pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks
Authors: Joery A. de Vries, Jinke He, Mathijs M. de Weerdt, Matthijs T. J. Spaan
Date: 2025-05-24 00:00:00
Tasks: Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Variational Inference

Meta-reinforcement learning trains a single reinforcement learning agent on a distribution of tasks to quickly generalize to new tasks outside of the training set at test time. From a Bayesian perspective, one can interpret this as performing amortized variational inference on the posterior distribution over training tasks. Among the various meta-reinforcement learning approaches, a common method is to represent this distribution with a point-estimate using a recurrent neural network. We show how one can augment this point estimate to give full distributions through the Laplace approximation, either at the start of, during, or after learning, without modifying the base model architecture. With our approximation, we are able to estimate distribution statistics (e.g., the entropy) of non-Bayesian agents and observe that point-estimate based methods produce overconfident estimators while not satisfying consistency. Furthermore, when comparing our approach to full-distribution based learning of the task posterior, our method performs on par with variational baselines while having much fewer parameters.
Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks
Authors: Sirui Chen, Shuqin Ma, Shu Yu, Hanwang Zhang, Shengjie Zhao, Chaochao Lu
Date: 2025-05-26 00:00:00

Consciousness stands as one of the most profound and distinguishing features of the human mind, fundamentally shaping our understanding of existence and agency. As large language models (LLMs) develop at an unprecedented pace, questions concerning intelligence and consciousness have become increasingly significant. However, discourse on LLM consciousness remains largely unexplored territory. In this paper, we first clarify frequently conflated terminologies (e.g., LLM consciousness and LLM awareness). Then, we systematically organize and synthesize existing research on LLM consciousness from both theoretical and empirical perspectives. Furthermore, we highlight potential frontier risks that conscious LLMs might introduce. Finally, we discuss current challenges and outline future directions in this emerging field. The references discussed in this paper are organized at https://github.com/OpenCausaLab/Awesome-LLM-Consciousness.
ADGSyn: Dual-Stream Learning for Efficient Anticancer Drug Synergy Prediction
Authors: Yuxuan Nie, Yutong Song, Hong Peng
Date: 2025-05-25 00:00:00
Tasks: GPU

Drug combinations play a critical role in cancer therapy by significantly enhancing treatment efficacy and overcoming drug resistance. However, the combinatorial space of possible drug pairs grows exponentially, making experimental screening highly impractical. Therefore, developing efficient computational methods to predict promising drug combinations and guide experimental validation is of paramount importance. In this work, we propose ADGSyn, an innovative method for predicting drug synergy. The key components of our approach include: (1) shared projection matrices combined with attention mechanisms to enable cross-drug feature alignment; (2) automatic mixed precision (AMP)-optimized graph operations that reduce memory consumption by 40\% while accelerating training speed threefold; and (3) residual pathways stabilized by LayerNorm to ensure stable gradient propagation during training. Evaluated on the O'Neil dataset containing 13,243 drug--cell line combinations, ADGSyn demonstrates superior performance over eight baseline methods. Moreover, the framework supports full-batch processing of up to 256 molecular graphs on a single GPU, setting a new standard for efficiency in drug synergy prediction within the field of computational oncology.
HS-STAR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation
Authors: Feng Xiong, Hongling Xu, Yifei Wang, Runxi Cheng, Yong Wang, Xiangxiang Chu
Date: 2025-05-26 00:00:00
Tasks: Mathematical Reasoning

