pwc-archive/papers-with-abstracts huggingface dataset:
MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation
Authors: Beibei Wang, Boyue Cui, Shiqu Chen, Xuan Wang, Yadong Wang, Junyi Li
Date: 2025-03-29 00:00:00
Tasks: Graph Representation Learning, Prediction, Protein Function Prediction, Representation Learning

Motivation: In recent years, protein function prediction has broken through the bottleneck of sequence features, significantly improving prediction accuracy using high-precision protein structures predicted by AlphaFold2. While single-species protein function prediction methods have achieved remarkable success, multi-species protein function prediction methods are still in the stage of using PPI networks and sequence features. Providing effective cross-species label propagation for species with sparse protein annotations remains a challenging issue. To address this problem, we propose the MSNGO model, which integrates structural features and network propagation methods. Our validation shows that using structural features can significantly improve the accuracy of multi-species protein function prediction. Results: We employ graph representation learning techniques to extract amino acid representations from protein structure contact maps and train a structural model using a graph convolution pooling module to derive protein-level structural features. After incorporating the sequence features from ESM-2, we apply a network propagation algorithm to aggregate information and update node representations within a heterogeneous network. The results demonstrate that MSNGO outperforms previous multi-species protein function prediction methods that rely on sequence features and PPI networks. Availability: https://github.com/blingbell/MSNGO.
On Geometrical Properties of Text Token Embeddings for Strong Semantic Binding in Text-to-Image Generation
Authors: Hoigi Seo, Junseo Bang, Haechang Lee, Joohoon Lee, Byung Hyun Lee, Se Young Chun
Date: 2025-03-29 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

Text-to-Image (T2I) models often suffer from text-image misalignment in complex scenes involving multiple objects and attributes. Semantic binding aims to mitigate this issue by accurately associating the generated attributes and objects with their corresponding noun phrases (NPs). Existing methods rely on text or latent optimizations, yet the factors influencing semantic binding remain underexplored. Here we investigate the geometrical properties of text token embeddings and their cross-attention (CA) maps. We empirically and theoretically analyze that the geometrical properties of token embeddings, specifically both angular distances and norms, play a crucial role in CA map differentiation. Then, we propose \textbf{TeeMo}, a training-free text embedding-aware T2I framework with strong semantic binding. TeeMo consists of Causality-Aware Projection-Out (CAPO) for distinct inter-NP CA maps and Adaptive Token Mixing (ATM) with our loss to enhance inter-NP separation while maintaining intra-NP cohesion in CA maps. Extensive experiments confirm TeeMo consistently outperforms prior arts across diverse baselines and datasets.
Learning Structure-enhanced Temporal Point Processes with Gromov-Wasserstein Regularization
Authors: Qingmei Wang, Fanmeng Wang, Bing Su, Hongteng Xu
Date: 2025-03-29 00:00:00
Tasks: Clustering, Point Processes

Real-world event sequences are often generated by different temporal point processes (TPPs) and thus have clustering structures. Nonetheless, in the modeling and prediction of event sequences, most existing TPPs ignore the inherent clustering structures of the event sequences, leading to the models with unsatisfactory interpretability. In this study, we learn structure-enhanced TPPs with the help of Gromov-Wasserstein (GW) regularization, which imposes clustering structures on the sequence-level embeddings of the TPPs in the maximum likelihood estimation framework.In the training phase, the proposed method leverages a nonparametric TPP kernel to regularize the similarity matrix derived based on the sequence embeddings. In large-scale applications, we sample the kernel matrix and implement the regularization as a Gromov-Wasserstein (GW) discrepancy term, which achieves a trade-off between regularity and computational efficiency.The TPPs learned through this method result in clustered sequence embeddings and demonstrate competitive predictive and clustering performance, significantly improving the model interpretability without compromising prediction accuracy.
AuditVotes: A Framework Towards More Deployable Certified Robustness for Graph Neural Networks
Authors: Yuni Lai, Yulin Zhu, Yixuan Sun, Yulun Wu, Bin Xiao, Gaolei Li, Jianhua Li, Kai Zhou
Date: 2025-03-29 00:00:00
Tasks: Computational Efficiency

