pwc-archive/papers-with-abstracts huggingface dataset:
Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized Detection
Authors: Boyong He, Yuxiang Ji, Qianwen Ye, Zhuoyue Tan, Liaoni Wu
Date: 2025-03-03 00:00:00
Proceeding: CVPR 2025 1
Tasks: Domain Adaptation, Domain Generalization, object-detection, Object Detection, Robust Object Detection, Scene Generation, Transfer Learning

Domain generalization (DG) for object detection aims to enhance detectors' performance in unseen scenarios. This task remains challenging due to complex variations in real-world applications. Recently, diffusion models have demonstrated remarkable capabilities in diverse scene generation, which inspires us to explore their potential for improving DG tasks. Instead of generating images, our method extracts multi-step intermediate features during the diffusion process to obtain domain-invariant features for generalized detection. Furthermore, we propose an efficient knowledge transfer framework that enables detectors to inherit the generalization capabilities of diffusion models through feature and object-level alignment, without increasing inference time. We conduct extensive experiments on six challenging DG benchmarks. The results demonstrate that our method achieves substantial improvements of 14.0% mAP over existing DG approaches across different domains and corruption types. Notably, our method even outperforms most domain adaptation methods without accessing any target domain data. Moreover, the diffusion-guided detectors show consistent improvements of 15.9% mAP on average compared to the baseline. Our work aims to present an effective approach for domain-generalized detection and provide potential insights for robust visual recognition in real-world scenarios. The code is available at \href{https://github.com/heboyong/Generalized-Diffusion-Detector}{Generalized Diffusion Detector}
Generalizable LLM Learning of Graph Synthetic Data with Reinforcement Learning
Authors: Yizhuo Zhang, Heng Wang, Shangbin Feng, Zhaoxuan Tan, Xinyun Liu, Yulia Tsvetkov
Date: 2025-06-01 00:00:00

Previous research has sought to enhance the graph reasoning capabilities of LLMs by supervised fine-tuning on synthetic graph data. While these led to specialized LLMs better at solving graph algorithm problems, we don't need LLMs for shortest path: we need generalization from synthetic graph data to real-world tasks with implicit graph structures. In this work, we propose to unlock generalizable learning of graph synthetic data with reinforcement learning. We first design solution-based and process-based rewards for synthetic graph problems: instead of rigid memorizing response patterns in direct fine-tuning, we posit that RL would help LLMs grasp the essentials underlying graph reasoning and alleviate overfitting. We employ RL algorithms such as GRPO and DPO, aligning both off-the-shelf LLMs and LLMs fine-tuned on synthetic graph data. We then compare them against existing settings on both in-domain synthetic tasks and out-of-domain real-world tasks with implicit graph structures such as multi-hop QA, structured planning, and more. Extensive experiments demonstrate that our RL recipe leads to statistically significant improvement on 5 datasets, with an average gain of 12.9\% over baseline settings. Further analysis reveals that process-based rewards consistently outperform solution-based rewards, mixing synthetic and real-world task data yields potential gains, while compositionality and explainable intermediate steps remains a critical challenge even after RL.
CoBRA: Quantifying Strategic Language Use and LLM Pragmatics
Authors: Anshun Asher Zheng, Junyi Jessy Li, David I. Beaver
Date: 2025-06-01 00:00:00