Self-taught reasoners (STaRs) enhance the mathematical reasoning abilities of large language models (LLMs) by leveraging self-generated responses for self-training. Recent studies have incorporated reward models to guide response selection or decoding, aiming to obtain higher-quality data. However, they typically allocate a uniform sampling budget across all problems, overlooking the varying utility of problems at different difficulty levels. In this work, we conduct an empirical study and find that problems near the boundary of the LLM's reasoning capability offer significantly greater learning utility than both easy and overly difficult ones. To identify and exploit such problems, we propose HS-STaR, a Hierarchical Sampling framework for Self-Taught Reasoners. Given a fixed sampling budget, HS-STaR first performs lightweight pre-sampling with a reward-guided difficulty estimation strategy to efficiently identify boundary-level problems. Subsequently, it dynamically reallocates the remaining budget toward these high-utility problems during a re-sampling phase, maximizing the generation of valuable training data. Extensive experiments across multiple reasoning benchmarks and backbone LLMs demonstrate that HS-STaR significantly outperforms other baselines without requiring additional sampling budget.
Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive Hypergraphs
Authors: Jiawen Chen, Qi Shao, Duxin Chen, Wenwu Yu
Date: 2025-05-26 00:00:00
Tasks: Computational Efficiency, Scheduling

Spatio-temporal prediction is a pivotal task with broad applications in traffic management, climate monitoring, energy scheduling, etc. However, existing methodologies often struggle to balance model expressiveness and computational efficiency, especially when scaling to large real-world datasets. To tackle these challenges, we propose STH-SepNet (Spatio-Temporal Hypergraph Separation Networks), a novel framework that decouples temporal and spatial modeling to enhance both efficiency and precision. Therein, the temporal dimension is modeled using lightweight large language models, which effectively capture low-rank temporal dynamics. Concurrently, the spatial dimension is addressed through an adaptive hypergraph neural network, which dynamically constructs hyperedges to model intricate, higher-order interactions. A carefully designed gating mechanism is integrated to seamlessly fuse temporal and spatial representations. By leveraging the fundamental principles of low-rank temporal dynamics and spatial interactions, STH-SepNet offers a pragmatic and scalable solution for spatio-temporal prediction in real-world applications. Extensive experiments on large-scale real-world datasets across multiple benchmarks demonstrate the effectiveness of STH-SepNet in boosting predictive performance while maintaining computational efficiency. This work may provide a promising lightweight framework for spatio-temporal prediction, aiming to reduce computational demands and while enhancing predictive performance. Our code is avaliable at https://github.com/SEU-WENJIA/ST-SepNet-Lightweight-LLMs-Meet-Adaptive-Hypergraphs.
Directly Forecasting Belief for Reinforcement Learning with Delays
Authors: Qingyuan Wu, Yuhui Wang, Simon Sinong Zhan, YiXuan Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Jürgen Schmidhuber, Chao Huang
Date: 2025-05-01 00:00:00
Tasks: D4RL, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) with delays is challenging as sensory perceptions lag behind the actual events: the RL agent needs to estimate the real state of its environment based on past observations. State-of-the-art (SOTA) methods typically employ recursive, step-by-step forecasting of states. This can cause the accumulation of compounding errors. To tackle this problem, our novel belief estimation method, named Directly Forecasting Belief Transformer (DFBT), directly forecasts states from observations without incrementally estimating intermediate states step-by-step. We theoretically demonstrate that DFBT greatly reduces compounding errors of existing recursively forecasting methods, yielding stronger performance guarantees. In experiments with D4RL offline datasets, DFBT reduces compounding errors with remarkable prediction accuracy. DFBT's capability to forecast state sequences also facilitates multi-step bootstrapping, thus greatly improving learning efficiency. On the MuJoCo benchmark, our DFBT-based method substantially outperforms SOTA baselines.
Multi-modal Atmospheric Sensing to Augment Wearable IMU-Based Hand Washing Detection
Authors: Robin Burchard, Kristof Van Laerhoven
Date: 2024-09-17 00:00:00
Tasks: Specificity

