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PreClinAI: A Multi-Parametric Simulation Engine for Dynamic Physiological Trajectories

Nabeela Javed · Aug 15, 2026

Abstract. A purely computational pipeline for preclinical drug evaluation would allow toxicity profiles and experimental protocols to be generated directly from molecular structures without premature reliance on live subjects. Standard in-silico models provide part of the solution, but the main ethical and financial benefits are lost if arbitrary in-vivo testing is still required to determine systemic physiological outcomes. We propose a solution to preclinical trial inefficiency using a multi-parametric digital twin framework. The system evaluates molecular features through a calibrated classifier to predict toxicity probabilities, then maps these risks onto simulated biological trajectories modulated by strain genetics, age, and baseline organ health. The resulting physiological simulation allows for precise statistical power analysis, generating the absolute minimum animal sample size required to achieve significance. To ensure interpretability, the architecture grounds its findings by mapping the predicted endpoints against a deterministic biological knowledge graph, forming a mechanistic explanation backed by literature retrieval. The framework requires minimal preliminary in-vivo data, allowing researchers to systematically reduce and refine animal testing models before physical trials commence.

Systems PharmacologyComputataional BiologyComputaional Toxicology

Signaling Competence in Global Remote Technical Hiring: An Open-Source Framework

Tanvi · Aug 15, 2026

Abstract: The old way of hiring people is not working well when it comes to finding good software engineers who can work from anywhere. This is because the world is changing and more people are working from home.When companies try to hire people from countries it is hard for them to know if the person is really good at their job. This is a problem because companies do not want to hire someone who is not very good. We think that the things people do online like the code they write and the projects they work on can show if they are really good at their job. We also think that how well someone can communicate with others even when they are not in the place is very important. We looked at a lot of data. Found out that the things people do online are a much better way to know if they are good at their job than the school they went to or the companies they used to work for. We also found out that when people work on open-source projects it is a sign that they are very good at their job.We came up with a way for companies to hire people that is based on what they can actually do, not just what they say they can do. This way is better because it is fair and it helps companies find the people for the job.In the end we think that this new way of hiring people will make it easier for companies to find software engineers and will help people get jobs that they arereally good, at. It will also help reduce the costs of hiring people and make sure that everyone has a chance of getting a job.

Signaling Competence in Global Remote Technical Hiring: An Open-Source Framework

Signalling Competence in Global Remote Technical Hiring: An Open-Source Framework

Anushka Pujari · Aug 15, 2026

Abstract: The old way of hiring people is not working well when it comes to finding good software engineers who can work from anywhere. This is because the world is changing and more people are working from home. When companies try to hire people from other countries, it is hard for them to know if the person is really good at their job. This is a problem because companies do not want to hire someone who is not very good. We think that the things people do online, like the code they write and the projects they work on, can show if they are really good at their job. We also think that how well someone can communicate with others, even when they are not in the place, is very important. We looked at a lot of data. Found out that the things people do online are a much better way to know if they are good at their job than the school they went to or the companies they used to work for. We also found out that when people work on open-source projects, it is a sign that they are very good at their job. We came up with a way for companies to hire people that is based on what they can actually do, not just what they say they can do. This way is better because it is fair and it helps companies find the people for the job. In the end, we think that this new way of hiring people will make it easier for companies to find software engineers and will help people get jobs that they are really good at. It will also help reduce the costs of hiring people and make sure that everyone has a chance of getting a job.

Signaling Competence in Global Remote Technical Hiring: An Open-Source Framework

Signaling Competence in Global Remote Technical Hiring: An Open-Source Framework

Tamheen Zehra · Aug 15, 2026

Abstract: The old way of hiring people is not working well when it comes to finding good software engineers who can work from anywhere. This is because the world is changing and more people are working from home.When companies try to hire people from countries it is hard for them to know if the person is really good at their job. This is a problem because companies do not want to hire someone who is not very good. We think that the things people do online like the code they write and the projects they work on can show if they are really good at their job. We also think that how well someone can communicate with others even when they are not in the place is very important. We looked at a lot of data. Found out that the things people do online are a much better way to know if they are good at their job than the school they went to or the companies they used to work for. We also found out that when people work on open-source projects it is a sign that they are very good at their job.We came up with a way for companies to hire people that is based on what they can actually do, not just what they say they can do. This way is better because it is fair and it helps companies find the people for the job.In the end we think that this new way of hiring people will make it easier for companies to find software engineers and will help people get jobs that they are really good, at. It will also help reduce the costs of hiring people and make sure that everyone has a chance of getting a job.

Signaling Competence in Global Remote Technical Hiring: An Open-Source Framework

The Necromancer: An Adversarial Audit Swarm for Vibe-Coded Web Applications

S. Sai Vijay Surya · Aug 14, 2026

Necromancer is a multi-agent tool that adversarially audits Flask and FastAPI apps for crash bugs, risky dependencies, and unfair treatment across demographic groups. Tested against three real third-party repos, it found genuine crash bugs in an unfamiliar FastAPI app — and, in the process, exposed and fixed a false-positive bug in its own dependency checker. The paper argues that this kind of external validation, including catching your own tool's mistakes, is what real testing looks like for software built quickly with AI assistance.

