pwc-archive/papers-with-abstracts huggingface dataset:
When Counterfactual Reasoning Fails: Chaos and Real-World Complexity
Authors: Yahya Aalaila, Gerrit Großmann, Sumantrak Mukherjee, jonas Wahl, Sebastian Vollmer
Date: 2025-03-31 00:00:00
Tasks: counterfactual, Counterfactual Reasoning, Decision Making, Fairness

Counterfactual reasoning, a cornerstone of human cognition and decision-making, is often seen as the 'holy grail' of causal learning, with applications ranging from interpreting machine learning models to promoting algorithmic fairness. While counterfactual reasoning has been extensively studied in contexts where the underlying causal model is well-defined, real-world causal modeling is often hindered by model and parameter uncertainty, observational noise, and chaotic behavior. The reliability of counterfactual analysis in such settings remains largely unexplored. In this work, we investigate the limitations of counterfactual reasoning within the framework of Structural Causal Models. Specifically, we empirically investigate \emph{counterfactual sequence estimation} and highlight cases where it becomes increasingly unreliable. We find that realistic assumptions, such as low degrees of model uncertainty or chaotic dynamics, can result in counterintuitive outcomes, including dramatic deviations between predicted and true counterfactual trajectories. This work urges caution when applying counterfactual reasoning in settings characterized by chaos and uncertainty. Furthermore, it raises the question of whether certain systems may pose fundamental limitations on the ability to answer counterfactual questions about their behavior.
Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics
Authors: Swarnava Bhattacharyya, Umapada Pal, Tapabrata Chakraborti
Date: 2025-03-31 00:00:00
Tasks: Diagnostic, Fairness, Lesion Classification, Skin Lesion Classification, Uncertainty Quantification

Deep learning based diagnostic AI systems based on medical images are starting to provide similar performance as human experts. However these data hungry complex systems are inherently black boxes and therefore slow to be adopted for high risk applications like healthcare. This problem of lack of transparency is exacerbated in the case of recent large foundation models, which are trained in a self supervised manner on millions of data points to provide robust generalisation across a range of downstream tasks, but the embeddings generated from them happen through a process that is not interpretable, and hence not easily trustable for clinical applications. To address this timely issue, we deploy conformal analysis to quantify the predictive uncertainty of a vision transformer (ViT) based foundation model across patient demographics with respect to sex, age and ethnicity for the tasks of skin lesion classification using several public benchmark datasets. The significant advantage of this method is that conformal analysis is method independent and it not only provides a coverage guarantee at population level but also provides an uncertainty score for each individual. We used a model-agnostic dynamic F1-score-based sampling during model training, which helped to stabilize the class imbalance and we investigate the effects on uncertainty quantification (UQ) with or without this bias mitigation step. Thus we show how this can be used as a fairness metric to evaluate the robustness of the feature embeddings of the foundation model (Google DermFoundation) and thus advance the trustworthiness and fairness of clinical AI.
Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute
Authors: Yingwei Ma, Yongbin Li, Yihong Dong, Xue Jiang, Rongyu Cao, Jue Chen, Fei Huang, Binhua Li
Date: 2025-03-31 00:00:00
Tasks: Fault localization

Recent advancements in software engineering agents have demonstrated promising capabilities in automating program improvements. However, their reliance on closed-source or resource-intensive models introduces significant deployment challenges in private environments, prompting a critical question: \textit{How can personally deployable open-source LLMs achieve comparable code reasoning performance?} To this end, we propose a unified Test-Time Compute scaling framework that leverages increased inference-time computation instead of larger models. Our framework incorporates two complementary strategies: internal TTC and external TTC. Internally, we introduce a \textit{development-contextualized trajectory synthesis} method leveraging real-world software repositories to bootstrap multi-stage reasoning processes, such as fault localization and patch generation. We further enhance trajectory quality through rejection sampling, rigorously evaluating trajectories along accuracy and complexity. Externally, we propose a novel \textit{development-process-based search} strategy guided by reward models and execution verification. This approach enables targeted computational allocation at critical development decision points, overcoming limitations of existing "end-point only" verification methods. Evaluations on SWE-bench Verified demonstrate our \textbf{32B model achieves a 46\% issue resolution rate}, surpassing significantly larger models such as DeepSeek R1 671B and OpenAI o1. Additionally, we provide the empirical validation of the test-time scaling phenomenon within SWE agents, revealing that \textbf{models dynamically allocate more tokens to increasingly challenging problems}, effectively enhancing reasoning capabilities. We publicly release all training data, models, and code to facilitate future research. https://github.com/yingweima2022/SWE-Reasoner
Adaptive Layer-skipping in Pre-trained LLMs
Authors: Xuan Luo, Weizhi Wang, Xifeng Yan
Date: 2025-03-31 00:00:00
Tasks: Text Generation

