pwc-archive/papers-with-abstracts huggingface dataset:
Nosy Layers, Noisy Fixes: Tackling DRAs in Federated Learning Systems using Explainable AI
Authors: Meghali Nandi, Arash Shaghaghi, Nazatul Haque Sultan, Gustavo Batista, Raymond K. Zhao, Sanjay Jha
Date: 2025-05-16 00:00:00
Tasks: Federated Learning

Federated Learning (FL) has emerged as a powerful paradigm for collaborative model training while keeping client data decentralized and private. However, it is vulnerable to Data Reconstruction Attacks (DRA) such as "LoKI" and "Robbing the Fed", where malicious models sent from the server to the client can reconstruct sensitive user data. To counter this, we introduce DRArmor, a novel defense mechanism that integrates Explainable AI with targeted detection and mitigation strategies for DRA. Unlike existing defenses that focus on the entire model, DRArmor identifies and addresses the root cause (i.e., malicious layers within the model that send gradients with malicious intent) by analyzing their contribution to the output and detecting inconsistencies in gradient values. Once these malicious layers are identified, DRArmor applies defense techniques such as noise injection, pixelation, and pruning to these layers rather than the whole model, minimizing the attack surface and preserving client data privacy. We evaluate DRArmor's performance against the advanced LoKI attack across diverse datasets, including MNIST, CIFAR-10, CIFAR-100, and ImageNet, in a 200-client FL setup. Our results demonstrate DRArmor's effectiveness in mitigating data leakage, achieving high True Positive and True Negative Rates of 0.910 and 0.890, respectively. Additionally, DRArmor maintains an average accuracy of 87%, effectively protecting client privacy without compromising model performance. Compared to existing defense mechanisms, DRArmor reduces the data leakage rate by 62.5% with datasets containing 500 samples per client.
GRoQ-LoCO: Generalist and Robot-agnostic Quadruped Locomotion Control using Offline Datasets
Authors: Narayanan PP, Sarvesh Prasanth Venkatesan, Srinivas Kantha Reddy, Shishir Kolathaya
Date: 2025-05-16 00:00:00

Recent advancements in large-scale offline training have demonstrated the potential of generalist policy learning for complex robotic tasks. However, applying these principles to legged locomotion remains a challenge due to continuous dynamics and the need for real-time adaptation across diverse terrains and robot morphologies. In this work, we propose GRoQ-LoCO, a scalable, attention-based framework that learns a single generalist locomotion policy across multiple quadruped robots and terrains, relying solely on offline datasets. Our approach leverages expert demonstrations from two distinct locomotion behaviors - stair traversal (non-periodic gaits) and flat terrain traversal (periodic gaits) - collected across multiple quadruped robots, to train a generalist model that enables behavior fusion. Crucially, our framework operates solely on proprioceptive data from all robots without incorporating any robot-specific encodings. The policy is directly deployable on an Intel i7 nuc, producing low-latency control outputs without any test-time optimization. Our extensive experiments demonstrate zero-shot transfer across highly diverse quadruped robots and terrains, including hardware deployment on the Unitree Go1, a commercially available 12kg robot. Notably, we evaluate challenging cross-robot training setups where different locomotion skills are unevenly distributed across robots, yet observe successful transfer of both flat walking and stair traversal behaviors to all robots at test time. We also show preliminary walking on Stoch 5, a 70kg quadruped, on flat and outdoor terrains without requiring any fine tuning. These results demonstrate the potential of offline, data-driven learning to generalize locomotion across diverse quadruped morphologies and behaviors.
Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory
Authors: Yexiang Liu, Zekun Li, Zhi Fang, Nan Xu, Ran He, Tieniu Tan
Date: 2025-05-16 00:00:00

