, Neil Shah, Danai Koutra
Date: 2025-05-16 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Collaborative filtering (CF) enables large-scale recommendation systems by encoding information from historical user-item interactions into dense ID-embedding tables. However, as embedding tables grow, closed-form solutions become impractical, often necessitating the use of mini-batch gradient descent for training. Despite extensive work on designing loss functions to train CF models, we argue that one core component of these pipelines is heavily overlooked: weight decay. Attaining high-performing models typically requires careful tuning of weight decay, regardless of loss, yet its necessity is not well understood. In this work, we question why weight decay is crucial in CF pipelines and how it impacts training. Through theoretical and empirical analysis, we surprisingly uncover that weight decay's primary function is to encode popularity information into the magnitudes of the embedding vectors. Moreover, we find that tuning weight decay acts as a coarse, non-linear knob to influence preference towards popular or unpopular items. Based on these findings, we propose PRISM (Popularity-awaRe Initialization Strategy for embedding Magnitudes), a straightforward yet effective solution to simplify the training of high-performing CF models. PRISM pre-encodes the popularity information typically learned through weight decay, eliminating its necessity. Our experiments show that PRISM improves performance by up to 4.77% and reduces training times by 38.48%, compared to state-of-the-art training strategies. Additionally, we parameterize PRISM to modulate the initialization strength, offering a cost-effective and meaningful strategy to mitigate popularity bias.
The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems
Authors: Petr Kasalický, Martin Spišák, Vojtěch Vančura, Daniel Bohuněk, Rodrigo Alves, Pavel Kordík
Date: 2025-05-16 00:00:00
Tasks: Recommendation Systems, Retrieval

Industry-scale recommender systems face a core challenge: representing entities with high cardinality, such as users or items, using dense embeddings that must be accessible during both training and inference. However, as embedding sizes grow, memory constraints make storage and access increasingly difficult. We describe a lightweight, learnable embedding compression technique that projects dense embeddings into a high-dimensional, sparsely activated space. Designed for retrieval tasks, our method reduces memory requirements while preserving retrieval performance, enabling scalable deployment under strict resource constraints. Our results demonstrate that leveraging sparsity is a promising approach for improving the efficiency of large-scale recommenders. We release our code at https://github.com/recombee/CompresSAE.
CRISP: Clustering Multi-Vector Representations for Denoising and Pruning
Authors: João Veneroso, Rajesh Jayaram, Jinmeng Rao, Gustavo Hernández Ábrego, Majid Hadian, Daniel Cer
Date: 2025-05-16 00:00:00
Tasks: Clustering, Denoising, Information Retrieval, Retrieval

Multi-vector models, such as ColBERT, are a significant advancement in neural information retrieval (IR), delivering state-of-the-art performance by representing queries and documents by multiple contextualized token-level embeddings. However, this increased representation size introduces considerable storage and computational overheads which have hindered widespread adoption in practice. A common approach to mitigate this overhead is to cluster the model's frozen vectors, but this strategy's effectiveness is fundamentally limited by the intrinsic clusterability of these embeddings. In this work, we introduce CRISP (Clustered Representations with Intrinsic Structure Pruning), a novel multi-vector training method which learns inherently clusterable representations directly within the end-to-end training process. By integrating clustering into the training phase rather than imposing it post-hoc, CRISP significantly outperforms post-hoc clustering at all representation sizes, as well as other token pruning methods. On the BEIR retrieval benchmarks, CRISP achieves a significant rate of ~3x reduction in the number of vectors while outperforming the original unpruned model. This indicates that learned clustering effectively denoises the model by filtering irrelevant information, thereby generating more robust multi-vector representations. With more aggressive clustering, CRISP achieves an 11x reduction in the number of vectors with only a $3.6\%$ quality loss.
SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval
Authors: Qiwei Peng, Robert Moro, Michal Gregor, Ivan Srba, Simon Ostermann, Marian Simko, Juraj Podroužek, Matúš Mesarčík, Jaroslav Kopčan, Anders Søgaard
Date: 2025-05-15 00:00:00
Tasks: Fact Checking, Retrieval