Various layer-skipping methods have been proposed to accelerate token generation in large language models (LLMs). However, they have overlooked a fundamental question: How do computational demands vary across the generation of different tokens? In this work, we introduce FlexiDepth, a method that dynamically adjusts the number of Transformer layers used in text generation. By incorporating a plug-in router and adapter, FlexiDepth enables adaptive layer-skipping in LLMs without modifying their original parameters. Introducing FlexiDepth to Llama-3-8B model achieves layer skipping of 8 layers out of 32, and meanwhile maintains the full 100\% benchmark performance. Experimental results with FlexiDepth demonstrate that computational demands in LLMs significantly vary based on token type. Specifically, generating repetitive tokens or fixed phrases requires fewer layers, whereas producing tokens involving computation or high uncertainty requires more layers. Interestingly, this adaptive allocation pattern aligns with human intuition. To advance research in this area, we open sourced FlexiDepth and a dataset documenting FlexiDepth's layer allocation patterns for future exploration.
MGD-SAM2: Multi-view Guided Detail-enhanced Segment Anything Model 2 for High-Resolution Class-agnostic Segmentation
Authors: Haoran Shen, Peixian Zhuang, Jiahao Kou, Yuxin Zeng, Haoying Xu, Jiangyun Li
Date: 2025-03-31 00:00:00

Segment Anything Models (SAMs), as vision foundation models, have demonstrated remarkable performance across various image analysis tasks. Despite their strong generalization capabilities, SAMs encounter challenges in fine-grained detail segmentation for high-resolution class-independent segmentation (HRCS), due to the limitations in the direct processing of high-resolution inputs and low-resolution mask predictions, and the reliance on accurate manual prompts. To address these limitations, we propose MGD-SAM2 which integrates SAM2 with multi-view feature interaction between a global image and local patches to achieve precise segmentation. MGD-SAM2 incorporates the pre-trained SAM2 with four novel modules: the Multi-view Perception Adapter (MPAdapter), the Multi-view Complementary Enhancement Module (MCEM), the Hierarchical Multi-view Interaction Module (HMIM), and the Detail Refinement Module (DRM). Specifically, we first introduce MPAdapter to adapt the SAM2 encoder for enhanced extraction of local details and global semantics in HRCS images. Then, MCEM and HMIM are proposed to further exploit local texture and global context by aggregating multi-view features within and across multi-scales. Finally, DRM is designed to generate gradually restored high-resolution mask predictions, compensating for the loss of fine-grained details resulting from directly upsampling the low-resolution prediction maps. Experimental results demonstrate the superior performance and strong generalization of our model on multiple high-resolution and normal-resolution datasets. Code will be available at https://github.com/sevenshr/MGD-SAM2.
DebFlow: Automating Agent Creation via Agent Debate
Authors: Jinwei Su, Yinghui Xia, Ronghua Shi, Jianhui Wang, Jianuo Huang, Yijin Wang, Tianyu Shi, Yang Jingsong, Lewei He
Date: 2025-03-31 00:00:00
Tasks: Math

