pwc-archive/papers-with-abstracts huggingface dataset:
Pickup & Delivery with Time Windows and Transfers: combining decomposition with metaheuristics
Authors: Ioannis Avgerinos, Ioannis Mourtos, Nikolaos Tsompanidis, Georgios Zois
Date: 2025-05-04 00:00:00

This paper examines the generalisation of the Pickup and Delivery Problem that allows mid-route load exchanges among vehicles and obeys strict time-windows at all locations. We propose a novel Logic-Based Benders Decomposition (LBBD) that improves optimality gaps for all benchmarks in the literature and scales up to handle larger ones. To tackle even larger instances, we introduce a refined Large Neighborhood Search (LNS) algorithm that improves the adaptability of LNS beyond case-specific configurations appearing in related literature. To bridge the gap in benchmark availability, we develop an instance generator that allows for extensive experimentation. For moderate datasets (25 and 50 requests), we evaluate the performance of both LBBD and LNS, the former being able to close the gap and the latter capable of providing near-optimal solutions. For larger instances (75 and 100 requests), we recreate indicative state-of-the-art metaheuristics to highlight the improvements introduced by our LNS refinements, while establishing its scalability.
Interpreting Multilingual and Document-Length Sensitive Relevance Computations in Neural Retrieval Models through Axiomatic Causal Interventions
Authors: Oliver Savolainen, Dur e Najaf Amjad, Roxana Petcu
Date: 2025-05-04 00:00:00
Tasks: Information Retrieval, Retrieval

This reproducibility study analyzes and extends the paper "Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models," which investigates how neural retrieval models encode task-relevant properties such as term frequency. We reproduce key experiments from the original paper, confirming that information on query terms is captured in the model encoding. We extend this work by applying activation patching to Spanish and Chinese datasets and by exploring whether document-length information is encoded in the model as well. Our results confirm that the designed activation patching method can isolate the behavior to specific components and tokens in neural retrieval models. Moreover, our findings indicate that the location of term frequency generalizes across languages and that in later layers, the information for sequence-level tasks is represented in the CLS token. The results highlight the need for further research into interpretability in information retrieval and reproducibility in machine learning research. Our code is available at https://github.com/OliverSavolainen/axiomatic-ir-reproduce.
Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data
Authors: Zhong Guan, Likang Wu, Hongke Zhao, Ming He, Jianpin Fan
Date: 2025-05-04 00:00:00

Attention mechanisms are critical to the success of large language models (LLMs), driving significant advancements in multiple fields. However, for graph-structured data, which requires emphasis on topological connections, they fall short compared to message-passing mechanisms on fixed links, such as those employed by Graph Neural Networks (GNNs). This raises a question: ``Does attention fail for graphs in natural language settings?'' Motivated by these observations, we embarked on an empirical study from the perspective of attention mechanisms to explore how LLMs process graph-structured data. The goal is to gain deeper insights into the attention behavior of LLMs over graph structures. We uncovered unique phenomena regarding how LLMs apply attention to graph-structured data and analyzed these findings to improve the modeling of such data by LLMs. The primary findings of our research are: 1) While LLMs can recognize graph data and capture text-node interactions, they struggle to model inter-node relationships within graph structures due to inherent architectural constraints. 2) The attention distribution of LLMs across graph nodes does not align with ideal structural patterns, indicating a failure to adapt to graph topology nuances. 3) Neither fully connected attention nor fixed connectivity is optimal; each has specific limitations in its application scenarios. Instead, intermediate-state attention windows improve LLM training performance and seamlessly transition to fully connected windows during inference. Source code: \href{https://github.com/millioniron/LLM_exploration}{LLM4Exploration}
Subspace Aggregation Query and Index Generation for Multidimensional Resource Space Model
Authors: Xiaoping Sun, Hai Zhuge
Date: 2025-05-04 00:00:00