Hand washing is a crucial part of personal hygiene. Hand washing detection is a relevant topic for wearable sensing with applications in the medical and professional fields. Hand washing detection can be used to aid workers in complying with hygiene rules. Hand washing detection using body-worn IMU-based sensor systems has been shown to be a feasible approach, although, for some reported results, the specificity of the detection was low, leading to a high rate of false positives. In this work, we present a novel, open-source prototype device that additionally includes a humidity, temperature, and barometric sensor. We contribute a benchmark dataset of 10 participants and 43 hand-washing events and perform an evaluation of the sensors' benefits. Added to that, we outline the usefulness of the additional sensor in both the annotation pipeline and the machine learning models. By visual inspection, we show that especially the humidity sensor registers a strong increase in the relative humidity during a hand-washing activity. A machine learning analysis of our data shows that distinct features benefiting from such relative humidity patterns remain to be identified.
MASKSEARCH: A Universal Pre-Training Framework to Enhance Agentic Search Capability
Authors: Weiqi Wu, Xin Guan, Shen Huang, Yong Jiang, Pengjun Xie, Fei Huang, Jiuxin Cao, Hai Zhao, Jingren Zhou
Date: 2025-05-26 00:00:00
Tasks: Multi-hop Question Answering, Question Answering, Reinforcement Learning (RL), Retrieval

Retrieval-Augmented Language Models (RALMs) represent a classic paradigm where models enhance generative capabilities using external knowledge retrieved via a specialized module. Recent advancements in Agent techniques enable Large Language Models (LLMs) to autonomously utilize tools for retrieval, planning, and reasoning. While existing training-based methods show promise, their agentic abilities are limited by inherent characteristics of the task-specific data used during training. To further enhance the universal search capability of agents, we propose a novel pre-training framework, MASKSEARCH. In the pre-training stage, we introduce the Retrieval Augmented Mask Prediction (RAMP) task, where the model learns to leverage search tools to fill masked spans on a large number of pre-training data, thus acquiring universal retrieval and reasoning capabilities for LLMs. After that, the model is trained on downstream tasks to achieve further improvement. We apply both Supervised Fine-tuning (SFT) and Reinforcement Learning (RL) for training. For SFT, we combine agent-based and distillation-based methods to generate training data, starting with a multi-agent system consisting of a planner, rewriter, observer, and followed by a self-evolving teacher model. While for RL, we employ DAPO as the training framework and adopt a hybrid reward system consisting of answer rewards and format rewards. Additionally, we introduce a curriculum learning approach that allows the model to learn progressively from easier to more challenging instances based on the number of masked spans. We evaluate the effectiveness of our framework in the scenario of open-domain multi-hop question answering. Through extensive experiments, we demonstrate that MASKSEARCH significantly enhances the performance of LLM-based search agents on both in-domain and out-of-domain downstream tasks.
OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation
Authors: Shenghai Yuan, Xianyi He, Yufan Deng, Yang Ye, Jinfa Huang, Bin Lin, Chongyang Ma, Jiebo Luo, Li Yuan
Date: 2025-05-26 00:00:00
Tasks: Human-Domain Subject-to-Video, Open-Domain Subject-to-Video, Single-Domain Subject-to-Video, Video Generation

Subject-to-Video (S2V) generation aims to create videos that faithfully incorporate reference content, providing enhanced flexibility in the production of videos. To establish the infrastructure for S2V generation, we propose OpenS2V-Nexus, consisting of (i) OpenS2V-Eval, a fine-grained benchmark, and (ii) OpenS2V-5M, a million-scale dataset. In contrast to existing S2V benchmarks inherited from VBench that focus on global and coarse-grained assessment of generated videos, OpenS2V-Eval focuses on the model's ability to generate subject-consistent videos with natural subject appearance and identity fidelity. For these purposes, OpenS2V-Eval introduces 180 prompts from seven major categories of S2V, which incorporate both real and synthetic test data. Furthermore, to accurately align human preferences with S2V benchmarks, we propose three automatic metrics, NexusScore, NaturalScore and GmeScore, to separately quantify subject consistency, naturalness, and text relevance in generated videos. Building on this, we conduct a comprehensive evaluation of 16 representative S2V models, highlighting their strengths and weaknesses across different content. Moreover, we create the first open-source large-scale S2V generation dataset OpenS2V-5M, which consists of five million high-quality 720P subject-text-video triples. Specifically, we ensure subject-information diversity in our dataset by (1) segmenting subjects and building pairing information via cross-video associations and (2) prompting GPT-Image-1 on raw frames to synthesize multi-view representations. Through OpenS2V-Nexus, we deliver a robust infrastructure to accelerate future S2V generation research.
Is Architectural Complexity Overrated? Competitive and Interpretable Knowledge Graph Completion with RelatE
Authors: Abhijit Chakraborty, Chahana Dahal, Ashutosh Balasubramaniam, Tejas Anvekar, Vivek Gupta
Date: 2025-05-25 00:00:00
Tasks: GPU, Knowledge Graph Completion