Despite advancements in Graph Neural Networks (GNNs), adaptive attacks continue to challenge their robustness. Certified robustness based on randomized smoothing has emerged as a promising solution, offering provable guarantees that a model's predictions remain stable under adversarial perturbations within a specified range. However, existing methods face a critical trade-off between accuracy and robustness, as achieving stronger robustness requires introducing greater noise into the input graph. This excessive randomization degrades data quality and disrupts prediction consistency, limiting the practical deployment of certifiably robust GNNs in real-world scenarios where both accuracy and robustness are essential. To address this challenge, we propose \textbf{AuditVotes}, the first framework to achieve both high clean accuracy and certifiably robust accuracy for GNNs. It integrates randomized smoothing with two key components, \underline{au}gmentation and con\underline{dit}ional smoothing, aiming to improve data quality and prediction consistency. The augmentation, acting as a pre-processing step, de-noises the randomized graph, significantly improving data quality and clean accuracy. The conditional smoothing, serving as a post-processing step, employs a filtering function to selectively count votes, thereby filtering low-quality predictions and improving voting consistency. Extensive experimental results demonstrate that AuditVotes significantly enhances clean accuracy, certified robustness, and empirical robustness while maintaining high computational efficiency. Notably, compared to baseline randomized smoothing, AuditVotes improves clean accuracy by $437.1\%$ and certified accuracy by $409.3\%$ when the attacker can arbitrarily insert $20$ edges on the Cora-ML datasets, representing a substantial step toward deploying certifiably robust GNNs in real-world applications.
FindTheFlaws: Annotated Errors for Detecting Flawed Reasoning and Scalable Oversight Research
Authors: Gabriel Recchia, Chatrik Singh Mangat, Issac Li, Gayatri Krishnakumar
Date: 2025-03-29 00:00:00
Tasks: Form

As AI models tackle increasingly complex problems, ensuring reliable human oversight becomes more challenging due to the difficulty of verifying solutions. Approaches to scaling AI supervision include debate, in which two agents engage in structured dialogue to help a judge evaluate claims; critique, in which models identify potential flaws in proposed solutions; and prover-verifier games, in which a capable 'prover' model generates solutions that must be verifiable by a less capable 'verifier'. Evaluations of the scalability of these and similar approaches to difficult problems benefit from datasets that include (1) long-form expert-verified correct solutions and (2) long-form flawed solutions with annotations highlighting specific errors, but few are available. To address this gap, we present FindTheFlaws, a group of five diverse datasets spanning medicine, mathematics, science, coding, and the Lojban language. Each dataset contains questions and long-form solutions with expert annotations validating their correctness or identifying specific error(s) in the reasoning. We evaluate frontier models' critiquing capabilities and observe a range of performance that can be leveraged for scalable oversight experiments: models performing more poorly on particular datasets can serve as judges/verifiers for more capable models. Additionally, for some task/dataset combinations, expert baselines exceed even top model performance, making them more beneficial for scalable oversight experiments.
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation
Authors: Chengkun Wei, Weixian Li, Chen Gong, Wenzhi Chen
Date: 2025-03-29 00:00:00
Tasks: Deep Learning, image-classification, Image Classification, Privacy Preserving, Privacy Preserving Deep Learning

