pwc-archive/papers-with-abstracts huggingface dataset:
CVC: A Large-Scale Chinese Value Rule Corpus for Value Alignment of Large Language Models
Authors: Ping Wu, Guobin Shen, Dongcheng Zhao, Yuwei Wang, Yiting Dong, Yu Shi, Enmeng Lu, Feifei Zhao, Yi Zeng
Date: 2025-06-02 00:00:00
Tasks: Benchmarking

Ensuring that Large Language Models (LLMs) align with mainstream human values and ethical norms is crucial for the safe and sustainable development of AI. Current value evaluation and alignment are constrained by Western cultural bias and incomplete domestic frameworks reliant on non-native rules; furthermore, the lack of scalable, rule-driven scenario generation methods makes evaluations costly and inadequate across diverse cultural contexts. To address these challenges, we propose a hierarchical value framework grounded in core Chinese values, encompassing three main dimensions, 12 core values, and 50 derived values. Based on this framework, we construct a large-scale Chinese Values Corpus (CVC) containing over 250,000 value rules enhanced and expanded through human annotation. Experimental results show that CVC-guided scenarios outperform direct generation ones in value boundaries and content diversity. In the evaluation across six sensitive themes (e.g., surrogacy, suicide), seven mainstream LLMs preferred CVC-generated options in over 70.5% of cases, while five Chinese human annotators showed an 87.5% alignment with CVC, confirming its universality, cultural relevance, and strong alignment with Chinese values. Additionally, we construct 400,000 rule-based moral dilemma scenarios that objectively capture nuanced distinctions in conflicting value prioritization across 17 LLMs. Our work establishes a culturally-adaptive benchmarking framework for comprehensive value evaluation and alignment, representing Chinese characteristics. All data are available at https://huggingface.co/datasets/Beijing-AISI/CVC, and the code is available at https://github.com/Beijing-AISI/CVC.
E-ABIN: an Explainable module for Anomaly detection in BIological Networks
Authors: Ugo Lomoio, Tommaso Mazza, Pierangelo Veltri, Pietro Hiram Guzzi
Date: 2025-06-25 00:00:00
Tasks: Anomaly Detection

The increasing availability of large-scale omics data calls for robust analytical frameworks capable of handling complex gene expression datasets while offering interpretable results. Recent advances in artificial intelligence have enabled the identification of aberrant molecular patterns distinguishing disease states from healthy controls. Coupled with improvements in model interpretability, these tools now support the identification of genes potentially driving disease phenotypes. However, current approaches to gene anomaly detection often remain limited to single datasets and lack accessible graphical interfaces. Here, we introduce E-ABIN, a general-purpose, explainable framework for Anomaly detection in Biological Networks. E-ABIN combines classical machine learning and graph-based deep learning techniques within a unified, user-friendly platform, enabling the detection and interpretation of anomalies from gene expression or methylation-derived networks. By integrating algorithms such as Support Vector Machines, Random Forests, Graph Autoencoders (GAEs), and Graph Adversarial Attributed Networks (GAANs), E-ABIN ensures a high predictive accuracy while maintaining interpretability. We demonstrate the utility of E-ABIN through case studies of bladder cancer and coeliac disease, where it effectively uncovers biologically relevant anomalies and offers insights into disease mechanisms.
On Context-Content Uncertainty Principle
Authors: Xin Li
Date: 2025-06-25 00:00:00
Tasks: Specificity

The Context-Content Uncertainty Principle (CCUP) proposes that inference under uncertainty is governed by an entropy asymmetry between context and content: high-entropy contexts must be interpreted through alignment with low-entropy, structured content. In this paper, we develop a layered computational framework that derives operational principles from this foundational asymmetry. At the base level, CCUP formalizes inference as directional entropy minimization, establishing a variational gradient that favors content-first structuring. Building upon this, we identify four hierarchical layers of operational principles: (\textbf{L1}) \emph{Core Inference Constraints}, including structure-before-specificity, asymmetric inference flow, cycle-consistent bootstrapping, and conditional compression, all shown to be mutually reducible; (\textbf{L2}) \emph{Resource Allocation Principles}, such as precision-weighted attention, asymmetric learning rates, and attractor-based memory encoding; (\textbf{L3}) \emph{Temporal Bootstrapping Dynamics}, which organize learning over time via structure-guided curricula; and (\textbf{L4}) \emph{Spatial Hierarchical Composition}, which integrates these mechanisms into self-organizing cycles of memory, inference, and planning. We present formal equivalence theorems, a dependency lattice among principles, and computational simulations demonstrating the efficiency gains of CCUP-aligned inference. This work provides a unified theoretical foundation for understanding how brains and machines minimize uncertainty through recursive structure-specificity alignment. The brain is not just an inference machine. It is a cycle-consistent entropy gradient resolver, aligning structure and specificity via path-dependent, content-seeded simulation.
Multiple Streams of Relation Extraction: Enriching and Recalling in Transformers
Authors: Todd Nief, David Reber, Sean Richardson, Ari Holtzman
Date: 2025-06-25 00:00:00
Tasks: Relation, Relation Extraction

