pwc-archive/papers-with-abstracts huggingface dataset:
FairPO: Robust Preference Optimization for Fair Multi-Label Learning
Authors: Soumen Kumar Mondal, Akshit Varmora, Prateek Chanda, Ganesh Ramakrishnan
Date: 2025-05-05 00:00:00
Tasks: Fairness, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning

We propose FairPO, a novel framework designed to promote fairness in multi-label classification by directly optimizing preference signals with a group robustness perspective. In our framework, the set of labels is partitioned into privileged and non-privileged groups, and a preference-based loss inspired by Direct Preference Optimization (DPO) is employed to more effectively differentiate true positive labels from confusing negatives within the privileged group, while preserving baseline classification performance for non-privileged labels. By framing the learning problem as a robust optimization over groups, our approach dynamically adjusts the training emphasis toward groups with poorer performance, thereby mitigating bias and ensuring a fairer treatment across diverse label categories. In addition, we outline plans to extend this approach by investigating alternative loss formulations such as Simple Preference Optimisation (SimPO) and Contrastive Preference Optimization (CPO) to exploit reference-free reward formulations and contrastive training signals. Furthermore, we plan to extend FairPO with multilabel generation capabilities, enabling the model to dynamically generate diverse and coherent label sets for ambiguous inputs.
Towards One-shot Federated Learning: Advances, Challenges, and Future Directions
Authors: Flora Amato, Lingyu Qiu, Mohammad Tanveer, Salvatore Cuomo, Fabio Giampaolo, Francesco Piccialli
Date: 2025-05-05 00:00:00
Tasks: Federated Learning, Survey

One-shot FL enables collaborative training in a single round, eliminating the need for iterative communication, making it particularly suitable for use in resource-constrained and privacy-sensitive applications. This survey offers a thorough examination of One-shot FL, highlighting its distinct operational framework compared to traditional federated approaches. One-shot FL supports resource-limited devices by enabling single-round model aggregation while maintaining data locality. The survey systematically categorizes existing methodologies, emphasizing advancements in client model initialization, aggregation techniques, and strategies for managing heterogeneous data distributions. Furthermore, we analyze the limitations of current approaches, particularly in terms of scalability and generalization in non-IID settings. By analyzing cutting-edge techniques and outlining open challenges, this survey aspires to provide a comprehensive reference for researchers and practitioners aiming to design and implement One-shot FL systems, advancing the development and adoption of One-shot FL solutions in a real-world, resource-constrained scenario.
Rewriting Pre-Training Data Boosts LLM Performance in Math and Code
Authors: Kazuki Fujii, Yukito Tajima, Sakae Mizuki, Hinari Shimada, Taihei Shiotani, Koshiro Saito, Masanari Ohi, Masaki Kawamura, Taishi Nakamura, Takumi Okamoto, Shigeki Ishida, Kakeru Hattori, Youmi Ma, Hiroya Takamura, Rio Yokota, Naoaki Okazaki
Date: 2025-05-05 00:00:00
Tasks: Code Generation, GSM8K, HumanEval, Math, Mathematical Reasoning, Program Synthesis

The performance of large language models (LLMs) in program synthesis and mathematical reasoning is fundamentally limited by the quality of their pre-training corpora. We introduce two openly licensed datasets, released under the Llama 3.3 Community License, that significantly enhance LLM performance by systematically rewriting public data. SwallowCode (approximately 16.1 billion tokens) refines Python snippets from The-Stack-v2 through a novel four-stage pipeline: syntax validation, pylint-based style filtering, and a two-stage LLM rewriting process that enforces style conformity and transforms snippets into self-contained, algorithmically efficient examples. Unlike prior methods that rely on exclusionary filtering or limited transformations, our transform-and-retain approach upgrades low-quality code, maximizing data utility. SwallowMath (approximately 2.3 billion tokens) enhances Finemath-4+ by removing boilerplate, restoring context, and reformatting solutions into concise, step-by-step explanations. Within a fixed 50 billion token training budget, continual pre-training of Llama-3.1-8B with SwallowCode boosts pass@1 by +17.0 on HumanEval and +17.7 on HumanEval+ compared to Stack-Edu, surpassing the baseline model's code generation capabilities. Similarly, substituting SwallowMath yields +12.4 accuracy on GSM8K and +7.6 on MATH. Ablation studies confirm that each pipeline stage contributes incrementally, with rewriting delivering the largest gains. All datasets, prompts, and checkpoints are publicly available, enabling reproducible research and advancing LLM pre-training for specialized domains.
Prefill-Based Jailbreak: A Novel Approach of Bypassing LLM Safety Boundary
Authors: Yakai Li, Jiekang Hu, Weiduan Sang, Luping Ma, Jing Xie, Weijuan Zhang, Aimin Yu, Shijie Zhao, Qingjia Huang, Qihang Zhou
Date: 2025-04-28 00:00:00