Organizing resources in a multidimensional classification space is an approach to efficiently managing and querying large-scale resources. This paper defines an aggregation query on subspace defined by a range on the partial order on coordinate tree at each dimension, where each point contains resources aggregated along the paths of partial order relations on the points so that aggregated resources at each point within the subspace can be measured, ranked and selected. To efficiently locate non-empty points in a large subspace, an approach to generating graph index is proposed to build inclusion links with partial order relations on coordinates of dimensions to enable a subspace query to reach non-empty points by following indexing links and aggregate resources along indexing paths back to their super points. Generating such an index is costly as the number of children of an index node can be very large so that the total number of indexing nodes is unbounded. The proposed approach adopts the following strategies to reduce the cost: (1) adding intersection links between two indexing nodes, which can better reduce query processing costs while controlling the number of nodes of the graph index; (2) intersection links are added between two nodes according to the probabilistic distribution calculated for estimating the costs of adding intersection between two nodes; (3) coordinates at one dimension having more resources are split by coordinates at another dimension to balance the number of resources hold by indexing nodes; and, (4) short-cut links are added between sibling coordinates of coordinate trees to make an efficient query on linear order coordinates. Analysis and experiments verified the effectiveness of the generated index in supporting subspace aggregation query. This work makes significant contributions to the development of data model based on multi-dimensional classification.
Overview of AI Grading of Physics Olympiad Exams
Authors: Lachlan McGinness
Date: 2025-05-04 00:00:00
Tasks: High School Physics, Systematic Literature Review

Automatically grading the diverse range of question types in high school physics problem is a challenge that requires automated grading techniques from different fields. We report the findings of a Systematic Literature Review of potential physics grading techniques. We propose a multi-modal AI grading framework to address these challenges and examine our framework in light of Australia's AI Ethical Principles.
Tricolore: Multi-Behavior User Profiling for Enhanced Candidate Generation in Recommender Systems
Authors: Xiao Zhou, Zhongxiang Zhao, Hanze Guo
Date: 2025-05-04 00:00:00
Tasks: Recommendation Systems

Online platforms aggregate extensive user feedback across diverse behaviors, providing a rich source for enhancing user engagement. Traditional recommender systems, however, typically optimize for a single target behavior and represent user preferences with a single vector, limiting their ability to handle multiple important behaviors or optimization objectives. This conventional approach also struggles to capture the full spectrum of user interests, resulting in a narrow item pool during candidate generation. To address these limitations, we present Tricolore, a versatile multi-vector learning framework that uncovers connections between different behavior types for more robust candidate generation. Tricolore's adaptive multi-task structure is also customizable to specific platform needs. To manage the variability in sparsity across behavior types, we incorporate a behavior-wise multi-view fusion module that dynamically enhances learning. Moreover, a popularity-balanced strategy ensures the recommendation list balances accuracy with item popularity, fostering diversity and improving overall performance. Extensive experiments on public datasets demonstrate Tricolore's effectiveness across various recommendation scenarios, from short video platforms to e-commerce. By leveraging a shared base embedding strategy, Tricolore also significantly improves the performance for cold-start users. The source code is publicly available at: https://github.com/abnering/Tricolore.
UD-English-CHILDES: A Collected Resource of Gold and Silver Universal Dependencies Trees for Child Language Interactions
Authors: Xiulin Yang, Zhuoxuan Ju, Lanni Bu, Zoey Liu, Nathan Schneider
Date: 2025-04-28 00:00:00

CHILDES is a widely used resource of transcribed child and child-directed speech. This paper introduces UD-English-CHILDES, the first officially released Universal Dependencies (UD) treebank derived from previously dependency-annotated CHILDES data with consistent and unified annotation guidelines. Our corpus harmonizes annotations from 11 children and their caregivers, totaling over 48k sentences. We validate existing gold-standard annotations under the UD v2 framework and provide an additional 1M silver-standard sentences, offering a consistent resource for computational and linguistic research.
Eterna is Solved
Authors: Tristan Cazenave
Date: 2025-05-04 00:00:00