We revisit the efficacy of simple, real-valued embedding models for knowledge graph completion and introduce RelatE, an interpretable and modular method that efficiently integrates dual representations for entities and relations. RelatE employs a real-valued phase-modulus decomposition, leveraging sinusoidal phase alignments to encode relational patterns such as symmetry, inversion, and composition. In contrast to recent approaches based on complex-valued embeddings or deep neural architectures, RelatE preserves architectural simplicity while achieving competitive or superior performance on standard benchmarks. Empirically, RelatE outperforms prior methods across several datasets: on YAGO3-10, it achieves an MRR of 0.521 and Hit@10 of 0.680, surpassing all baselines. Additionally, RelatE offers significant efficiency gains, reducing training time by 24%, inference latency by 31%, and peak GPU memory usage by 22% compared to RotatE. Perturbation studies demonstrate improved robustness, with MRR degradation reduced by up to 61% relative to TransE and by up to 19% compared to RotatE under structural edits such as edge removals and relation swaps. Formal analysis further establishes the model's full expressiveness and its capacity to represent essential first-order logical inference patterns. These results position RelatE as a scalable and interpretable alternative to more complex architectures for knowledge graph completion.
Smart Waste Management System for Makkah City using Artificial Intelligence and Internet of Things
Authors: Rawabi S. Al Qurashi, Maram M. Almnjomi, Teef L. Alghamdi, Amjad H. Almalki, Shahad S. Alharthi, Shahad M. althobuti, Alanoud S. Alharthi, Maha A. Thafar
Date: 2025-05-25 00:00:00
Tasks: Management

Waste management is a critical global issue with significant environmental and public health implications. It has become more destructive during large-scale events such as the annual pilgrimage to Makkah, Saudi Arabia, one of the world's largest religious gatherings. This event's popularity has attracted millions worldwide, leading to significant and un-predictable accumulation of waste. Such a tremendous number of visitors leads to in-creased waste management issues at the Grand Mosque and other holy sites, highlighting the need for an effective solution other than traditional methods based on rigid collection schedules. To address this challenge, this research proposed an innovative solution that is context-specific and tailored to the unique requirements of pilgrimage season: a Smart Waste Management System, called TUHR, that utilizes the Internet of Things and Artificial Intelligence. This system encompasses ultrasonic sensors that monitor waste levels in each container at the performance sites. Once the container reaches full capacity, the sensor communicates with the microcontroller, which alerts the relevant authorities. Moreover, our system can detect harmful substances such as gas from the gas detector sensor. Such a proactive and dynamic approach promises to mitigate the environmental and health risks associated with waste accumulation and enhance the cleanliness of these sites. It also delivers economic benefits by reducing unnecessary gasoline consumption and optimizing waste management resources. Importantly, this research aligns with the principles of smart cities and exemplifies the innovative, sustainable, and health-conscious approach that Saudi Arabia is implementing as part of its Vision 2030 initiative.
A Smart Healthcare System for Monkeypox Skin Lesion Detection and Tracking
Authors: Huda Alghoraibi, Nuha Alqurashi, Sarah Alotaibi, Renad Alkhudaydi, Bdoor Aldajani, Lubna Alqurashi, Jood Batweel, Maha A. Thafar
Date: 2025-05-25 00:00:00
Tasks: Binary Classification, Diagnostic, Lesion Detection, Transfer Learning