When an LLM learns a relation during finetuning (e.g., new movie releases, corporate mergers, etc.), where does this information go? Is it extracted when the model processes an entity, recalled just-in-time before a prediction, or are there multiple separate heuristics? Existing localization approaches (e.g. activation patching) are ill-suited for this analysis because they tend to replace parts of the residual stream, potentially deleting information. To fill this gap, we propose dynamic weight-grafting between fine-tuned and pre-trained language models to show that fine-tuned language models both (1) extract relation information learned during finetuning while processing entities and (2) ``recall" this information in later layers while generating predictions. In some cases, models need both of these pathways to correctly generate finetuned information while, in other cases, a single ``enrichment" or ``recall" pathway alone is sufficient. We examine the necessity and sufficiency of these information pathways, examining what layers they occur at, how much redundancy they exhibit, and which model components are involved -- finding that the ``recall" pathway occurs via both task-specific attention mechanisms and a relation extraction step in the output of the attention and the feedforward networks at the final layers before next token prediction.
Divide, Specialize, and Route: A New Approach to Efficient Ensemble Learning
Authors: Jakub Piwko, Jędrzej Ruciński, Dawid Płudowski, Antoni Zajko, Patryzja Żak, Mateusz Zacharecki, Anna Kozak, Katarzyna Woźnica
Date: 2025-06-25 00:00:00
Tasks: Binary Classification, Computational Efficiency, Ensemble Learning

Ensemble learning has proven effective in boosting predictive performance, but traditional methods such as bagging, boosting, and dynamic ensemble selection (DES) suffer from high computational cost and limited adaptability to heterogeneous data distributions. To address these limitations, we propose Hellsemble, a novel and interpretable ensemble framework for binary classification that leverages dataset complexity during both training and inference. Hellsemble incrementally partitions the dataset into circles of difficulty by iteratively passing misclassified instances from simpler models to subsequent ones, forming a committee of specialised base learners. Each model is trained on increasingly challenging subsets, while a separate router model learns to assign new instances to the most suitable base model based on inferred difficulty. Hellsemble achieves strong classification accuracy while maintaining computational efficiency and interpretability. Experimental results on OpenML-CC18 and Tabzilla benchmarks demonstrate that Hellsemble often outperforms classical ensemble methods. Our findings suggest that embracing instance-level difficulty offers a promising direction for constructing efficient and robust ensemble systems.
Demystifying Distributed Training of Graph Neural Networks for Link Prediction
Authors: Xin Huang, Chul-Ho Lee
Date: 2025-06-25 00:00:00
Tasks: graph partitioning, Link Prediction, Node Classification, Prediction

Graph neural networks (GNNs) are powerful tools for solving graph-related problems. Distributed GNN frameworks and systems enhance the scalability of GNNs and accelerate model training, yet most are optimized for node classification. Their performance on link prediction remains underexplored. This paper demystifies distributed training of GNNs for link prediction by investigating the issue of performance degradation when each worker trains a GNN on its assigned partitioned subgraph without having access to the entire graph. We discover that the main sources of the issue come from not only the information loss caused by graph partitioning but also the ways of drawing negative samples during model training. While sharing the complete graph information with each worker resolves the issue and preserves link prediction accuracy, it incurs a high communication cost. We propose SpLPG, which effectively leverages graph sparsification to mitigate the issue of performance degradation at a reduced communication cost. Experiment results on several public real-world datasets demonstrate the effectiveness of SpLPG, which reduces the communication overhead by up to about 80% while mostly preserving link prediction accuracy.
Learning-Based Resource Management in Integrated Sensing and Communication Systems
Authors: Ziyang Lu, M. Cenk Gursoy, Chilukuri K. Mohan, Pramod K. Varshney
Date: 2025-06-25 00:00:00
Tasks: Deep Reinforcement Learning, Integrated sensing and communication, Management

In this paper, we tackle the task of adaptive time allocation in integrated sensing and communication systems equipped with radar and communication units. The dual-functional radar-communication system's task involves allocating dwell times for tracking multiple targets and utilizing the remaining time for data transmission towards estimated target locations. We introduce a novel constrained deep reinforcement learning (CDRL) approach, designed to optimize resource allocation between tracking and communication under time budget constraints, thereby enhancing target communication quality. Our numerical results demonstrate the efficiency of our proposed CDRL framework, confirming its ability to maximize communication quality in highly dynamic environments while adhering to time constraints.
On the Necessity of Output Distribution Reweighting for Effective Class Unlearning
Authors: Yian Wang, Ali Ebrahimpour-Boroojeny, Hari Sundaram
Date: 2025-06-25 00:00:00
Tasks: Machine Unlearning