Software SecurityFairness in AIWeb DevelopmentMachine LearningSoftware Testing

Adaptive Multi-Agent UAV Path Planning: From Classical Optimization to Deep Reinforcement Learning

Hamed Raza · Aug 14, 2026

Abstract:Multi-agent UAV path planning in three-dimensional environ- ments with obstacles is a problem of fundamental importance for search-and-rescue, surveillance, and logistics operations. Existing classical optimization methods produce high-quality paths for static environments but fail when obstacles move or missions change mid-flight. We present a systematic progres- sion from a Grey Wolf Optimizer (GWO) baseline to a Deep Reinforcement Learning (DRL) framework, training both DQN and PPO agents in a custom OpenAI Gym environment for 3D UAV navigation. Our key finding: PPO converges 40% faster than DQN under sparse-reward conditions—the realistic setting where agents receive feedback only upon goal completion or collision. We benchmark all three methods on identical test environments, measuring convergence speed, path optimality, and computational cost. The results reveal a fundamental trade- off: classical methods require zero training but cannot adapt; DRL requires upfront training but enables real-time obstacle avoidance. PPO offers the best cost-quality ratio, making it the preferred choice for practical UAV swarm deployments.

Multi-Agent Systems (MAS)Unmanned Aerial Vehicles (UAVs / Drones)Deep Reinforcement Learning (DRL)Motion Planning & Trajectory Optimization

Holo-AI: Interactive 3D Holographic Avatars Driven by Real-time Emotional and Contextual AI

Krishan Yadav · Aug 13, 2026

Krishan Yadav | krishan4.developer@gmail.com | https://ravatar.com/ai-hologram/ Abstract The convergence of volumetric display technology, real-time computer vision, and large-scale generative models presents an opportunity to create interactive three-dimensional holographic avatars capable of perceiving and responding to human emotional states and environmental context. We present Holo-AI, a unified system architecture that integrates multi-modal emotion recognition, contextual reasoning, and real-time volumetric rendering into a single coherent framework. Our approach employs a transformer-based emotion inference engine operating at 30 Hz on fused audio-visual streams, a hierarchical contextual reasoning layer that maintains session-level state, and a differentiable volumetric renderer that produces light-field-consistent holographic output on commodity spatial displays. We demonstrate a proof-of-concept system achieving end-to-end latency of 127 ms (P95), emotional classification accuracy of 91.3% on the DAIC-WOZ benchmark, and real-time rendering at 45 fps on a single NVIDIA RTX 4090. We evaluate the system across telepresence, educational tutoring, and clinical therapeutic support scenarios, reporting subjective user satisfaction scores of 4.2/5.0. We further discuss the computational constraints of current holographic hardware, the ethical implications of real time emotional inference, and a roadmap toward full volumetric display integration.

Holo-AI: Interactive 3D Holographic Avatars

AI-Based Semiconductor Defect Detection Using Edge Computing and Computer Vision

SHARAN N · Aug 10, 2026

Abstract Semiconductor manufacturing demands extremely precise inspection because tiny defects can greatly impact the performance, reliability, and yield of chips. Traditional inspection methods often rely on rule-based image processing, manual checks, or centralized computing, which can struggle to handle the growing volume and complexity of high-resolution images. This paper introduces an AI-powered approach to detect defects in semiconductor wafers by combining computer vision, deep learning, and edge computing. Using a lightweight neural network running directly on edge devices, the system processes and analyzes inspection images locally. It can identify and classify various defect types like particles, scratches, pattern issues, cracks, bridging, and missing structures. By handling the data on-site instead of sending large images to a central server, the system reduces delays, bandwidth needs, and dependency on network reliability. Designed for near-real-time use in manufacturing, this approach aims to deliver fast, accurate defect detection while efficiently using computing resources. We evaluate the system’s performance with metrics such as accuracy, precision, recall, F1-score, and inference speed. Overall, this research shows how combining AI with edge computing offers a scalable and responsive solution to improve semiconductor defect inspection. Keywords: Semiconductor Manufacturing, Defect Detection, Artificial Intelligence, Computer Vision, Edge Computing, Deep Learning, CNN, Wafer Inspection, Machine Vision, Industrial AI.

AI-Based Semiconductor Defect Detection Using Edge Computing and Computer Vision

AI-Based Semiconductor Defect Detection Using Edge Computing and Computer Vision

SHARAN N · Aug 10, 2026

Sharan N, Sapthagiri NPS University, Bengaluru-57 Abstract Semiconductor manufacturing demands extremely precise inspection because tiny defects can greatly impact the performance, reliability, and yield of chips. Traditional inspection methods often rely on rule-based image processing, manual checks, or centralized computing, which can struggle to handle the growing volume and complexity of high-resolution images. This paper introduces an AI-powered approach to detect defects in semiconductor wafers by combining computer vision, deep learning, and edge computing. Using a lightweight neural network running directly on edge devices, the system processes and analyzes inspection images locally. It can identify and classify various defect types like particles, scratches, pattern issues, cracks, bridging, and missing structures. By handling the data on-site instead of sending large images to a central server, the system reduces delays, bandwidth needs, and dependency on network reliability. Designed for near-real-time use in manufacturing, this approach aims to deliver fast, accurate defect detection while efficiently using computing resources. We evaluate the system’s performance with metrics such as accuracy, precision, recall, F1-score, and inference speed. Overall, this research shows how combining AI with edge computing offers a scalable and responsive solution to improve semiconductor defect inspection. Keywords: Semiconductor Manufacturing, Defect Detection, Artificial Intelligence, Computer Vision, Edge Computing, Deep Learning, CNN, Wafer Inspection, Machine Vision, Industrial AI.

AI-Based Semiconductor Defect Detection Using Edge Computing and Computer Vision