Recently, scaling test-time compute on Large Language Models (LLM) has garnered wide attention. However, there has been limited investigation of how various reasoning prompting strategies perform as scaling. In this paper, we focus on a standard and realistic scaling setting: majority voting. We systematically conduct experiments on 6 LLMs $\times$ 8 prompting strategies $\times$ 6 benchmarks. Experiment results consistently show that as the sampling time and computational overhead increase, complicated prompting strategies with superior initial performance gradually fall behind simple Chain-of-Thought. We analyze this phenomenon and provide theoretical proofs. Additionally, we propose a method according to probability theory to quickly and accurately predict the scaling performance and select the best strategy under large sampling times without extra resource-intensive inference in practice. It can serve as the test-time scaling law for majority voting. Furthermore, we introduce two ways derived from our theoretical analysis to significantly improve the scaling performance. We hope that our research can promote to re-examine the role of complicated prompting, unleash the potential of simple prompting strategies, and provide new insights for enhancing test-time scaling performance.
Most General Explanations of Tree Ensembles (Extended Version)
Authors: Yacine Izza, Alexey Ignatiev, Sasha Rubin, Joao Marques-Silva, Peter J. Stuckey
Date: 2025-05-16 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence (XAI) is critical for attaining trust in the operation of AI systems. A key question of an AI system is ``why was this decision made this way''. Formal approaches to XAI use a formal model of the AI system to identify abductive explanations. While abductive explanations may be applicable to a large number of inputs sharing the same concrete values, more general explanations may be preferred for numeric inputs. So-called inflated abductive explanations give intervals for each feature ensuring that any input whose values fall withing these intervals is still guaranteed to make the same prediction. Inflated explanations cover a larger portion of the input space, and hence are deemed more general explanations. But there can be many (inflated) abductive explanations for an instance. Which is the best? In this paper, we show how to find a most general abductive explanation for an AI decision. This explanation covers as much of the input space as possible, while still being a correct formal explanation of the model's behaviour. Given that we only want to give a human one explanation for a decision, the most general explanation gives us the explanation with the broadest applicability, and hence the one most likely to seem sensible. (The paper has been accepted at IJCAI2025 conference.)
Reconstructing Syllable Sequences in Abugida Scripts with Incomplete Inputs
Authors: Ye Kyaw Thu, Thazin Myint Oo
Date: 2025-05-16 00:00:00
Tasks: Data Augmentation, Prediction, Spelling Correction

This paper explores syllable sequence prediction in Abugida languages using Transformer-based models, focusing on six languages: Bengali, Hindi, Khmer, Lao, Myanmar, and Thai, from the Asian Language Treebank (ALT) dataset. We investigate the reconstruction of complete syllable sequences from various incomplete input types, including consonant sequences, vowel sequences, partial syllables (with random character deletions), and masked syllables (with fixed syllable deletions). Our experiments reveal that consonant sequences play a critical role in accurate syllable prediction, achieving high BLEU scores, while vowel sequences present a significantly greater challenge. The model demonstrates robust performance across tasks, particularly in handling partial and masked syllable reconstruction, with strong results for tasks involving consonant information and syllable masking. This study advances the understanding of sequence prediction for Abugida languages and provides practical insights for applications such as text prediction, spelling correction, and data augmentation in these scripts.
A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
Authors: Harsh Parikh, Trang Quynh Nguyen, Elizabeth A. Stuart, Kara E. Rudolph, Caleb H. Miles
Date: 2025-05-16 00:00:00
Tasks: Causal Inference, Data Integration

Data integration approaches are increasingly used to enhance the efficiency and generalizability of studies. However, a key limitation of these methods is the assumption that outcome measures are identical across datasets -- an assumption that often does not hold in practice. Consider the following opioid use disorder (OUD) studies: the XBOT trial and the POAT study, both evaluating the effect of medications for OUD on withdrawal symptom severity (not the primary outcome of either trial). While XBOT measures withdrawal severity using the subjective opiate withdrawal scale, POAT uses the clinical opiate withdrawal scale. We analyze this realistic yet challenging setting where outcome measures differ across studies and where neither study records both types of outcomes. Our paper studies whether and when integrating studies with disparate outcome measures leads to efficiency gains. We introduce three sets of assumptions -- with varying degrees of strength -- linking both outcome measures. Our theoretical and empirical results highlight a cautionary tale: integration can improve asymptotic efficiency only under the strongest assumption linking the outcomes. However, misspecification of this assumption leads to bias. In contrast, a milder assumption may yield finite-sample efficiency gains, yet these benefits diminish as sample size increases. We illustrate these trade-offs via a case study integrating the XBOT and POAT datasets to estimate the comparative effect of two medications for opioid use disorder on withdrawal symptoms. By systematically varying the assumptions linking the SOW and COW scales, we show potential efficiency gains and the risks of bias. Our findings emphasize the need for careful assumption selection when fusing datasets with differing outcome measures, offering guidance for researchers navigating this common challenge in modern data integration.
Generalization Bounds for Quantum Learning via Rényi Divergences
Authors: Naqueeb Ahmad Warsi, Ayanava Dasgupta, Masahito Hayashi
Date: 2025-05-16 00:00:00
Tasks: Generalization Bounds