Large language models (LLMs) have demonstrated strong potential and impressive performance in automating the generation and optimization of workflows. However, existing approaches are marked by limited reasoning capabilities, high computational demands, and significant resource requirements. To address these issues, we propose DebFlow, a framework that employs a debate mechanism to optimize workflows and integrates reflexion to improve based on previous experiences. We evaluated our method across six benchmark datasets, including HotpotQA, MATH, and ALFWorld. Our approach achieved a 3\% average performance improvement over the latest baselines, demonstrating its effectiveness in diverse problem domains. In particular, during training, our framework reduces resource consumption by 37\% compared to the state-of-the-art baselines. Additionally, we performed ablation studies. Removing the Debate component resulted in a 4\% performance drop across two benchmark datasets, significantly greater than the 2\% drop observed when the Reflection component was removed. These findings strongly demonstrate the critical role of Debate in enhancing framework performance, while also highlighting the auxiliary contribution of reflexion to overall optimization.
WinoWhat: A Parallel Corpus of Paraphrased WinoGrande Sentences with Common Sense Categorization
Authors: Ine Gevers, Victor De Marez, Luna De Bruyne, Walter Daelemans
Date: 2025-03-31 00:00:00
Tasks: Common Sense Reasoning, Memorization, Winogrande

In this study, we take a closer look at how Winograd schema challenges can be used to evaluate common sense reasoning in LLMs. Specifically, we evaluate generative models of different sizes on the popular WinoGrande benchmark. We release WinoWhat, a new corpus, in which each instance of the WinoGrande validation set is paraphrased. Additionally, we evaluate the performance on the challenge across five common sense knowledge categories, giving more fine-grained insights on what types of knowledge are more challenging for LLMs. Surprisingly, all models perform significantly worse on WinoWhat, implying that LLM reasoning capabilities are overestimated on WinoGrande. To verify whether this is an effect of benchmark memorization, we match benchmark instances to LLM trainingdata and create two test-suites. We observe that memorization has a minimal effect on model performance on WinoGrande.
WaveFormer: A 3D Transformer with Wavelet-Driven Feature Representation for Efficient Medical Image Segmentation
Authors: Md Mahfuz Al Hasan, Mahdi Zaman, Abdul Jawad, Alberto Santamaria-Pang, Ho Hin Lee, Ivan Tarapov, Kyle See, Md Shah Imran, Antika Roy, Yaser Pourmohammadi Fallah, Navid Asadizanjani, Reza Forghani
Date: 2025-03-31 00:00:00
Tasks: Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Semantic Segmentation

Transformer-based architectures have advanced medical image analysis by effectively modeling long-range dependencies, yet they often struggle in 3D settings due to substantial memory overhead and insufficient capture of fine-grained local features. We address these limitations with WaveFormer, a novel 3D-transformer that: i) leverages the fundamental frequency-domain properties of features for contextual representation, and ii) is inspired by the top-down mechanism of the human visual recognition system, making it a biologically motivated architecture. By employing discrete wavelet transformations (DWT) at multiple scales, WaveFormer preserves both global context and high-frequency details while replacing heavy upsampling layers with efficient wavelet-based summarization and reconstruction. This significantly reduces the number of parameters, which is critical for real-world deployment where computational resources and training times are constrained. Furthermore, the model is generic and easily adaptable to diverse applications. Evaluations on BraTS2023, FLARE2021, and KiTS2023 demonstrate performance on par with state-of-the-art methods while offering substantially lower computational complexity.
LANID: LLM-assisted New Intent Discovery
Authors: Lu Fan, Jiashu Pu, Rongsheng Zhang, Xiao-Ming Wu
Date: 2025-03-31 00:00:00
Tasks: Intent Discovery, Task-Oriented Dialogue Systems, Triplet