The rapid spread of online disinformation presents a global challenge, and machine learning has been widely explored as a potential solution. However, multilingual settings and low-resource languages are often neglected in this field. To address this gap, we conducted a shared task on multilingual claim retrieval at SemEval 2025, aimed at identifying fact-checked claims that match newly encountered claims expressed in social media posts across different languages. The task includes two subtracks: (1) a monolingual track, where social posts and claims are in the same language, and (2) a crosslingual track, where social posts and claims might be in different languages. A total of 179 participants registered for the task contributing to 52 test submissions. 23 out of 31 teams have submitted their system papers. In this paper, we report the best-performing systems as well as the most common and the most effective approaches across both subtracks. This shared task, along with its dataset and participating systems, provides valuable insights into multilingual claim retrieval and automated fact-checking, supporting future research in this field.
Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate
Authors: Ziyang Huang, Wangtao Sun, Jun Zhao, Kang Liu
Date: 2025-05-16 00:00:00
Tasks: Retrieval

This paper systematically addresses the challenges of rule retrieval, a crucial yet underexplored area. Vanilla retrieval methods using sparse or dense retrievers to directly search for relevant rules to support downstream reasoning, often suffer from low accuracy. This is primarily due to a significant semantic gap between the instantiated facts in the queries and the abstract representations of the rules. Such misalignment results in suboptimal retrieval quality, which in turn negatively impacts reasoning performance. To overcome these challenges, we propose Self-Induction Augmented Retrieval (SIAR), a novel approach that utilizes Large Language Models (LLMs) to induce potential inferential rules that might offer benefits for reasoning by abstracting the underlying knowledge and logical structure in queries. These induced rules are then used for query augmentation to improve retrieval effectiveness. Additionally, we introduce Rule Relevance ReEstimate (R$^3$), a method that re-estimates the relevance of retrieved rules by assessing whether the abstract knowledge they contain can be instantiated to align with the facts in the queries and the helpfulness for reasoning. Extensive experiments across various settings demonstrate the effectiveness and versatility of our proposed methods.
Semantic Caching of Contextual Summaries for Efficient Question-Answering with Language Models
Authors: Camille Couturier, Spyros Mastorakis, Haiying Shen, Saravan Rajmohan, Victor Rühle
Date: 2025-05-16 00:00:00
Tasks: Question Answering, Retrieval, Retrieval-augmented Generation, TriviaQA

Large Language Models (LLMs) are increasingly deployed across edge and cloud platforms for real-time question-answering and retrieval-augmented generation. However, processing lengthy contexts in distributed systems incurs high computational overhead, memory usage, and network bandwidth. This paper introduces a novel semantic caching approach for storing and reusing intermediate contextual summaries, enabling efficient information reuse across similar queries in LLM-based QA workflows. Our method reduces redundant computations by up to 50-60% while maintaining answer accuracy comparable to full document processing, as demonstrated on NaturalQuestions, TriviaQA, and a synthetic ArXiv dataset. This approach balances computational cost and response quality, critical for real-time AI assistants.
Evolution imposes an inductive bias that alters and accelerates learning dynamics
Authors: Benjamin Midler, Alejandro Pan Vazquez
Date: 2025-05-15 00:00:00
Tasks: Few-Shot Learning, Inductive Bias

The learning dynamics of biological brains and artificial neural networks are of interest to both neuroscience and machine learning. A key difference between them is that neural networks are often trained from a randomly initialized state whereas each brain is the product of generations of evolutionary optimization, yielding innate structures that enable few-shot learning and inbuilt reflexes. Artificial neural networks, by contrast, require non-ethological quantities of training data to attain comparable performance. To investigate the effect of evolutionary optimization on the learning dynamics of neural networks, we combined algorithms simulating natural selection and online learning to produce a method for evolutionarily conditioning artificial neural networks, and applied it to both reinforcement and supervised learning contexts. We found the evolutionary conditioning algorithm, by itself, performs comparably to an unoptimized baseline. However, evolutionarily conditioned networks show signs of unique and latent learning dynamics, and can be rapidly fine-tuned to optimal performance. These results suggest evolution constitutes an inductive bias that tunes neural systems to enable rapid learning.
Evolutionary training-free guidance in diffusion model for 3D multi-objective molecular generation
Authors: Ruiqing Sun, Dawei Feng, Sen yang, Yijie Wang, Huaimin Wang
Date: 2025-05-16 00:00:00
Tasks: Denoising, Drug Design

