cs papers | Gist.Science

Nonparametric domain adaptation for multimodal connoisseurship of Buddhist statuary across dynasties (ChronoStyleNet2.0)

ChronoStyleNet 2.0 is a nonparametric, multimodal system designed to assist specialists in documenting, dating, and interpreting Buddhist statuary across Chinese dynasties by leveraging structured prompting and retrieval-augmented generation, demonstrating strong performance in stylistic analysis and dating while serving as a traceable aid rather than a replacement for expert judgment.

Zike YU, Jia Xing, Lin Zhao, Du Lei, Linfeng Chen, Yurui Han, Wei Ren2026-08-12

💻 computer science

Quantum Key Search Algorithms under Side-channel Attack

This paper proposes an improved quantum key search algorithm that leverages side-channel attack-induced error distributions to achieve a super-quadratic speedup over classical methods and outperforms existing quantum approaches like Glaser's, while also addressing input state preparation challenges through efficient Dicke state implementation.

Yunteng Yang, Jianhong Shi, Hailong Zhang, Hongwei Li, Xiangqun Fu, Yonghui Yang, Yubing Zhu, Yanyang Zhou2026-08-12

💻 computer science

Conditional Generation of Overlapping Mixed-type Wafer Bin Map Defects via Feature-wise Linear Modulation

This paper proposes the Wafer Conditional Generative Network (WCGN), a framework integrating Feature-wise Linear Modulation into a U-Net architecture to generate interpretable, pixel-level masks for overlapping mixed-type wafer bin map defects, thereby outperforming state-of-the-art methods in segmentation accuracy and supporting engineer-in-the-loop diagnosis in semiconductor manufacturing.

Jaejoon Choi, Younghoon Kim2026-08-12

💻 computer science

TCGA-Informed Digital Twins for Safe Offline Reinforcement Learning in Chemotherapy Dose Optimization

This paper presents a TCGA-informed digital twin framework that utilizes SafeCQL offline reinforcement learning to optimize chemotherapy dosing policies by explicitly balancing tumor control against physiological safety constraints within a transparent, preclinical simulation environment.

Haoxuan Song, Yongliang Jia2026-08-12

💻 computer science

Action-masked deep Q-learning for optimizing full-length and short-turn urban rail services

This paper proposes an action-masked deep Q-learning framework to optimize urban rail services by dynamically selecting between full-length and short-turn patterns, which significantly reduces passenger waiting times and increases service capacity compared to traditional fixed or threshold-based policies while ensuring operational feasibility.

Yibo Wang, Zhengfeng Ma, Rongjie Chen2026-08-12

💻 computer science

Traffic flow forecasting with explicit priors and prior-conditioned adaptive fusion

This paper proposes a traffic flow forecasting framework that explicitly separates dynamic observations from structural and periodic priors, integrating them via a novel Prior-Conditioned Adaptive Fusion (PCAF) mechanism to achieve state-of-the-art accuracy across multiple datasets.

Peibei Jiang, Xiang Gu, Yuxiang Guo, Jing Wen, Dongjie Wang2026-08-12

💻 computer science

Reliability-Aware Sensor Fusion via Bidirectional Diffusion for Robust Robot Odometry

This paper presents PUD–DIKF, a reliability-aware LiDAR–RGB-D–IMU odometry system that utilizes a Prediction Uncertainty Tensor derived from bidirectional diffusion to dynamically adapt Kalman filter covariances, thereby preventing degraded measurements from dominating state updates and achieving superior trajectory accuracy on multiple benchmarks.

Workagegn Tatek Asfu, Shoukun Wang, Long Zhenhai2026-08-12

💻 computer science

A Contrastive Multi-Decomposition Approach with Social-Guided Signals for Robust Recommender Systems

This paper proposes COMPASS, a robust recommender system that enhances performance under noisy and sparse data by integrating multi-resolution low-rank decomposition views with social-guided contrastive learning and adaptive optimization strategies.

Mohammad Aliabadi, Alireza Abdollahpouri, Parham Moradi2026-08-12

💻 computer science

Deployment protocol and base model jointly determine residual risk in rule-based clinical LLM supervision

This study demonstrates that the residual safety risk of rule-based clinical LLM supervision is jointly determined by the deployment protocol (specifically handoff availability) and the base model's failure characteristics, revealing a critical gap between automated rule compliance and actual clinical adequacy.

Kaiyuan Wu, Aditya Nagori, Rishikesan Kamaleswaran2026-08-12

💻 computer science

On the Convergence of Thompson Sampling to Nash Equilibria in Multi-Agent Models

This paper demonstrates and proves that Thompson Sampling, when combined with a simple imitation mechanism in a symmetric multi-agent Bertrand pricing game, reliably converges to the Nash equilibrium in a decentralized, model-free manner, offering a practical reinforcement learning approach for complex multi-agent environments.

Marco Gross, Elisa Letizia2026-08-12

← Previous Next →