Task-oriented Dialogue Systems (TODS) often face the challenge of encountering new intents. New Intent Discovery (NID) is a crucial task that aims to identify these novel intents while maintaining the capability to recognize existing ones. Previous efforts to adapt TODS to new intents have struggled with inadequate semantic representation or have depended on external knowledge, which is often not scalable or flexible. Recently, Large Language Models (LLMs) have demonstrated strong zero-shot capabilities; however, their scale can be impractical for real-world applications that involve extensive queries. To address the limitations of existing NID methods by leveraging LLMs, we propose LANID, a framework that enhances the semantic representation of lightweight NID encoders with the guidance of LLMs. Specifically, LANID employs the $K$-nearest neighbors and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms to sample selective utterance pairs from the training set. It then queries an LLM to ascertain the relationships between these pairs. The data produced from this process is utilized to design a contrastive fine-tuning task, which is then used to train a small encoder with a contrastive triplet loss. Our experimental results demonstrate the efficacy of the proposed method across three distinct NID datasets, surpassing strong baselines in both unsupervised and semi-supervised settings. Our code is available at https://github.com/floatSDSDS/LANID.
Investigation of intelligent barbell squat coaching system based on computer vision and machine learning
Authors: Yinq-Rong Chern, Yuhao Lee, Hsiao-Ching Lin, Guan-Ting Chen, Ying-Hsien Chen, Fu-Sung Lin, Chih-Yao Chuang, Jenn-Jier James Lien, Chih-Hsien Huang
Date: 2025-03-31 00:00:00
Tasks: Diagnostic, feature selection

Purpose: Research has revealed that strength training can reduce the incidence of chronic diseases and physical deterioration at any age. Therefore, having a movement diagnostic system is crucial for training alone. Hence, this study developed an artificial intelligence and computer vision-based barbell squat coaching system with a real-time mode that immediately diagnoses the issue and provides feedback after each squat. In addition, a replay mode allows users to examine their previous squats and check their comments. Initially, four primary characteristics of the barbell squat were identified: body joint angles, dorsiflexion, the ratio of knee-to-hip movement, and barbell stability. Methods: We collect 8,151 squats from 77 participants, categorizing them as good squats and six issues. Then, we trained the diagnosis models with three machine-learning architectures. Furthermore, this research applied the SHapley Additive exPlanations (SHAP) method to enhance the accuracy of issue prediction and reduce the computation time by feature selection. Results: The F1 score of the six issues reached 86.86%, 69.01%, 77.42%, 90.74%, 95.83%, and 100%. Each squat diagnosis took less than 0.5 seconds. Finally, this study examined the efficacy of the proposed system with two groups of participants trained with and without the system. Subsequently, participants trained with the system exhibited substantial improvements in their squat technique, as assessed both by the system itself and by a professional weightlifting coach. Conclusion: This is a comprehensive study that integrates artificial intelligence, computer vision and multivariable processing technologies, aimed at building a real-time, user-friendly barbell squat feedback and training system.
KOFFVQA: An Objectively Evaluated Free-form VQA Benchmark for Large Vision-Language Models in the Korean Language
Authors: Yoonshik Kim, JaeYoon Jung
Date: 2025-03-31 00:00:00
Tasks: Form, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

The recent emergence of Large Vision-Language Models(VLMs) has resulted in a variety of different benchmarks for evaluating such models. Despite this, we observe that most existing evaluation methods suffer from the fact that they either require the model to choose from pre-determined responses, sacrificing open-endedness, or evaluate responses using a judge model, resulting in subjective and unreliable evaluation. In addition, we observe a lack of benchmarks for VLMs in the Korean language, which are necessary as a separate metric from more common English language benchmarks, as the performance of generative language models can differ significantly based on the language being used. Therefore, we present KOFFVQA, a general-purpose free-form visual question answering benchmark in the Korean language for the evaluation of VLMs. Our benchmark consists of 275 carefully crafted questions each paired with an image and grading criteria covering 10 different aspects of VLM performance. The grading criteria eliminate the problem of unreliability by allowing the judge model to grade each response based on a pre-determined set of rules. By defining the evaluation criteria in an objective manner, even a small open-source model can be used to evaluate models on our benchmark reliably. In addition to evaluating a large number of existing VLMs on our benchmark, we also experimentally verify that our method of using pre-existing grading criteria for evaluation is much more reliable than existing methods. Our evaluation code is available at https://github.com/maum-ai/KOFFVQA
Unimodal-driven Distillation in Multimodal Emotion Recognition with Dynamic Fusion
Authors: Jiagen Li, Rui Yu, Huihao Huang, Huaicheng Yan
Date: 2025-03-31 00:00:00
Tasks: Emotion Recognition, Knowledge Distillation, Mixture-of-Experts, Multimodal Emotion Recognition