In this work, we introduce an output-reweighting unlearning method, RWFT, a lightweight technique that erases an entire class from a trained classifier without full retraining. Forgetting specific classes from trained models is essential for enforcing user deletion rights and mitigating harmful or biased predictions. The full retraining is costly and existing unlearning methods fail to replicate the behavior of the retrained models when predicting samples from the unlearned class. We prove this failure by designing a variant of membership inference attacks, MIA-NN that successfully reveals the unlearned class for any of these methods. We propose a simple redistribution of the probability mass for the prediction on the samples in the forgotten class which is robust to MIA-NN. We also introduce a new metric based on the total variation (TV) distance of the prediction probabilities to quantify residual leakage to prevent future methods from susceptibility to the new attack. Through extensive experiments with state of the art baselines in machine unlearning, we show that our approach matches the results of full retraining in both metrics used for evaluation by prior work and the new metric we propose in this work. Compare to state-of-the-art methods, we gain 2.79% in previously used metrics and 111.45% in our new TV-based metric over the best existing method.
Graph-Structured Feedback Multimodel Ensemble Online Conformal Prediction
Authors: Erfan Hajihashemi, Yanning Shen
Date: 2025-06-26 00:00:00
Tasks: Conformal Prediction, Prediction

Online conformal prediction has demonstrated its capability to construct a prediction set for each incoming data point that covers the true label with a predetermined probability. To cope with potential distribution shift, multi-model online conformal prediction has been introduced to select and leverage different models from a preselected candidate set. Along with the improved flexibility, the choice of the preselected set also brings challenges. A candidate set that includes a large number of models may increase the computational complexity. In addition, the inclusion of irrelevant models with poor performance may negatively impact the performance and lead to unnecessarily large prediction sets. To address these challenges, we propose a novel multi-model online conformal prediction algorithm that identifies a subset of effective models at each time step by collecting feedback from a bipartite graph, which is refined upon receiving new data. A model is then selected from this subset to construct the prediction set, resulting in reduced computational complexity and smaller prediction sets. Additionally, we demonstrate that using prediction set size as feedback, alongside model loss, can significantly improve efficiency by constructing smaller prediction sets while still satisfying the required coverage guarantee. The proposed algorithms are proven to ensure valid coverage and achieve sublinear regret. Experiments on real and synthetic datasets validate that the proposed methods construct smaller prediction sets and outperform existing multi-model online conformal prediction approaches.
Explainable AI for Radar Resource Management: Modified LIME in Deep Reinforcement Learning
Authors: Ziyang Lu, M. Cenk Gursoy, Chilukuri K. Mohan, Pramod K. Varshney
Date: 2025-06-26 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning

Deep reinforcement learning has been extensively studied in decision-making processes and has demonstrated superior performance over conventional approaches in various fields, including radar resource management (RRM). However, a notable limitation of neural networks is their ``black box" nature and recent research work has increasingly focused on explainable AI (XAI) techniques to describe the rationale behind neural network decisions. One promising XAI method is local interpretable model-agnostic explanations (LIME). However, the sampling process in LIME ignores the correlations between features. In this paper, we propose a modified LIME approach that integrates deep learning (DL) into the sampling process, which we refer to as DL-LIME. We employ DL-LIME within deep reinforcement learning for radar resource management. Numerical results show that DL-LIME outperforms conventional LIME in terms of both fidelity and task performance, demonstrating superior performance with both metrics. DL-LIME also provides insights on which factors are more important in decision making for radar resource management.
Model State Arithmetic for Machine Unlearning
Authors: Keivan Rezaei, Mehrdad Saberi, Abhilasha Ravichander, Soheil Feizi
Date: 2025-06-26 00:00:00
Tasks: Machine Unlearning, model

Large language models are trained on massive corpora of web data, which may include private data, copyrighted material, factually inaccurate data, or data that degrades model performance. Eliminating the influence of such problematic datapoints through complete retraining -- by repeatedly pretraining the model on datasets that exclude these specific instances -- is computationally prohibitive. For this reason, unlearning algorithms have emerged that aim to eliminate the influence of particular datapoints, while otherwise preserving the model -- at a low computational cost. However, precisely estimating and undoing the influence of individual datapoints has proved to be challenging. In this work, we propose a new algorithm, MSA, for estimating and undoing the influence of datapoints -- by leveraging model checkpoints i.e. artifacts capturing model states at different stages of pretraining. Our experimental results demonstrate that MSA consistently outperforms existing machine unlearning algorithms across multiple benchmarks, models, and evaluation metrics, suggesting that MSA could be an effective approach towards more flexible large language models that are capable of data erasure.
Distilling Normalizing Flows
Authors: Steven Walton, Valeriy Klyukin, Maksim Artemev, Denis Derkach, Nikita Orlov, Humphrey Shi
Date: 2025-06-26 00:00:00
Tasks: Density Estimation, Knowledge Distillation, Transfer Learning