Language is often used strategically, particularly in high-stakes, adversarial settings, yet most work on pragmatics and LLMs centers on cooperativity. This leaves a gap in systematic understanding of non-cooperative discourse. To address this, we introduce CoBRA (Cooperation-Breach Response Assessment), along with three interpretable metrics -- Benefit at Turn (BaT), Penalty at Turn (PaT), and Normalized Relative Benefit at Turn (NRBaT) -- to quantify the perceived strategic effects of discourse moves. We also present CHARM, an annotated dataset of real courtroom cross-examinations, to demonstrate the framework's effectiveness. Using these tools, we evaluate a range of LLMs and show that LLMs generally exhibit limited pragmatic understanding of strategic language. While model size shows an increase in performance on our metrics, reasoning ability does not help and largely hurts, introducing overcomplication and internal confusion.
CyberBOT: Towards Reliable Cybersecurity Education via Ontology-Grounded Retrieval Augmented Generation
Authors: Chengshuai Zhao, Riccardo De Maria, Tharindu Kumarage, Kumar Satvik Chaudhary, Garima Agrawal, Yiwen Li, Jongchan Park, Yuli Deng, Ying-Chih Chen, Huan Liu
Date: 2025-04-01 00:00:00
Tasks: Chatbot, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Advancements in large language models (LLMs) have enabled the development of intelligent educational tools that support inquiry-based learning across technical domains. In cybersecurity education, where accuracy and safety are paramount, systems must go beyond surface-level relevance to provide information that is both trustworthy and domain-appropriate. To address this challenge, we introduce CyberBOT, a question-answering chatbot that leverages a retrieval-augmented generation (RAG) pipeline to incorporate contextual information from course-specific materials and validate responses using a domain-specific cybersecurity ontology. The ontology serves as a structured reasoning layer that constrains and verifies LLM-generated answers, reducing the risk of misleading or unsafe guidance. CyberBOT has been deployed in a large graduate-level course at Arizona State University (ASU), where more than one hundred students actively engage with the system through a dedicated web-based platform. Computational evaluations in lab environments highlight the potential capacity of CyberBOT, and a forthcoming field study will evaluate its pedagogical impact. By integrating structured domain reasoning with modern generative capabilities, CyberBOT illustrates a promising direction for developing reliable and curriculum-aligned AI applications in specialized educational contexts.
Do Language Models Mirror Human Confidence? Exploring Psychological Insights to Address Overconfidence in LLMs
Authors: Chenjun Xu, Bingbing Wen, Bin Han, Robert Wolfe, Lucy Lu Wang, Bill Howe
Date: 2025-05-31 00:00:00
Tasks: MMLU

Psychology research has shown that humans are poor at estimating their performance on tasks, tending towards underconfidence on easy tasks and overconfidence on difficult tasks. We examine three LLMs, Llama-3-70B-instruct, Claude-3-Sonnet, and GPT-4o, on a range of QA tasks of varying difficulty, and show that models exhibit subtle differences from human patterns of overconfidence: less sensitive to task difficulty, and when prompted to answer based on different personas -- e.g., expert vs layman, or different race, gender, and ages -- the models will respond with stereotypically biased confidence estimations even though their underlying answer accuracy remains the same. Based on these observations, we propose Answer-Free Confidence Estimation (AFCE) to improve confidence calibration and LLM interpretability in these settings. AFCE is a self-assessment method that employs two stages of prompting, first eliciting only confidence scores on questions, then asking separately for the answer. Experiments on the MMLU and GPQA datasets spanning subjects and difficulty show that this separation of tasks significantly reduces overconfidence and delivers more human-like sensitivity to task difficulty.
AhaKV: Adaptive Holistic Attention-Driven KV Cache Eviction for Efficient Inference of Large Language Models
Authors: Yifeng Gu, Zicong Jiang, Jianxiu Jin, Kailing Guo, Ziyang Zhang, Xiangmin Xu
Date: 2025-06-04 00:00:00

Large Language Models (LLMs) have significantly advanced the field of Artificial Intelligence. However, their deployment is resource-intensive, not only due to the large number of model parameters but also because the (Key-Value) KV cache consumes a lot of memory during inference. While several works propose reducing the KV cache by evicting the unnecessary tokens, these approaches rely on accumulated attention score as eviction score to quantify the importance of the token. We identify the accumulated attention score is biased and it decreases with the position of the tokens in the mathematical expectation. As a result, the retained tokens concentrate on the initial positions, limiting model's access to global contextual information. To address this issue, we propose Adaptive holistic attention KV (AhaKV), it addresses the bias of the accumulated attention score by adaptively tuning the scale of softmax according the expectation of information entropy of attention scores. To make use of the holistic attention information in self-attention mechanism, AhaKV utilize the information of value vectors, which is overlooked in previous works, to refine the adaptive score. We show theoretically that our method is well suited for bias reduction. We deployed AhaKV on different models with a fixed cache budget. Experiments show that AhaKV successfully mitigates bias and retains crucial tokens across global context and achieve state-of-the-art results against other related work on several benchmark tasks.
TextAtari: 100K Frames Game Playing with Language Agents
Authors: Wenhao Li, Wenwu Li, Chuyun Shen, Junjie Sheng, Zixiao Huang, Di wu, Yun Hua, Wei Yin, Xiangfeng Wang, Hongyuan Zha, Bo Jin
Date: 2025-06-04 00:00:00
Tasks: Atari Games, Decision Making, Representation Learning, Sequential Decision Making