Large Language Models (LLMs) are designed to generate helpful and safe content. However, adversarial attacks, commonly referred to as jailbreak, can bypass their safety protocols, prompting LLMs to generate harmful content or reveal sensitive data. Consequently, investigating jailbreak methodologies is crucial for exposing systemic vulnerabilities within LLMs, ultimately guiding the continuous implementation of security enhancements by developers. In this paper, we introduce a novel jailbreak attack method that leverages the prefilling feature of LLMs, a feature designed to enhance model output constraints. Unlike traditional jailbreak methods, the proposed attack circumvents LLMs' safety mechanisms by directly manipulating the probability distribution of subsequent tokens, thereby exerting control over the model's output. We propose two attack variants: Static Prefilling (SP), which employs a universal prefill text, and Optimized Prefilling (OP), which iteratively optimizes the prefill text to maximize the attack success rate. Experiments on six state-of-the-art LLMs using the AdvBench benchmark validate the effectiveness of our method and demonstrate its capability to substantially enhance attack success rates when combined with existing jailbreak approaches. The OP method achieved attack success rates of up to 99.82% on certain models, significantly outperforming baseline methods. This work introduces a new jailbreak attack method in LLMs, emphasizing the need for robust content validation mechanisms to mitigate the adversarial exploitation of prefilling features. All code and data used in this paper are publicly available.
T2S: High-resolution Time Series Generation with Text-to-Series Diffusion Models
Authors: Yunfeng Ge, Jiawei Li, Yiji Zhao, Haomin Wen, Zhao Li, Meikang Qiu, Hongyan Li, Ming Jin, Shirui Pan
Date: 2025-05-05 00:00:00
Tasks: Time Series, Time Series Generation

Text-to-Time Series generation holds significant potential to address challenges such as data sparsity, imbalance, and limited availability of multimodal time series datasets across domains. While diffusion models have achieved remarkable success in Text-to-X (e.g., vision and audio data) generation, their use in time series generation remains in its nascent stages. Existing approaches face two critical limitations: (1) the lack of systematic exploration of general-proposed time series captions, which are often domain-specific and struggle with generalization; and (2) the inability to generate time series of arbitrary lengths, limiting their applicability to real-world scenarios. In this work, we first categorize time series captions into three levels: point-level, fragment-level, and instance-level. Additionally, we introduce a new fragment-level dataset containing over 600,000 high-resolution time series-text pairs. Second, we propose Text-to-Series (T2S), a diffusion-based framework that bridges the gap between natural language and time series in a domain-agnostic manner. T2S employs a length-adaptive variational autoencoder to encode time series of varying lengths into consistent latent embeddings. On top of that, T2S effectively aligns textual representations with latent embeddings by utilizing Flow Matching and employing Diffusion Transformer as the denoiser. We train T2S in an interleaved paradigm across multiple lengths, allowing it to generate sequences of any desired length. Extensive evaluations demonstrate that T2S achieves state-of-the-art performance across 13 datasets spanning 12 domains.
Security Bug Report Prediction Within and Across Projects: A Comparative Study of BERT and Random Forest
Authors: Farnaz Soltaniani, Mohammad Ghafari, Mohammed Sayagh
Date: 2025-04-28 00:00:00