Differentially Private Stochastic Gradient Descent (DP-SGD) is a widely adopted technique for privacy-preserving deep learning. A critical challenge in DP-SGD is selecting the optimal clipping threshold C, which involves balancing the trade-off between clipping bias and noise magnitude, incurring substantial privacy and computing overhead during hyperparameter tuning. In this paper, we propose Dynamic Clipping DP-SGD (DC-SGD), a framework that leverages differentially private histograms to estimate gradient norm distributions and dynamically adjust the clipping threshold C. Our framework includes two novel mechanisms: DC-SGD-P and DC-SGD-E. DC-SGD-P adjusts the clipping threshold based on a percentile of gradient norms, while DC-SGD-E minimizes the expected squared error of gradients to optimize C. These dynamic adjustments significantly reduce the burden of hyperparameter tuning C. The extensive experiments on various deep learning tasks, including image classification and natural language processing, show that our proposed dynamic algorithms achieve up to 9 times acceleration on hyperparameter tuning than DP-SGD. And DC-SGD-E can achieve an accuracy improvement of 10.62% on CIFAR10 than DP-SGD under the same privacy budget of hyperparameter tuning. We conduct rigorous theoretical privacy and convergence analyses, showing that our methods seamlessly integrate with the Adam optimizer. Our results highlight the robust performance and efficiency of DC-SGD, offering a practical solution for differentially private deep learning with reduced computational overhead and enhanced privacy guarantees.
PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inference
Authors: Guanqiao Qu, Qian Chen, Xianhao Chen, Kaibin Huang, Yuguang Fang
Date: 2025-03-29 00:00:00
Tasks: GPU, Scheduling

By provisioning inference offloading services, edge inference drives the rapid growth of AI applications at the network edge. However, achieving high task throughput with stringent latency requirements remains a significant challenge. To address this issue, we develop a parameter-sharing AI model loading (PartialLoading) framework for multi-user edge inference, which exploits two key insights: 1) the majority of latency arises from loading AI models into server GPU memory, and 2) different AI models can share a significant number of parameters, for which redundant loading should be avoided. Towards this end, we formulate a joint multi-user scheduling and spectrum bandwidth allocation problem to maximize task throughput by exploiting shared parameter blocks across models. The intuition is to judiciously schedule user requests to reuse the shared parameter blocks between consecutively loaded models, thereby reducing model loading time substantially. To facilitate solution finding, we decouple the problem into two sub-problems, i.e., user scheduling and bandwidth allocation, showing that solving them sequentially is equivalent to solving the original problem. Due to the NP-hardness of the problem, we first study an important special case called the "bottom-layer-sharing" case, where AI models share some bottom layers within clusters, and design a dynamic programming-based algorithm to obtain the optimal solution in polynomial time. For the general case, where shared parameter blocks appear at arbitrary positions within AI models, we propose a greedy heuristic to obtain the sub-optimal solution efficiently. Simulation results demonstrate that the proposed framework significantly improves task throughput under deadline constraints compared with user scheduling without exploiting parameter sharing.
XL-Instruct: Synthetic Data for Cross-Lingual Open-Ended Generation
Authors: Vivek Iyer, Ricardo Rei, Pinzhen Chen, Alexandra Birch
Date: 2025-03-29 00:00:00
Tasks: 8k, Synthetic Data Generation

Cross-lingual open-ended generation -- i.e. generating responses in a desired language different from that of the user's query -- is an important yet understudied problem. We introduce XL-AlpacaEval, a new benchmark for evaluating cross-lingual generation capabilities in Large Language Models (LLMs), and propose XL-Instruct, a high-quality synthetic data generation method. Fine-tuning with just 8K XL-Instruct-generated instructions significantly improves model performance, increasing the win rate against GPT-4o-Mini from 7.4% to 21.5%, and improving on several fine-grained quality metrics. Additionally, models fine-tuned on XL-Instruct exhibit strong zero-shot transfer to both English-only and multilingual generation tasks. Given its consistent gains across the board, we strongly recommend incorporating XL-Instruct in the post-training pipeline of future multilingual LLMs. To facilitate further research, we will publicly and freely release the XL-Instruct and XL-AlpacaEval datasets, which constitute two of the few cross-lingual resources currently available in the literature.
Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation
Authors: Kanishka Ranaweera, Azadeh Ghari Neiat, Xiao Liu, Bipasha Kashyap, Pubudu N. Pathirana
Date: 2025-03-29 00:00:00
Tasks: Fairness, Federated Learning