Monkeypox is a viral disease characterized by distinctive skin lesions and has been reported in many countries. The recent global outbreak has emphasized the urgent need for scalable, accessible, and accurate diagnostic solutions to support public health responses. In this study, we developed ITMAINN, an intelligent, AI-driven healthcare system specifically designed to detect Monkeypox from skin lesion images using advanced deep learning techniques. Our system consists of three main components. First, we trained and evaluated several pretrained models using transfer learning on publicly available skin lesion datasets to identify the most effective models. For binary classification (Monkeypox vs. non-Monkeypox), the Vision Transformer, MobileViT, Transformer-in-Transformer, and VGG16 achieved the highest performance, each with an accuracy and F1-score of 97.8%. For multiclass classification, which contains images of patients with Monkeypox and five other classes (chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy), ResNetViT and ViT Hybrid models achieved 92% accuracy, with F1 scores of 92.24% and 92.19%, respectively. The best-performing and most lightweight model, MobileViT, was deployed within the mobile application. The second component is a cross-platform smartphone application that enables users to detect Monkeypox through image analysis, track symptoms, and receive recommendations for nearby healthcare centers based on their location. The third component is a real-time monitoring dashboard designed for health authorities to support them in tracking cases, analyzing symptom trends, guiding public health interventions, and taking proactive measures. This system is fundamental in developing responsive healthcare infrastructure within smart cities. Our solution, ITMAINN, is part of revolutionizing public health management.
Accelerating Prefilling for Long-Context LLMs via Sparse Pattern Sharing
Authors: Dan Peng, Zhihui Fu, Zewen Ye, Zhuoran Song, Jun Wang
Date: 2025-05-26 00:00:00

Sparse attention methods exploit the inherent sparsity in attention to speed up the prefilling phase of long-context inference, mitigating the quadratic complexity of full attention computation. While existing sparse attention methods rely on predefined patterns or inaccurate estimations to approximate attention behavior, they often fail to fully capture the true dynamics of attention, resulting in reduced efficiency and compromised accuracy. Instead, we propose a highly accurate sparse attention mechanism that shares similar yet precise attention patterns across heads, enabling a more realistic capture of the dynamic behavior of attention. Our approach is grounded in two key observations: (1) attention patterns demonstrate strong inter-head similarity, and (2) this similarity remains remarkably consistent across diverse inputs. By strategically sharing computed accurate patterns across attention heads, our method effectively captures actual patterns while requiring full attention computation for only a small subset of heads. Comprehensive evaluations demonstrate that our approach achieves superior or comparable speedup relative to state-of-the-art methods while delivering the best overall accuracy.
A novel prediction based portfolio optimization model using deep learning
Authors: Yilin Ma, Weizhong Wang, Qianting Ma
Date: 2023-03-01 00:00:00
Proceeding: Computers & Industrial Engineering 2023 3
Tasks: Portfolio Optimization, Stock Prediction

Portfolio optimization is an important part of portfolio management. It realizes the trade-off between
maximizing expected return and minimizing risk. A better portfolio optimization model helps investors achieve
higher expected returns under the same risk level. This paper proposes a novel prediction based portfolio
optimization model. This model uses autoencoder (AE) for feature extraction and long short term memory
(LSTM) network to predict stock return, then predicted and historical returns are utilized to build a portfolio
optimization model by advancing worst-case omega model. In order to show the effect of AE, the LSTM network
without any feature extraction methods is used as a benchmark in stock prediction. Also, an equally weighted
portfolio is considered as a comparison to reveal the advantage of the worst-case omega model. Empirical
results show that the proposed model significantly outperforms the equally weighted portfolio, and a high
risk–return preference is more suitable to this model. In addition, even after deducting transaction fees, this
model still achieves a satisfying return and performs better than the state-of-art prediction based portfolio
optimization models. Thus, this paper recommends applying this model in practical investment.