We present TextAtari, a benchmark for evaluating language agents on very long-horizon decision-making tasks spanning up to 100,000 steps. By translating the visual state representations of classic Atari games into rich textual descriptions, TextAtari creates a challenging test bed that bridges sequential decision-making with natural language processing. The benchmark includes nearly 100 distinct tasks with varying complexity, action spaces, and planning horizons, all rendered as text through an unsupervised representation learning framework (AtariARI). We evaluate three open-source large language models (Qwen2.5-7B, Gemma-7B, and Llama3.1-8B) across three agent frameworks (zero-shot, few-shot chain-of-thought, and reflection reasoning) to assess how different forms of prior knowledge affect performance on these long-horizon challenges. Four scenarios-Basic, Obscured, Manual Augmentation, and Reference-based-investigate the impact of semantic understanding, instruction comprehension, and expert demonstrations on agent decision-making. Our results reveal significant performance gaps between language agents and human players in extensive planning tasks, highlighting challenges in sequential reasoning, state tracking, and strategic planning across tens of thousands of steps. TextAtari provides standardized evaluation protocols, baseline implementations, and a framework for advancing research at the intersection of language models and planning.
POLARIS: A High-contrast Polarimetric Imaging Benchmark Dataset for Exoplanetary Disk Representation Learning
Authors: Fangyi Cao, Bin Ren, ZiHao Wang, Shiwei Fu, Youbin Mo, Xiaoyang Liu, Yuzhou Chen, Weixin Yao
Date: 2025-06-04 00:00:00
Tasks: Representation Learning

With over 1,000,000 images from more than 10,000 exposures using state-of-the-art high-contrast imagers (e.g., Gemini Planet Imager, VLT/SPHERE) in the search for exoplanets, can artificial intelligence (AI) serve as a transformative tool in imaging Earth-like exoplanets in the coming decade? In this paper, we introduce a benchmark and explore this question from a polarimetric image representation learning perspective. Despite extensive investments over the past decade, only a few new exoplanets have been directly imaged. Existing imaging approaches rely heavily on labor-intensive labeling of reference stars, which serve as background to extract circumstellar objects (disks or exoplanets) around target stars. With our POLARIS (POlarized Light dAta for total intensity Representation learning of direct Imaging of exoplanetary Systems) dataset, we classify reference star and circumstellar disk images using the full public SPHERE/IRDIS polarized-light archive since 2014, requiring less than 10 percent manual labeling. We evaluate a range of models including statistical, generative, and large vision-language models and provide baseline performance. We also propose an unsupervised generative representation learning framework that integrates these models, achieving superior performance and enhanced representational power. To our knowledge, this is the first uniformly reduced, high-quality exoplanet imaging dataset, rare in astrophysics and machine learning. By releasing this dataset and baselines, we aim to equip astrophysicists with new tools and engage data scientists in advancing direct exoplanet imaging, catalyzing major interdisciplinary breakthroughs.
TokAlign: Efficient Vocabulary Adaptation via Token Alignment
Authors: Chong Li, Jiajun Zhang, Chengqing Zong
Date: 2025-06-04 00:00:00
Tasks: Sentence, Text Compression, Transfer Learning

