Artificial Intelligence

2608 Submissions

[3] viXra:2608.0031 [pdf] submitted on 2026-08-08 23:37:15

Performance of AI Tools for Automated Literature Screening in Evidence Synthesis

Authors: Minghao Ruan, Junhao Fu
Comments: 10 Pages. (Note by viXra Admin: Please submit article written with AI assistance to ai.viXra.org)

Background Literature screening constitutes a critical component in evidence synthesis; however, it typically requires substantial time and human resources. Artificial intelligence (AI) has shown promise in this field, yet the accuracy and effectiveness of AI tools for literature screening remain uncertain. This study aims to evaluate the performance of several existing AI-powered automated tools for literature screening.Methods This diagnostic accuracy study employed a cohort to evaluate the performance of five AI tools—ChatGPT 4.0, Claude 3.5, Gemini 1.5, DeepSeek-V3, and RobotSearch—in literature screening. We selected a random sample of 1,000 publications from a well-established literature cohort, with 500 as randomized controlled trials (RCTs) group and 500 as others group. Diagnostic accuracy was measured using several metrics, including the false negative fraction (FNF), time used for screening, false positive fraction (FPF), and the redundancy number needed to screen.Results We reported the FNF for the RCTs group and the FPF for the others group. In the RCTs group, RobotSearch exhibited the lowest FNF at 6.4% (95% CI: 4.6% to 8.9%), whereas Gemini exhibited the highest at 13.0% (95% CI: 10.3% to 16.3%). In the others group, the FPF of the four large language models ranged from 2.8% (95% CI: 1.7% to 4.7%) to 3.8% (95% CI: 2.4% to 5.9%), both of which were significantly lower than RobotSearch's rate of 22.2% (95% CI: 18.8% to 26.1%). In terms of screening efficiency, the mean time used for screening per article was 1.3 s for ChatGPT, 6.0 s for Claude, 1.2 s for Gemini, and 2.6 s for DeepSeek.Conclusions The AI tools assessed in this study demonstrated commendable performance in literature screening; however, they are not yet suitable as standalone solutions. These tools can serve as effective auxiliary aids, and a hybrid approach that integrates human expertise with AI may enhance both the efficiency and accuracy of the literature screening process.
Category: Artificial Intelligence

[2] viXra:2608.0020 [pdf] submitted on 2026-08-06 06:49:38

Neural Networks with {-1,0,1} Weights Playing Atari Space Invaders (2) Trained by Genetic Algorithm

Authors: Hidehiko Okada
Comments: 8 Pages.

Discrete-valued neural networks have attracted increasing attention because they require less memory and lower computational cost than conventional floating-point neural networks. While discrete weights are often obtained by quantizing pretrained continuous-valued networks, such approaches are not applicable to reinforcement learning tasks that cannot be trained by gradient-based methods. This paper investigates the training of multilayer perceptrons (MLPs) with ternary connection weights {−1,0,1} using Genetic Algorithm (GA) for the Atari Space Invaders reinforcement learning task. The performance of ternary-weight networks is compared with that of binary-weight networks {−1,1} under identical network topologies and GA configurations. Experimental results show that GA configuration employing a smaller population size and a larger number of generations significantly outperforms a configuration with a larger population size and fewer generations when the total number of fitness evaluations is fixed. Furthermore, no statistically significant difference is observed between ternary and binary weight representations in terms of game performance. Although ternary weights exhibit slightly better median and worst-case performance, binary weights occasionally achieve higher best scores while requiring less memory. These findings suggest that binary-weight neural networks provide an attractive trade-off between performance and memory efficiency, making them an attractive representation for evolutionary reinforcement learning.
Category: Artificial Intelligence

[1] viXra:2608.0006 [pdf] submitted on 2026-08-02 00:34:14

D3F: Diffusion-Driven Dual-Stream Framework for Occluded Person Re-Identification

Authors: Xiaohao Xie, Wei Meng, Wenhua Jiao
Comments: 16 Pages.

Person Re-Identification (ReID) under severe occlusion remains a formidable challenge, primarily because existing discriminative models lack the capacity to infer missing topological structures, inevitably leading to fragmented feature representations and compromised metric spaces. To break this perceptual limitation, we propose a novel end-to-end Diffusion-Driven Dual-stream Framework ($text{D}^3text{F}$), which seamlessly integrates generative structural priors from Diffusion Transformers (DiT) into vision-language ReID. To overcome the high computational overhead and semantic gaps inherent in diffusion models, we first design a truncated prior extraction mechanism alongside a Cascaded Inverted Modality Shuffle Network (CIMSN) to achieve lightweight and deep cross-modal interaction. Furthermore, recognizing that divergent generative features can severely contaminate the strict distance metric space, we propose an Asymmetric Decoupled Feature Guidance (ADFG) strategy. ADFG strictly utilizes the fused generative prior as a contextual lens to update text prompts, while reverting to pure discriminative features for mask localization and final metric pooling. This restrained decoupling strategy strikes an optimal balance between occlusion-resistant inference and metric space purity. Extensive experiments demonstrate that the proposed $text{D}^3text{F}$ significantly outperforms existing methods, achieving state-of-the-art (SOTA) performance on both occluded and holistic ReID benchmark datasets.
Category: Artificial Intelligence