Early detection of security bug reports (SBRs) is crucial for preventing vulnerabilities and ensuring system reliability. While machine learning models have been developed for SBR prediction, their predictive performance still has room for improvement. In this study, we conduct a comprehensive comparison between BERT and Random Forest (RF), a competitive baseline for predicting SBRs. The results show that RF outperforms BERT with a 34% higher average G-measure for within-project predictions. Adding only SBRs from various projects improves both models' average performance. However, including both security and nonsecurity bug reports significantly reduces RF's average performance to 46%, while boosts BERT to its best average performance of 66%, surpassing RF. In cross-project SBR prediction, BERT achieves a remarkable 62% G-measure, which is substantially higher than RF.
Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks
Authors: Baoxia Du, Hongyang Du, Dusit Niyato, Ruidong Li
Date: 2025-05-05 00:00:00
Tasks: Question Answering, Semantic Communication, Visual Question Answering, Visual Question Answering (VQA)

Task-oriented semantic communication has emerged as a fundamental approach for enhancing performance in various communication scenarios. While recent advances in Generative Artificial Intelligence (GenAI), such as Large Language Models (LLMs), have been applied to semantic communication designs, the potential of Large Multimodal Models (LMMs) remains largely unexplored. In this paper, we investigate an LMM-based vehicle AI assistant using a Large Language and Vision Assistant (LLaVA) and propose a task-oriented semantic communication framework to facilitate efficient interaction between users and cloud servers. To reduce computational demands and shorten response time, we optimize LLaVA's image slicing to selectively focus on areas of utmost interest to users. Additionally, we assess the importance of image patches by combining objective and subjective user attention, adjusting energy usage for transmitting semantic information. This strategy optimizes resource utilization, ensuring precise transmission of critical information. We construct a Visual Question Answering (VQA) dataset for traffic scenarios to evaluate effectiveness. Experimental results show that our semantic communication framework significantly increases accuracy in answering questions under the same channel conditions, performing particularly well in environments with poor Signal-to-Noise Ratios (SNR). Accuracy can be improved by 13.4% at an SNR of 12dB and 33.1% at 10dB, respectively.
Bielik 11B v2 Technical Report
Authors: Krzysztof Ociepa, Łukasz Flis, Krzysztof Wróbel, Adrian Gwoździej, Remigiusz Kinas
Date: 2025-05-05 00:00:00
Tasks: Language Modeling, Language Modelling, Quantization

We present Bielik 11B v2, a state-of-the-art language model optimized for Polish text processing. Built on the Mistral 7B v0.2 architecture and scaled to 11B parameters using depth up-scaling, this model demonstrates exceptional performance across Polish language benchmarks while maintaining strong cross-lingual capabilities. We introduce two key technical innovations: Weighted Instruction Cross-Entropy Loss, which optimizes learning across diverse instruction types by assigning quality-based weights to training examples, and Adaptive Learning Rate, which dynamically adjusts based on context length. Comprehensive evaluation across multiple benchmarks demonstrates that Bielik 11B v2 outperforms many larger models, including those with 2-6 times more parameters, and significantly surpasses other specialized Polish language models on tasks ranging from linguistic understanding to complex reasoning. The model's parameter efficiency and extensive quantization options enable deployment across various hardware configurations, advancing Polish language AI capabilities and establishing new benchmarks for resource-efficient language modeling in less-represented languages.
Diagnostic Uncertainty in Pneumonia Detection using CNN MobileNetV2 and CNN from Scratch
Authors: Kennard Norbert Sudiardjo, Islam Nur Alam, Wilson Wijaya, Lili Ayu Wulandhari
Date: 2025-05-05 00:00:00
Tasks: Diagnostic, Pneumonia Detection