Tokenization serves as a foundational step for Large Language Models (LLMs) to process text. In new domains or languages, the inefficiency of the tokenizer will slow down the training and generation of LLM. The mismatch in vocabulary also hinders deep knowledge transfer between LLMs like token-level distillation. To mitigate this gap, we propose an efficient method named TokAlign to replace the vocabulary of LLM from the token co-occurrences view, and further transfer the token-level knowledge between models. It first aligns the source vocabulary to the target one by learning a one-to-one mapping matrix for token IDs. Model parameters, including embeddings, are rearranged and progressively fine-tuned for the new vocabulary. Our method significantly improves multilingual text compression rates and vocabulary initialization for LLMs, decreasing the perplexity from 3.4$\text{e}^2$ of strong baseline methods to 1.2$\text{e}^2$ after initialization. Experimental results on models across multiple parameter scales demonstrate the effectiveness and generalization of TokAlign, which costs as few as 5k steps to restore the performance of the vanilla model. After unifying vocabularies between LLMs, token-level distillation can remarkably boost (+4.4% than sentence-level distillation) the base model, costing only 235M tokens.
ViTSGMM: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels
Authors: Rui Yann, Xianglei Xing
Date: 2025-06-04 00:00:00
Proceeding: SSRN Electronic Journal 2025 3
Tasks: Semi-Supervised Image Classification

We present ViTSGMM, an image recognition network that leverages semi-supervised learning in a highly efficient manner. Existing works often rely on complex training techniques and architectures, while their generalization ability when dealing with extremely limited labeled data remains to be improved. To address these limitations, we construct a hierarchical mixture density classification decision mechanism by optimizing mutual information between feature representations and target classes, compressing redundant information while retaining crucial discriminative components. Experimental results demonstrate that our method achieves state-of-the-art performance on STL-10 and CIFAR-10/100 datasets when using negligible labeled samples. Notably, this paper also reveals a long-overlooked data leakage issue in the STL-10 dataset for semi-supervised learning tasks and removes duplicates to ensure the reliability of experimental results. Code available at https://github.com/Shu1L0n9/ViTSGMM.
UniCUE: Unified Recognition and Generation Framework for Chinese Cued Speech Video-to-Speech Generation
Authors: Jinting Wang, Shan Yang, Li Liu
Date: 2025-06-04 00:00:00
Tasks: cross-modal alignment, Lipreading, text-to-speech, Text to Speech

Cued Speech (CS) enhances lipreading through hand coding, providing precise speech perception support for the hearing-impaired. CS Video-to-Speech generation (CSV2S) task aims to convert the CS visual expressions (CS videos) of hearing-impaired individuals into comprehensible speech signals. Direct generation of speech from CS video (called single CSV2S) yields poor performance due to insufficient CS data. Current research mostly focuses on CS Recognition (CSR), which convert video content into linguistic text. Based on this, one straightforward way of CSV2S is to combine CSR with a Text-to-Speech system. This combined architecture relies on text as an intermediate medium for stepwise cross-modal alignment, which may lead to error propagation and temporal misalignment between speech and video dynamics. To address these challenges, we propose a novel approach that directly generates speech from CS videos without relying on intermediate text. Building upon this, we propose UniCUE, the first unified framework for CSV2S, whose core innovation lies in the integration of the CSR task that provides fine-grained visual-semantic information to facilitate speech generation from CS videos. More precisely, (1) a novel fine-grained semantic alignment pool to ensure precise mapping between visual features and speech contents; (2) a VisioPhonetic adapter to bridge cross-task representations, ensuring seamless compatibility between two distinct tasks (i.e., CSV2S and CSR); (3) a pose-aware visual processor is introduced to enhance fine-grained spatiotemporal correlations between lip and hand movements in CS video. Experiments on our new established Chinese CS dataset (14 cuers1: 8 hearing-impaired and 6 normal-hearing) show that our UniCUE significantly reduces Word Error Rate by 78.3% and improves lip-speech synchronization by 32% compared to the single CSV2S.
Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy to Enhance LLM Reasoning
Authors: Junqi Gao, Xiang Zou, YIng Ai, Dong Li, Yichen Niu, Biqing Qi, Jianxing Liu
Date: 2025-06-04 00:00:00
Tasks: Retrieval-augmented Generation