Federated learning (FL) has emerged as a promising paradigm in machine learning, enabling collaborative model training across decentralized devices without the need for raw data sharing. In FL, a global model is trained iteratively on local datasets residing on individual devices, each contributing to the model's improvement. However, the heterogeneous nature of these local datasets, stemming from diverse user behaviours, device capabilities, and data distributions, poses a significant challenge. The inherent heterogeneity in federated learning gives rise to various issues, including model performance discrepancies, convergence challenges, and potential privacy concerns. As the global model progresses through rounds of training, the disparities in local data quality and quantity can impede the overall effectiveness of federated learning systems. Moreover, maintaining fairness and privacy across diverse user groups becomes a paramount concern. To address this issue, this paper introduces a novel FL framework, ClusterGuardFL, that employs dissimilarity scores, k-means clustering, and reconciliation confidence scores to dynamically assign weights to client updates. The dissimilarity scores between global and local models guide the formation of clusters, with cluster size influencing the weight allocation. Within each cluster, a reconciliation confidence score is calculated for individual data points, and a softmax layer generates customized weights for clients. These weights are utilized in the aggregation process, enhancing the model's robustness and privacy. Experimental results demonstrate the efficacy of the proposed approach in achieving improved model performance in diverse datasets.
HRET: A Self-Evolving LLM Evaluation Toolkit for Korean
Authors: Hanwool Lee, Soo Yong Kim, Dasol Choi, Sangwon Baek, Seunghyeok Hong, Ilgyun Jeong, Inseon Hwang, Naeun Lee, Guijin Son
Date: 2025-03-29 00:00:00
Tasks: haerae, kmmlu

Recent advancements in Korean large language models (LLMs) have spurred numerous benchmarks and evaluation methodologies, yet the lack of a standardized evaluation framework has led to inconsistent results and limited comparability. To address this, we introduce HRET Haerae Evaluation Toolkit, an open-source, self-evolving evaluation framework tailored specifically for Korean LLMs. HRET unifies diverse evaluation methods, including logit-based scoring, exact-match, language-inconsistency penalization, and LLM-as-a-Judge assessments. Its modular, registry-based architecture integrates major benchmarks (HAE-RAE Bench, KMMLU, KUDGE, HRM8K) and multiple inference backends (vLLM, HuggingFace, OpenAI-compatible endpoints). With automated pipelines for continuous evolution, HRET provides a robust foundation for reproducible, fair, and transparent Korean NLP research.
Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning
Authors: Supriyo Maji, Linran Zhao, Souradip Poddar, David Z. Pan
Date: 2025-03-29 00:00:00
Tasks: Multi-agent Reinforcement Learning, Q-Learning

Layout-dependent effects (LDEs) significantly impact analog circuit performance. Traditionally, designers have relied on symmetric placement of circuit components to mitigate variations caused by LDEs. However, due to non-linear nature of these effects, conventional methods often fall short. We propose an objective-driven, multi-level, multi-agent Q-learning framework to explore unconventional design space of analog layout, opening new avenues for optimizing analog circuit performance. Our approach achieves better variation performance than the state-of-the-art layout techniques. Notably, this is the first application of multi-agent RL in analog layout automation. The proposed approach is compared with non-ML approach based on simulated annealing.
Can LLMs Support Medical Knowledge Imputation? An Evaluation-Based Perspective
Authors: Xinyu Yao, Aditya Sannabhadti, Holly Wiberg, Karmel S. Shehadeh, Rema Padman
Date: 2025-03-29 00:00:00
Tasks: Imputation, Knowledge Graphs

Medical knowledge graphs (KGs) are essential for clinical decision support and biomedical research, yet they often exhibit incompleteness due to knowledge gaps and structural limitations in medical coding systems. This issue is particularly evident in treatment mapping, where coding systems such as ICD, Mondo, and ATC lack comprehensive coverage, resulting in missing or inconsistent associations between diseases and their potential treatments. To address this issue, we have explored the use of Large Language Models (LLMs) for imputing missing treatment relationships. Although LLMs offer promising capabilities in knowledge augmentation, their application in medical knowledge imputation presents significant risks, including factual inaccuracies, hallucinated associations, and instability between and within LLMs. In this study, we systematically evaluate LLM-driven treatment mapping, assessing its reliability through benchmark comparisons. Our findings highlight critical limitations, including inconsistencies with established clinical guidelines and potential risks to patient safety. This study serves as a cautionary guide for researchers and practitioners, underscoring the importance of critical evaluation and hybrid approaches when leveraging LLMs to enhance treatment mappings on medical knowledge graphs.
SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning
Authors: Tianyang Xu, Xiaoze Liu, Feijie Wu, Xiaoqian Wang, Jing Gao
Date: 2025-03-29 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Memorization