This work advances the theoretical understanding of quantum learning by establishing a new family of upper bounds on the expected generalization error of quantum learning algorithms, leveraging the framework introduced by Caro et al. (2024) and a new definition for the expected true loss. Our primary contribution is the derivation of these bounds in terms of quantum and classical R\'enyi divergences, utilizing a variational approach for evaluating quantum R\'enyi divergences, specifically the Petz and a newly introduced modified sandwich quantum R\'enyi divergence. Analytically and numerically, we demonstrate the superior performance of the bounds derived using the modified sandwich quantum R\'enyi divergence compared to those based on the Petz divergence. Furthermore, we provide probabilistic generalization error bounds using two distinct techniques: one based on the modified sandwich quantum R\'enyi divergence and classical R\'enyi divergence, and another employing smooth max R\'enyi divergence.
StRuCom: A Novel Dataset of Structured Code Comments in Russian
Authors: Maria Dziuba, Valentin Malykh
Date: 2025-05-16 00:00:00

Structured code comments in docstring format are essential for code comprehension and maintenance, but existing machine learning models for their generation perform poorly for Russian compared to English. To bridge this gap, we present StRuCom - the first large-scale dataset (153K examples) specifically designed for Russian code documentation. Unlike machine-translated English datasets that distort terminology (e.g., technical loanwords vs. literal translations) and docstring structures, StRuCom combines human-written comments from Russian GitHub repositories with synthetically generated ones, ensuring compliance with Python, Java, JavaScript, C#, and Go standards through automated validation. Fine-tuning Qwen2.5-Coder models (0.5B-7B) on StRuCom shows statistically significant improvements of chrf++ and BERTScore over baseline models.
Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury's exospheric data
Authors: Adrian Kazakov, Anna Milillo, Alessandro Mura, Stavro Ivanovski, Valeria Mangano, Alessandro Aronica, Elisabetta De Angelis, Pier Paolo Di Bartolomeo, Alessandro Brin, Luca Colasanti, Miguel Escalona-Moran, Francesco Lazzarotto, Stefano Massetti, Martina Moroni, Raffaella Noschese, Fabrizio Nuccilli, Stefano Orsini, Christina Plainaki, Rosanna Rispoli, Roberto Sordini, Mirko Stumpo, Nello Vertolli
Date: 2025-05-16 00:00:00

Surface information derived from exospheric measurements at planetary bodies complements surface mapping provided by dedicated imagers, offering critical insights into surface release processes, interactions within the planetary environment, space weathering, and planetary evolution. This study explores the feasibility of deriving Mercury's regolith elemental composition from in-situ measurements of its neutral exosphere using deep neural networks (DNNs). We present a supervised feed-forward DNN architecture - a multilayer perceptron (MLP) - that, starting from exospheric densities and proton precipitation fluxes, predicts the chemical elements of the surface regolith below. It serves as an estimator for the surface-exosphere interaction and the processes leading to exosphere formation. Because the DNN requires a comprehensive exospheric dataset not available from previous missions, this study uses simulated exosphere components and simulated drivers. Extensive training and testing campaigns demonstrate the MLP's ability to accurately predict and reconstruct surface composition maps from these simulated measurements. Although this initial version does not aim to reproduce Mercury's actual surface composition, it provides a proof of concept, showcasing the algorithm's robustness and capacity for handling complex datasets to create estimators for exospheric generation models. Moreover, our tests reveal substantial potential for further development, suggesting that this method could significantly enhance the analysis of complex surface-exosphere interactions and complement planetary exosphere models. This work anticipates applying the approach to data from the BepiColombo mission, specifically the SERENA package, whose nominal phase begins in 2027.
BLEUBERI: BLEU is a surprisingly effective reward for instruction following
Authors: Yapei Chang, Yekyung Kim, Michael Krumdick, Amir Zadeh, Chuan Li, Chris Tanner, Mohit Iyyer
Date: 2025-05-16 00:00:00
Tasks: Instruction Following, Synthetic Data Generation