RNA design consists of discovering a nucleotide sequence that folds into a target secondary structure. It is useful for synthetic biology, medicine, and nanotechnology. We propose Montparnasse, a Multi Objective Generalized Nested Rollout Policy Adaptation with Limited Repetition (MOGNRPALR) RNA design algorithm. It solves the Eterna benchmark.
Decoding Open-Ended Information Seeking Goals from Eye Movements in Reading
Authors: Cfir Avraham Hadar, Omer Shubi, Yoav Meiri, Yevgeni Berzak
Date: 2025-05-04 00:00:00
Tasks: Experimental Design

When reading, we often have specific information that interests us in a text. For example, you might be reading this paper because you are curious about LLMs for eye movements in reading, the experimental design, or perhaps you only care about the question ``but does it work?''. More broadly, in daily life, people approach texts with any number of text-specific goals that guide their reading behavior. In this work, we ask, for the first time, whether open-ended reading goals can be automatically decoded from eye movements in reading. To address this question, we introduce goal classification and goal reconstruction tasks and evaluation frameworks, and use large-scale eye tracking for reading data in English with hundreds of text-specific information seeking tasks. We develop and compare several discriminative and generative multimodal LLMs that combine eye movements and text for goal classification and goal reconstruction. Our experiments show considerable success on both tasks, suggesting that LLMs can extract valuable information about the readers' text-specific goals from eye movements.
MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents
Authors: Zeyu Zhang, Quanyu Dai, Xu Chen, Rui Li, Zhongyang Li, Zhenhua Dong
Date: 2025-05-04 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Recently, large language model based (LLM-based) agents have been widely applied across various fields. As a critical part, their memory capabilities have captured significant interest from both industrial and academic communities. Despite the proposal of many advanced memory models in recent research, however, there remains a lack of unified implementations under a general framework. To address this issue, we develop a unified and modular library for developing advanced memory models of LLM-based agents, called MemEngine. Based on our framework, we implement abundant memory models from recent research works. Additionally, our library facilitates convenient and extensible memory development, and offers user-friendly and pluggable memory usage. For benefiting our community, we have made our project publicly available at https://github.com/nuster1128/MemEngine.
Retrieval-augmented in-context learning for multimodal large language models in disease classification
Authors: Zaifu Zhan, Shuang Zhou, Xiaoshan Zhou, Yongkang Xiao, Jun Wang, Jiawen Deng, He Zhu, Yu Hou, Rui Zhang
Date: 2025-05-04 00:00:00
Tasks: In-Context Learning, RAG, Retrieval, Retrieval-augmented Generation