Multimodal Emotion Recognition in Conversations (MERC) identifies emotional states across text, audio and video, which is essential for intelligent dialogue systems and opinion analysis. Existing methods emphasize heterogeneous modal fusion directly for cross-modal integration, but often suffer from disorientation in multimodal learning due to modal heterogeneity and lack of instructive guidance. In this work, we propose SUMMER, a novel heterogeneous multimodal integration framework leveraging Mixture of Experts with Hierarchical Cross-modal Fusion and Interactive Knowledge Distillation. Key components include a Sparse Dynamic Mixture of Experts (SDMoE) for capturing dynamic token-wise interactions, a Hierarchical Cross-Modal Fusion (HCMF) for effective fusion of heterogeneous modalities, and Interactive Knowledge Distillation (IKD), which uses a pre-trained unimodal teacher to guide multimodal fusion in latent and logit spaces. Experiments on IEMOCAP and MELD show SUMMER outperforms state-of-the-art methods, particularly in recognizing minority and semantically similar emotions.
GNN-Based Candidate Node Predictor for Influence Maximization in Temporal Graphs
Authors: Priyanka Gautam, Balasubramaniam Natarajan, Sai Munikoti, S M Ferdous, Mahantesh Halappanavar
Date: 2025-03-31 00:00:00
Tasks: Marketing

In an age where information spreads rapidly across social media, effectively identifying influential nodes in dynamic networks is critical. Traditional influence maximization strategies often fail to keep up with rapidly evolving relationships and structures, leading to missed opportunities and inefficiencies. To address this, we propose a novel learning-based approach integrating Graph Neural Networks (GNNs) with Bidirectional Long Short-Term Memory (BiLSTM) models. This hybrid framework captures both structural and temporal dynamics, enabling accurate prediction of candidate nodes for seed set selection. The bidirectional nature of BiLSTM allows our model to analyze patterns from both past and future network states, ensuring adaptability to changes over time. By dynamically adapting to graph evolution at each time snapshot, our approach improves seed set calculation efficiency, achieving an average of 90% accuracy in predicting potential seed nodes across diverse networks. This significantly reduces computational overhead by optimizing the number of nodes evaluated for seed selection. Our method is particularly effective in fields like viral marketing and social network analysis, where understanding temporal dynamics is crucial.
Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios
Authors: Jingzheng Li, Xianglong Liu, Shikui Wei, Zhijun Chen, Bing Li, Qing Guo, Xianqi Yang, Yanjun Pu, Jiakai Wang
Date: 2025-03-31 00:00:00
Tasks: Adversarial Attack, Autonomous Driving, Benchmarking

Autonomous driving has made significant progress in both academia and industry, including performance improvements in perception task and the development of end-to-end autonomous driving systems. However, the safety and robustness assessment of autonomous driving has not received sufficient attention. Current evaluations of autonomous driving are typically conducted in natural driving scenarios. However, many accidents often occur in edge cases, also known as safety-critical scenarios. These safety-critical scenarios are difficult to collect, and there is currently no clear definition of what constitutes a safety-critical scenario. In this work, we explore the safety and robustness of autonomous driving in safety-critical scenarios. First, we provide a definition of safety-critical scenarios, including static traffic scenarios such as adversarial attack scenarios and natural distribution shifts, as well as dynamic traffic scenarios such as accident scenarios. Then, we develop an autonomous driving safety testing platform to comprehensively evaluate autonomous driving systems, encompassing not only the assessment of perception modules but also system-level evaluations. Our work systematically constructs a safety verification process for autonomous driving, providing technical support for the industry to establish standardized test framework and reduce risks in real-world road deployment.