Reward models are central to aligning LLMs with human preferences, but they are costly to train, requiring large-scale human-labeled preference data and powerful pretrained LLM backbones. Meanwhile, the increasing availability of high-quality synthetic instruction-following datasets raises the question: can simpler, reference-based metrics serve as viable alternatives to reward models during RL-based alignment? In this paper, we show first that BLEU, a basic string-matching metric, surprisingly matches strong reward models in agreement with human preferences on general instruction-following datasets. Based on this insight, we develop BLEUBERI, a method that first identifies challenging instructions and then applies Group Relative Policy Optimization (GRPO) using BLEU directly as the reward function. We demonstrate that BLEUBERI-trained models are competitive with models trained via reward model-guided RL across four challenging instruction-following benchmarks and three different base language models. A human evaluation further supports that the quality of BLEUBERI model outputs is on par with those from reward model-aligned models. Moreover, BLEUBERI models generate outputs that are more factually grounded than competing methods. Overall, we show that given access to high-quality reference outputs (easily obtained via existing instruction-following datasets or synthetic data generation), string matching-based metrics are cheap yet effective proxies for reward models during alignment. We release our code and data at https://github.com/lilakk/BLEUBERI.
Predicting Student Dropout Risk With A Dual-Modal Abrupt Behavioral Changes Approach
Authors: Jiabei Cheng, Zhen-Qun Yang, Jiannong Cao, Yu Yang, Xinzhe Zheng
Date: 2025-05-16 00:00:00
Tasks: Prediction, Student dropout

Timely prediction of students at high risk of dropout is critical for early intervention and improving educational outcomes. However, in offline educational settings, poor data quality, limited scale, and high heterogeneity often hinder the application of advanced machine learning models. Furthermore, while educational theories provide valuable insights into dropout phenomena, the lack of quantifiable metrics for key indicators limits their use in data-driven modeling. Through data analysis and a review of educational literature, we identified abrupt changes in student behavior as key early signals of dropout risk. To address this, we propose the Dual-Modal Multiscale Sliding Window (DMSW) Model, which integrates academic performance and behavioral data to dynamically capture behavior patterns using minimal data. The DMSW model improves prediction accuracy by 15% compared to traditional methods, enabling educators to identify high-risk students earlier, provide timely support, and foster a more inclusive learning environment. Our analysis highlights key behavior patterns, offering practical insights for preventive strategies and tailored support. These findings bridge the gap between theory and practice in dropout prediction, giving educators an innovative tool to enhance student retention and outcomes.
Can Global XAI Methods Reveal Injected Bias in LLMs? SHAP vs Rule Extraction vs RuleSHAP
Authors: Francesco Sovrano
Date: 2025-05-16 00:00:00
Tasks: Bias Detection, Misinformation