Objectives: We aim to dynamically retrieve informative demonstrations, enhancing in-context learning in multimodal large language models (MLLMs) for disease classification. Methods: We propose a Retrieval-Augmented In-Context Learning (RAICL) framework, which integrates retrieval-augmented generation (RAG) and in-context learning (ICL) to adaptively select demonstrations with similar disease patterns, enabling more effective ICL in MLLMs. Specifically, RAICL examines embeddings from diverse encoders, including ResNet, BERT, BioBERT, and ClinicalBERT, to retrieve appropriate demonstrations, and constructs conversational prompts optimized for ICL. We evaluated the framework on two real-world multi-modal datasets (TCGA and IU Chest X-ray), assessing its performance across multiple MLLMs (Qwen, Llava, Gemma), embedding strategies, similarity metrics, and varying numbers of demonstrations. Results: RAICL consistently improved classification performance. Accuracy increased from 0.7854 to 0.8368 on TCGA and from 0.7924 to 0.8658 on IU Chest X-ray. Multi-modal inputs outperformed single-modal ones, with text-only inputs being stronger than images alone. The richness of information embedded in each modality will determine which embedding model can be used to get better results. Few-shot experiments showed that increasing the number of retrieved examples further enhanced performance. Across different similarity metrics, Euclidean distance achieved the highest accuracy while cosine similarity yielded better macro-F1 scores. RAICL demonstrated consistent improvements across various MLLMs, confirming its robustness and versatility. Conclusions: RAICL provides an efficient and scalable approach to enhance in-context learning in MLLMs for multimodal disease classification.
LecEval: An Automated Metric for Multimodal Knowledge Acquisition in Multimedia Learning
Authors: Joy Lim Jia Yin, Daniel Zhang-li, Jifan Yu, Haoxuan Li, Shangqing Tu, Yuanchun Wang, Zhiyuan Liu, Huiqin Liu, Lei Hou, Juanzi Li, Bin Xu
Date: 2025-05-04 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Evaluating the quality of slide-based multimedia instruction is challenging. Existing methods like manual assessment, reference-based metrics, and large language model evaluators face limitations in scalability, context capture, or bias. In this paper, we introduce LecEval, an automated metric grounded in Mayer's Cognitive Theory of Multimedia Learning, to evaluate multimodal knowledge acquisition in slide-based learning. LecEval assesses effectiveness using four rubrics: Content Relevance (CR), Expressive Clarity (EC), Logical Structure (LS), and Audience Engagement (AE). We curate a large-scale dataset of over 2,000 slides from more than 50 online course videos, annotated with fine-grained human ratings across these rubrics. A model trained on this dataset demonstrates superior accuracy and adaptability compared to existing metrics, bridging the gap between automated and human assessments. We release our dataset and toolkits at https://github.com/JoylimJY/LecEval.
On the Price of Differential Privacy for Hierarchical Clustering
Authors: Chengyuan Deng, Jie Gao, Jalaj Upadhyay, Chen Wang, Samson Zhou
Date: 2025-04-22 00:00:00
Tasks: Clustering, Privacy Preserving

Hierarchical clustering is a fundamental unsupervised machine learning task with the aim of organizing data into a hierarchy of clusters. Many applications of hierarchical clustering involve sensitive user information, therefore motivating recent studies on differentially private hierarchical clustering under the rigorous framework of Dasgupta's objective. However, it has been shown that any privacy-preserving algorithm under edge-level differential privacy necessarily suffers a large error. To capture practical applications of this problem, we focus on the weight privacy model, where each edge of the input graph is at least unit weight. We present a novel algorithm in the weight privacy model that shows significantly better approximation than known impossibility results in the edge-level DP setting. In particular, our algorithm achieves $O(\log^{1.5}n/\varepsilon)$ multiplicative error for $\varepsilon$-DP and runs in polynomial time, where $n$ is the size of the input graph, and the cost is never worse than the optimal additive error in existing work. We complement our algorithm by showing if the unit-weight constraint does not apply, the lower bound for weight-level DP hierarchical clustering is essentially the same as the edge-level DP, i.e. $\Omega(n^2/\varepsilon)$ additive error. As a result, we also obtain a new lower bound of $\tilde{\Omega}(1/\varepsilon)$ additive error for balanced sparsest cuts in the weight-level DP model, which may be of independent interest. Finally, we evaluate our algorithm on synthetic and real-world datasets. Our experimental results show that our algorithm performs well in terms of extra cost and has good scalability to large graphs.
Large Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes
Authors: Guanchen Wu, Linzhi Zheng, Han Xie, Zhen Xiang, Jiaying Lu, Darren Liu, Delgersuren Bold, Bo Li, Xiao Hu, Carl Yang
Date: 2025-04-22 00:00:00
Tasks: De-identification, Language Modeling, Language Modelling, Large Language Model

The de-identification of private information in medical data is a crucial process to mitigate the risk of confidentiality breaches, particularly when patient personal details are not adequately removed before the release of medical records. Although rule-based and learning-based methods have been proposed, they often struggle with limited generalizability and require substantial amounts of annotated data for effective performance. Recent advancements in large language models (LLMs) have shown significant promise in addressing these issues due to their superior language comprehension capabilities. However, LLMs present challenges, including potential privacy risks when using commercial LLM APIs and high computational costs for deploying open-source LLMs locally. In this work, we introduce LPPA, an LLM-empowered Privacy-Protected PHI Annotation framework for clinical notes, targeting the English language. By fine-tuning LLMs locally with synthetic notes, LPPA ensures strong privacy protection and high PHI annotation accuracy. Extensive experiments demonstrate LPPA's effectiveness in accurately de-identifying private information, offering a scalable and efficient solution for enhancing patient privacy protection.
Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants
Authors: Milapji Singh Gill, Javal Vyas, Artan Markaj, Felix Gehlhoff, Mehmet Mercangöz
Date: 2025-05-04 00:00:00
Tasks: Large Language Model