Graph Retrieval Augmented Generation (GraphRAG) effectively enhances external knowledge integration capabilities by explicitly modeling knowledge relationships, thereby improving the factual accuracy and generation quality of Large Language Models (LLMs) in specialized domains. However, existing methods suffer from two inherent limitations: 1) Inefficient Information Aggregation: They rely on a single agent and fixed iterative patterns, making it difficult to adaptively capture multi-level textual, structural, and degree information within graph data. 2) Rigid Reasoning Mechanism: They employ preset reasoning schemes, which cannot dynamically adjust reasoning depth nor achieve precise semantic correction. To overcome these limitations, we propose Graph Counselor, an GraphRAG method based on multi-agent collaboration. This method uses the Adaptive Graph Information Extraction Module (AGIEM), where Planning, Thought, and Execution Agents work together to precisely model complex graph structures and dynamically adjust information extraction strategies, addressing the challenges of multi-level dependency modeling and adaptive reasoning depth. Additionally, the Self-Reflection with Multiple Perspectives (SR) module improves the accuracy and semantic consistency of reasoning results through self-reflection and backward reasoning mechanisms. Experiments demonstrate that Graph Counselor outperforms existing methods in multiple graph reasoning tasks, exhibiting higher reasoning accuracy and generalization ability. Our code is available at https://github.com/gjq100/Graph-Counselor.git.
Do Large Language Models Know Folktales? A Case Study of Yokai in Japanese Folktales
Authors: Ayuto Tsutsumi, Yuu Jinnai
Date: 2025-06-04 00:00:00
Tasks: Multiple-choice

Although Large Language Models (LLMs) have demonstrated strong language understanding and generation abilities across various languages, their cultural knowledge is often limited to English-speaking communities, which can marginalize the cultures of non-English communities. To address the problem, evaluation of the cultural awareness of the LLMs and the methods to develop culturally aware LLMs have been investigated. In this study, we focus on evaluating knowledge of folktales, a key medium for conveying and circulating culture. In particular, we focus on Japanese folktales, specifically on knowledge of Yokai. Yokai are supernatural creatures originating from Japanese folktales that continue to be popular motifs in art and entertainment today. Yokai have long served as a medium for cultural expression, making them an ideal subject for assessing the cultural awareness of LLMs. We introduce YokaiEval, a benchmark dataset consisting of 809 multiple-choice questions (each with four options) designed to probe knowledge about yokai. We evaluate the performance of 31 Japanese and multilingual LLMs on this dataset. The results show that models trained with Japanese language resources achieve higher accuracy than English-centric models, with those that underwent continued pretraining in Japanese, particularly those based on Llama-3, performing especially well. The code and dataset are available at https://github.com/CyberAgentA ILab/YokaiEval.
ARB inactivation, ARGs and antibiotics degradation in hospital wastewater
Authors: Kornelia Stefaniak, Monika Harnisz, Magdalena Mecik, Ewa Korzeniewska
Date: 2025-06-04 00:00:00

Antibiotic resistance (AR) is one of the greatest public health challenges worldwide. Processes that allow the reduction of AR predictor of hospital wastewater has become crucial process that contributes to the protection of public health and the environment. The aim of this review article was to compare the effectiveness of various methods for treatment hospital wastewater in eliminating antibiotic-resistant bacteria (ARB) and degrading antibiotic resistance genes (ARGs) and antibiotics. A large number of studies dealing with wastewater treatment suggest that this topic is highly relevant and that new solutions are being developed to limit the spread of AR. Some wastewater treatment techniques have been in use for decades. Despite the negative effects of chlorine compounds, chlorination is still applied to eliminate ARB, ARGs, and drug metabolites. Ultraviolet (UV) radiation and ozonation have long been recognized for their treating properties. In the literature, advanced oxidation processes (AOPs) are increasingly often indicated as the most effective alternative to conventional treatment methods. Various methods for disinfecting hospital wastewater were reviewed and their environmental impact was analyzed in this article, and the results provide valuable insights for the further development of effective wastewater management strategies.