Explicit density learners are becoming an increasingly popular technique for generative models because of their ability to better model probability distributions. They have advantages over Generative Adversarial Networks due to their ability to perform density estimation and having exact latent-variable inference. This has many advantages, including: being able to simply interpolate, calculate sample likelihood, and analyze the probability distribution. The downside of these models is that they are often more difficult to train and have lower sampling quality. Normalizing flows are explicit density models, that use composable bijective functions to turn an intractable probability function into a tractable one. In this work, we present novel knowledge distillation techniques to increase sampling quality and density estimation of smaller student normalizing flows. We seek to study the capacity of knowledge distillation in Compositional Normalizing Flows to understand the benefits and weaknesses provided by these architectures. Normalizing flows have unique properties that allow for a non-traditional forms of knowledge transfer, where we can transfer that knowledge within intermediate layers. We find that through this distillation, we can make students significantly smaller while making substantial performance gains over a non-distilled student. With smaller models there is a proportionally increased throughput as this is dependent upon the number of bijectors, and thus parameters, in the network.
TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence
Authors: Feng Jiang, Mangal Prakash, Hehuan Ma, Jianyuan Deng, Yuzhi Guo, Amina Mollaysa, Tommaso Mansi, Rui Liao, Junzhou Huang
Date: 2025-06-26 00:00:00
Tasks: Molecular Property Prediction, molecular representation, Property Prediction, Representation Learning

Molecular property prediction aims to learn representations that map chemical structures to functional properties. While multimodal learning has emerged as a powerful paradigm to learn molecular representations, prior works have largely overlooked textual and taxonomic information of molecules for representation learning. We introduce TRIDENT, a novel framework that integrates molecular SMILES, textual descriptions, and taxonomic functional annotations to learn rich molecular representations. To achieve this, we curate a comprehensive dataset of molecule-text pairs with structured, multi-level functional annotations. Instead of relying on conventional contrastive loss, TRIDENT employs a volume-based alignment objective to jointly align tri-modal features at the global level, enabling soft, geometry-aware alignment across modalities. Additionally, TRIDENT introduces a novel local alignment objective that captures detailed relationships between molecular substructures and their corresponding sub-textual descriptions. A momentum-based mechanism dynamically balances global and local alignment, enabling the model to learn both broad functional semantics and fine-grained structure-function mappings. TRIDENT achieves state-of-the-art performance on 11 downstream tasks, demonstrating the value of combining SMILES, textual, and taxonomic functional annotations for molecular property prediction.
Little By Little: Continual Learning via Self-Activated Sparse Mixture-of-Rank Adaptive Learning
Authors: Haodong Lu, Chongyang Zhao, Jason Xue, Lina Yao, Kristen Moore, Dong Gong
Date: 2025-06-26 00:00:00
Tasks: Continual Learning, Mixture-of-Experts

Continual learning (CL) with large pre-trained models is challenged by catastrophic forgetting and task interference. Existing LoRA-based Mixture-of-Experts (MoE) approaches mitigate forgetting by assigning and freezing task-specific adapters, but suffer from interference, redundancy, and ambiguous routing due to coarse adapter-level selection. However, this design introduces three key challenges: 1) Interference: Activating full LoRA experts per input leads to subspace interference and prevents selective reuse of useful components across tasks. 2) Redundancy: Newly added experts often duplicate or contradict existing knowledge due to unnecessary activation of unrelated ranks and insufficient reuse of relevant ones. 3) Ambiguity: Overlapping features across tasks confuse the router, resulting in unstable expert assignments. As more experts accumulate, earlier task routing degrades, accelerating forgetting. We propose MoRA, a Mixture-of-Rank Adaptive learning approach with self-activated and sparse rank activation for CL. Unlike mixing multiple low-rank matrices, MoRA decomposes each rank-r update into r rank-1 components, each treated as an independent expert, enabling fine-grained mixture of rank-1 expert utilization while mitigating interference and redundancy. To avoid ambiguous routing, we propose that each rank-1 expert can infer its own relevance via intermediate activations. Coupled with our proposed rank pruning and activation budgets, MoRA adaptively selects a sparse mixture of ranks per input. We validate MoRA on continual learning tasks with CLIP and large language models (LLMs), analyzing both in-domain learning and out-of-domain forgetting/generalization during fine-tuning. MoRA shows significant effectiveness on enhancing CL with PTMs, and improving generalization while mitigating forgetting.