Large Language Models (LLMs) have transformed natural language processing by learning from massive datasets, yet this rapid progress has also drawn legal scrutiny, as the ability to unintentionally generate copyrighted content has already prompted several prominent lawsuits. In this work, we introduce SUV (Selective Unlearning for Verbatim data), a selective unlearning framework designed to prevent LLM from memorizing copyrighted content while preserving its overall utility. In detail, the proposed method constructs a dataset that captures instances of copyrighted infringement cases by the targeted LLM. With the dataset, we unlearn the content from the LLM by means of Direct Preference Optimization (DPO), which replaces the verbatim copyrighted content with plausible and coherent alternatives. Since DPO may hinder the LLM's performance in other unrelated tasks, we integrate gradient projection and Fisher information regularization to mitigate the degradation. We validate our approach using a large-scale dataset of 500 famous books (predominantly copyrighted works) and demonstrate that SUV significantly reduces verbatim memorization with negligible impact on the performance on unrelated tasks. Extensive experiments on both our dataset and public benchmarks confirm the scalability and efficacy of our approach, offering a promising solution for mitigating copyright risks in real-world LLM applications.
DATAWEAVER: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text
Authors: Yu Fu, Dennis Bromley, Vidya Setlur
Date: 2025-03-29 00:00:00

Data-driven storytelling has gained prominence in journalism and other data reporting fields. However, the process of creating these stories remains challenging, often requiring the integration of effective visualizations with compelling narratives to form a cohesive, interactive presentation. To help streamline this process, we present an integrated authoring framework and system, DataWeaver, that supports both visualization-to-text and text-to-visualization composition. DataWeaver enables users to create data narratives anchored to data facts derived from "call-out" interactions, i.e., user-initiated highlights of visualization elements that prompt relevant narrative content. In addition to this "vis-to-text" composition, DataWeaver also supports a "text-initiated" approach, generating relevant interactive visualizations from existing narratives. Key findings from an evaluation with 13 participants highlighted the utility and usability of DataWeaver and the effectiveness of its integrated authoring framework. The evaluation also revealed opportunities to enhance the framework by refining filtering mechanisms and visualization recommendations and better support authoring creativity by introducing advanced customization options.
Adaptive Interactive Navigation of Quadruped Robots using Large Language Models
Authors: Kangjie Zhou, Yao Mu, Haoyang Song, Yi Zeng, Pengying Wu, Han Gao, Chang Liu
Date: 2025-03-29 00:00:00
Tasks: Motion Planning, Task Planning

Robotic navigation in complex environments remains a critical research challenge. Traditional navigation methods focus on optimal trajectory generation within free space, struggling in environments lacking viable paths to the goal, such as disaster zones or cluttered warehouses. To address this gap, we propose an adaptive interactive navigation approach that proactively interacts with environments to create feasible paths to reach originally unavailable goals. Specifically, we present a primitive tree for task planning with large language models (LLMs), facilitating effective reasoning to determine interaction objects and sequences. To ensure robust subtask execution, we adopt reinforcement learning to pre-train a comprehensive skill library containing versatile locomotion and interaction behaviors for motion planning. Furthermore, we introduce an adaptive replanning method featuring two LLM-based modules: an advisor serving as a flexible replanning trigger and an arborist for autonomous plan adjustment. Integrated with the tree structure, the replanning mechanism allows for convenient node addition and pruning, enabling rapid plan modification in unknown environments. Comprehensive simulations and experiments have demonstrated our method's effectiveness and adaptivity in diverse scenarios. The supplementary video is available at page: https://youtu.be/W5ttPnSap2g.