Discovering novel 3D molecular structures that simultaneously satisfy multiple property targets remains a central challenge in materials and drug design. Although recent diffusion-based models can generate 3D conformations, they require expensive retraining for each new property or property-combination and lack flexibility in enforcing structural constraints. We introduce EGD (Evolutionary Guidance in Diffusion), a training-free framework that embeds evolutionary operators directly into the diffusion sampling process. By performing crossover on noise-perturbed samples and then denoising them with a pretrained Unconditional diffusion model, EGD seamlessly blends structural fragments and steers generation toward user-specified objectives without any additional model updates. On both single- and multi-target 3D conditional generation tasks-and on multi-objective optimization of quantum properties EGD outperforms state-of-the-art conditional diffusion methods in accuracy and runs up to five times faster per generation. In the single-objective optimization of protein ligands, EGD enables customized ligand generation. Moreover, EGD can embed arbitrary 3D fragments into the generated molecules while optimizing multiple conflicting properties in one unified process. This combination of efficiency, flexibility, and controllable structure makes EGD a powerful tool for rapid, guided exploration of chemical space.
STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking
Authors: Sicheng Shen, Dongcheng Zhao, Linghao Feng, Zeyang Yue, Jindong Li, Tenglong Li, Guobin Shen, Yi Zeng
Date: 2025-05-16 00:00:00
Tasks: Benchmarking

Spiking Transformers have recently emerged as promising architectures for combining the efficiency of spiking neural networks with the representational power of self-attention. However, the lack of standardized implementations, evaluation pipelines, and consistent design choices has hindered fair comparison and principled analysis. In this paper, we introduce \textbf{STEP}, a unified benchmark framework for Spiking Transformers that supports a wide range of tasks, including classification, segmentation, and detection across static, event-based, and sequential datasets. STEP provides modular support for diverse components such as spiking neurons, input encodings, surrogate gradients, and multiple backends (e.g., SpikingJelly, BrainCog). Using STEP, we reproduce and evaluate several representative models, and conduct systematic ablation studies on attention design, neuron types, encoding schemes, and temporal modeling capabilities. We also propose a unified analytical model for energy estimation, accounting for spike sparsity, bitwidth, and memory access, and show that quantized ANNs may offer comparable or better energy efficiency. Our results suggest that current Spiking Transformers rely heavily on convolutional frontends and lack strong temporal modeling, underscoring the need for spike-native architectural innovations. The full code is available at: https://github.com/Fancyssc/STEP
Lightweight LIF-only SNN accelerator using differential time encoding
Authors: Daniel Windhager, Lothar Ratschbacher, Bernhard A. Moser, Michael Lunglmayr
Date: 2025-05-16 00:00:00

Spiking Neural Networks (SNNs) offer a promising solution to the problem of increasing computational and energy requirements for modern Machine Learning (ML) applications. Due to their unique data representation choice of using spikes and spike trains, they mostly rely on additions and thresholding operations to achieve results approaching state-of-the-art (SOTA) Artificial Neural Networks (ANNs). This advantage is hindered by the fact that their temporal characteristic does not map well to already existing accelerator hardware like GPUs. Therefore, this work will introduce a hardware accelerator architecture capable of computing feedforward LIF-only SNNs, as well as an accompanying encoding method to efficiently encode already existing data into spike trains. Together, this leads to a design capable of >99% accuracy on the MNIST dataset, with ~0.29ms inference times on a Xilinx Ultrascale+ FPGA, as well as ~0.17ms on a custom ASIC using the open-source predictive 7nm ASAP7 PDK. Furthermore, this work will showcase the advantages of the previously presented differential time encoding for spikes, as well as provide proof that merging spikes from different synapses given in differential time encoding can be done efficiently in hardware.
A Step towards Interpretable Multimodal AI Models with MultiFIX
Authors: Mafalda Malafaia, Thalea Schlender, Tanja Alderliesten, Peter A. N. Bosman
Date: 2025-05-16 00:00:00

Real-world problems are often dependent on multiple data modalities, making multimodal fusion essential for leveraging diverse information sources. In high-stakes domains, such as in healthcare, understanding how each modality contributes to the prediction is critical to ensure trustworthy and interpretable AI models. We present MultiFIX, an interpretability-driven multimodal data fusion pipeline that explicitly engineers distinct features from different modalities and combines them to make the final prediction. Initially, only deep learning components are used to train a model from data. The black-box (deep learning) components are subsequently either explained using post-hoc methods such as Grad-CAM for images or fully replaced by interpretable blocks, namely symbolic expressions for tabular data, resulting in an explainable model. We study the use of MultiFIX using several training strategies for feature extraction and predictive modeling. Besides highlighting strengths and weaknesses of MultiFIX, experiments on a variety of synthetic datasets with varying degrees of interaction between modalities demonstrate that MultiFIX can generate multimodal models that can be used to accurately explain both the extracted features and their integration without compromising predictive performance.