Advances in Automation and Artificial Intelligence continue to enhance the autonomy of process plants in handling various operational scenarios. However, certain tasks, such as fault handling, remain challenging, as they rely heavily on human expertise. This highlights the need for systematic, knowledge-based methods. To address this gap, we propose a methodological framework that integrates Large Language Model (LLM) agents with a Digital Twin environment. The LLM agents continuously interpret system states and initiate control actions, including responses to unexpected faults, with the goal of returning the system to normal operation. In this context, the Digital Twin acts both as a structured repository of plant-specific engineering knowledge for agent prompting and as a simulation platform for the systematic validation and verification of the generated corrective control actions. The evaluation using a mixing module of a process plant demonstrates that the proposed framework is capable not only of autonomously controlling the mixing module, but also of generating effective corrective actions to mitigate a pipe clogging with only a few reprompts.
Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation
Authors: Volodymyr Havrylov, Haiwen Huang, Dan Zhang, Andreas Geiger
Date: 2025-05-04 00:00:00
Tasks: Benchmarking, Feature Upsampling, Interactive Segmentation, Scene Understanding

Vision Foundation Models (VFMs) are large-scale, pre-trained models that serve as general-purpose backbones for various computer vision tasks. As VFMs' popularity grows, there is an increasing interest in understanding their effectiveness for dense prediction tasks. However, VFMs typically produce low-resolution features, limiting their direct applicability in this context. One way to tackle this limitation is by employing a task-agnostic feature upsampling module that refines VFM features resolution. To assess the effectiveness of this approach, we investigate Interactive Segmentation (IS) as a novel benchmark for evaluating feature upsampling methods on VFMs. Due to its inherent multimodal input, consisting of an image and a set of user-defined clicks, as well as its dense mask output, IS creates a challenging environment that demands comprehensive visual scene understanding. Our benchmarking experiments show that selecting appropriate upsampling strategies significantly improves VFM features quality. The code is released at https://github.com/havrylovv/iSegProbe
Guillotine: Hypervisors for Isolating Malicious AIs
Authors: James Mickens, Sarah Radway, Ravi Netravali
Date: 2025-04-22 00:00:00

As AI models become more embedded in critical sectors like finance, healthcare, and the military, their inscrutable behavior poses ever-greater risks to society. To mitigate this risk, we propose Guillotine, a hypervisor architecture for sandboxing powerful AI models -- models that, by accident or malice, can generate existential threats to humanity. Although Guillotine borrows some well-known virtualization techniques, Guillotine must also introduce fundamentally new isolation mechanisms to handle the unique threat model posed by existential-risk AIs. For example, a rogue AI may try to introspect upon hypervisor software or the underlying hardware substrate to enable later subversion of that control plane; thus, a Guillotine hypervisor requires careful co-design of the hypervisor software and the CPUs, RAM, NIC, and storage devices that support the hypervisor software, to thwart side channel leakage and more generally eliminate mechanisms for AI to exploit reflection-based vulnerabilities. Beyond such isolation at the software, network, and microarchitectural layers, a Guillotine hypervisor must also provide physical fail-safes more commonly associated with nuclear power plants, avionic platforms, and other types of mission critical systems. Physical fail-safes, e.g., involving electromechanical disconnection of network cables, or the flooding of a datacenter which holds a rogue AI, provide defense in depth if software, network, and microarchitectural isolation is compromised and a rogue AI must be temporarily shut down or permanently destroyed.