Pneumonia Diagnosis, though it is crucial for an effective treatment, it can be hampered by uncertainty. This uncertainty starts to arise due to some factors like atypical presentations, limitations of diagnostic tools such as chest X-rays, and the presence of co-existing respiratory conditions. This research proposes one of the supervised learning methods, CNN. Using MobileNetV2 as the pre-trained one with ResNet101V2 architecture and using Keras API as the built from scratch model, for identifying lung diseases especially pneumonia. The datasets used in this research were obtained from the website through Kaggle. The result shows that by implementing CNN MobileNetV2 and CNN from scratch the result is promising. While validating data, MobileNetV2 performs with stability and minimal overfitting, while the training accuracy increased to 84.87% later it slightly decreased to 78.95%, with increasing validation loss from 0.499 to 0.6345. Nonetheless, MobileNetV2 is more stable. Although it takes more time to train each epoch. Meanwhile, after the 10th epoch, the Scratch model displayed more instability and overfitting despite having higher validation accuracy, training accuracy decreased significantly to 78.12% and the validation loss increased from 0.5698 to 1.1809. With these results, ResNet101V2 offers stability, and the Scratch model offers high accuracy.
SAGA: A Security Architecture for Governing AI Agentic Systems
Authors: Georgios Syros, Anshuman Suri, Cristina Nita-Rotaru, Alina Oprea
Date: 2025-04-27 00:00:00
Tasks: Large Language Model

Large Language Model (LLM)-based agents increasingly interact, collaborate, and delegate tasks to one another autonomously with minimal human interaction. Industry guidelines for agentic system governance emphasize the need for users to maintain comprehensive control over their agents, mitigating potential damage from malicious agents. Several proposed agentic system designs address agent identity, authorization, and delegation, but remain purely theoretical, without concrete implementation and evaluation. Most importantly, they do not provide user-controlled agent management. To address this gap, we propose SAGA, a Security Architecture for Governing Agentic systems, that offers user oversight over their agents' lifecycle. In our design, users register their agents with a central entity, the Provider, that maintains agents contact information, user-defined access control policies, and helps agents enforce these policies on inter-agent communication. We introduce a cryptographic mechanism for deriving access control tokens, that offers fine-grained control over an agent's interaction with other agents, balancing security and performance consideration. We evaluate SAGA on several agentic tasks, using agents in different geolocations, and multiple on-device and cloud LLMs, demonstrating minimal performance overhead with no impact on underlying task utility in a wide range of conditions. Our architecture enables secure and trustworthy deployment of autonomous agents, accelerating the responsible adoption of this technology in sensitive environments.
Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL
Authors: Jiarui Yao, Yifan Hao, Hanning Zhang, Hanze Dong, Wei Xiong, Nan Jiang, Tong Zhang
Date: 2025-05-05 00:00:00
Tasks: Mathematical Reasoning

Chain-of-thought (CoT) reasoning in large language models (LLMs) can be formalized as a latent variable problem, where the model needs to generate intermediate reasoning steps. While prior approaches such as iterative reward-ranked fine-tuning (RAFT) have relied on such formulations, they typically apply uniform inference budgets across prompts, which fails to account for variability in difficulty and convergence behavior. This work identifies the main bottleneck in CoT training as inefficient stochastic gradient estimation due to static sampling strategies. We propose GVM-RAFT, a prompt-specific Dynamic Sample Allocation Strategy designed to minimize stochastic gradient variance under a computational budget constraint. The method dynamically allocates computational resources by monitoring prompt acceptance rates and stochastic gradient norms, ensuring that the resulting gradient variance is minimized. Our theoretical analysis shows that the proposed dynamic sampling strategy leads to accelerated convergence guarantees under suitable conditions. Experiments on mathematical reasoning show that GVM-RAFT achieves a 2-4x speedup and considerable accuracy improvements over vanilla RAFT. The proposed dynamic sampling strategy is general and can be incorporated into other reinforcement learning algorithms, such as GRPO, leading to similar improvements in convergence and test accuracy. Our code is available at https://github.com/RLHFlow/GVM.
TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks
Authors: Mohammad M Maheri, Hamed Haddadi, Alex Davidson
Date: 2025-04-27 00:00:00
Tasks: Privacy Preserving, SNARKS

Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires access to model weights and (potentially sensitive or private) training data. So-called Zero-knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) would appear to provide the capability to verify model inference without access to such sensitive data. However, applying ZK-SNARKs to modern neural networks, such as transformers and large vision models, introduces significant computational overhead. We present TeleSparse, a ZK-friendly post-processing mechanisms to produce practical solutions to this problem. TeleSparse tackles two fundamental challenges inherent in applying ZK-SNARKs to modern neural networks: (1) Reducing circuit constraints: Over-parameterized models result in numerous constraints for ZK-SNARK verification, driving up memory and proof generation costs. We address this by applying sparsification to neural network models, enhancing proof efficiency without compromising accuracy or security. (2) Minimizing the size of lookup tables required for non-linear functions, by optimizing activation ranges through neural teleportation, a novel adaptation for narrowing activation functions' range. TeleSparse reduces prover memory usage by 67% and proof generation time by 46% on the same model, with an accuracy trade-off of approximately 1%. We implement our framework using the Halo2 proving system and demonstrate its effectiveness across multiple architectures (Vision-transformer, ResNet, MobileNet) and datasets (ImageNet,CIFAR-10,CIFAR-100). This work opens new directions for ZK-friendly model design, moving toward scalable, resource-efficient verifiable deep learning.
Quantitative Analysis of Performance Drop in DeepSeek Model Quantization
Authors: Enbo Zhao, Yi Shen, Shuming Shi, Jieyun Huang, Zhihao Chen, Ning Wang, Siqi Xiao, Jian Zhang, Kai Wang, Shiguo Lian
Date: 2025-05-05 00:00:00
Tasks: GPU, Quantization

Recently, there is a high demand for deploying DeepSeek-R1 and V3 locally, possibly because the official service often suffers from being busy and some organizations have data privacy concerns. While single-machine deployment offers infrastructure simplicity, the models' 671B FP8 parameter configuration exceeds the practical memory limits of a standard 8-GPU machine. Quantization is a widely used technique that helps reduce model memory consumption. However, it is unclear what the performance of DeepSeek-R1 and V3 will be after being quantized. This technical report presents the first quantitative evaluation of multi-bitwidth quantization across the complete DeepSeek model spectrum. Key findings reveal that 4-bit quantization maintains little performance degradation versus FP8 while enabling single-machine deployment on standard NVIDIA GPU devices. We further propose DQ3_K_M, a dynamic 3-bit quantization method that significantly outperforms traditional Q3_K_M variant on various benchmarks, which is also comparable with 4-bit quantization (Q4_K_M) approach in most tasks. Moreover, DQ3_K_M supports single-machine deployment configurations for both NVIDIA H100/A100 and Huawei 910B. Our implementation of DQ3\_K\_M is released at https://github.com/UnicomAI/DeepSeek-Eval, containing optimized 3-bit quantized variants of both DeepSeek-R1 and DeepSeek-V3.
CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenges
Authors: Yu Li, Qizhi Pei, Mengyuan Sun, Honglin Lin, Chenlin Ming, Xin Gao, Jiang Wu, Conghui He, Lijun Wu
Date: 2025-04-27 00:00:00

Large language models (LLMs) have demonstrated remarkable capabilities, especially the recent advancements in reasoning, such as o1 and o3, pushing the boundaries of AI. Despite these impressive achievements in mathematics and coding, the reasoning abilities of LLMs in domains requiring cryptographic expertise remain underexplored. In this paper, we introduce CipherBank, a comprehensive benchmark designed to evaluate the reasoning capabilities of LLMs in cryptographic decryption tasks. CipherBank comprises 2,358 meticulously crafted problems, covering 262 unique plaintexts across 5 domains and 14 subdomains, with a focus on privacy-sensitive and real-world scenarios that necessitate encryption. From a cryptographic perspective, CipherBank incorporates 3 major categories of encryption methods, spanning 9 distinct algorithms, ranging from classical ciphers to custom cryptographic techniques. We evaluate state-of-the-art LLMs on CipherBank, e.g., GPT-4o, DeepSeek-V3, and cutting-edge reasoning-focused models such as o1 and DeepSeek-R1. Our results reveal significant gaps in reasoning abilities not only between general-purpose chat LLMs and reasoning-focused LLMs but also in the performance of current reasoning-focused models when applied to classical cryptographic decryption tasks, highlighting the challenges these models face in understanding and manipulating encrypted data. Through detailed analysis and error investigations, we provide several key observations that shed light on the limitations and potential improvement areas for LLMs in cryptographic reasoning. These findings underscore the need for continuous advancements in LLM reasoning capabilities.