Generative AI systems can help spread information but also misinformation and biases, potentially undermining the UN Sustainable Development Goals (SDGs). Explainable AI (XAI) aims to reveal the inner workings of AI systems and expose misbehaviours or biases. However, current XAI tools, built for simpler models, struggle to handle the non-numerical nature of large language models (LLMs). This paper examines the effectiveness of global XAI methods, such as rule-extraction algorithms and SHAP, in detecting bias in LLMs. To do so, we first show a text-to-ordinal mapping strategy to convert non-numerical inputs/outputs into numerical features, enabling these tools to identify (some) misinformation-related biases in LLM-generated content. Then, we inject non-linear biases of varying complexity (univariate, conjunctive, and non-convex) into widespread LLMs like ChatGPT and Llama via system instructions, using global XAI methods to detect them. This way, we found that RuleFit struggles with conjunctive and non-convex biases, while SHAP can approximate conjunctive biases but cannot express them as actionable rules. Hence, we introduce RuleSHAP, a global rule extraction algorithm combining SHAP and RuleFit to detect more non-univariate biases, improving injected bias detection over RuleFit by +94% (MRR@1) on average.
NoPE: The Counting Power of Transformers with No Positional Encodings
Authors: Chris Köcher, Alexander Kozachinskiy, Anthony Widjaja Lin, Marco Sälzer, Georg Zetzsche
Date: 2025-05-16 00:00:00
Tasks: Hard Attention

Positional Encodings (PEs) seem to be indispensable for ensuring expressiveness of transformers; without them attention transformers reduce to a bag-of-word model. NoPE-transformers (i.e. with No PEs) with unique hard attention mechanisms were very recently shown to only be able to express regular languages, i.e., with limited counting ability. This paper shows that, with average hard attention mechanisms, NoPE-transformers are still surprisingly expressive: they can express counting languages corresponding to nonnegative integer solutions to multivariate polynomial equations (i.e. Diophantine equations), reasoning about which is well-known to be undecidable. In fact, we provide a precise characterization of languages expressible by Average Hard Attention NoPE-Transformers (NoPE-AHATs): they correspond precisely to what we call \emph{semi-algebraic sets}, i.e., finite unions of sets of nonnegative integer solutions to systems of multivariate polynomial inequations. We obtain several interesting consequences of our characterization. Firstly, NoPE-transformers can express counting properties that are far more complex than established models like simplified counter machines and Petri nets, but cannot express a very simple counting property of PARITY. Secondly, the problem of analyzing NoPE-transformers is undecidable, e.g., whether a given NoPE transformer classifies all input strings in one class. To complement our results, we exhibit a counting language that is not expressible by average hard attention transformers even with arbitrary PEs but is expressible in the circuit complexity class TC$^0$, answering an open problem.
Audio Turing Test: Benchmarking the Human-likeness of Large Language Model-based Text-to-Speech Systems in Chinese
Authors: Xihuai Wang, Ziyi Zhao, Siyu Ren, Shao Zhang, Song Li, Xiaoyu Li, Ziwen Wang, Lin Qiu, Guanglu Wan, Xuezhi Cao, Xunliang Cai, Weinan Zhang
Date: 2025-05-16 00:00:00
Tasks: Benchmarking, Language Modeling, Language Modelling, Large Language Model, text-to-speech, Text to Speech

Recent advances in large language models (LLMs) have significantly improved text-to-speech (TTS) systems, enhancing control over speech style, naturalness, and emotional expression, which brings TTS Systems closer to human-level performance. Although the Mean Opinion Score (MOS) remains the standard for TTS System evaluation, it suffers from subjectivity, environmental inconsistencies, and limited interpretability. Existing evaluation datasets also lack a multi-dimensional design, often neglecting factors such as speaking styles, context diversity, and trap utterances, which is particularly evident in Chinese TTS evaluation. To address these challenges, we introduce the Audio Turing Test (ATT), a multi-dimensional Chinese corpus dataset ATT-Corpus paired with a simple, Turing-Test-inspired evaluation protocol. Instead of relying on complex MOS scales or direct model comparisons, ATT asks evaluators to judge whether a voice sounds human. This simplification reduces rating bias and improves evaluation robustness. To further support rapid model development, we also finetune Qwen2-Audio-Instruct with human judgment data as Auto-ATT for automatic evaluation. Experimental results show that ATT effectively differentiates models across specific capability dimensions using its multi-dimensional design. Auto-ATT also demonstrates strong alignment with human evaluations, confirming its value as a fast and reliable assessment tool. The white-box ATT-Corpus and Auto-ATT can be found in ATT Hugging Face Collection (https://huggingface.co/collections/meituan/audio-turing-test-682446320368164faeaf38a4).