Every paper checked — nothing hidden
All papers
Every paper checked in the week of 19–25 Oct 2026 · AI safety and reliability in deployment — nothing is hidden. Brief papers first.
All9In brief0More (on-topic)0Not relevant6Not assessed0Older7Could not confirm0Blogs with no paper3All blogs4
| Paper | Topic | Relevance | Feedback |
|---|---|---|---|
| garak: A Framework for Security Probing Large Language Models Presents an open-source security probing framework to evaluate language models against adversarial attacks and identify vulnerabilities. Ranked #765New testPreprint7/10 suggestedOlder paper (2024) Older paper (2024) — discussed this week | AI safety and reliability in deployment via blog · 19 Oct, 06:19 | On-topic Introduces a red-teaming framework for probing vulnerabilities and adversarial security weaknesses in LLMs. | |
| Trustworthy Agentic AI Pipelines: Human-in-the-Loop Oversight Architectures for Secure Enterprise Deployment Proposes a seven-layer architecture and runtime control plane to enforce human oversight across planning, retrieval, and execution in AI agents. Ranked #769New way to see a problemPeer-reviewed7/10 suggestedOlder paper (2026) Older paper (2026) — discussed this week | AI safety and reliability in deployment via ieee · 19 Oct, 06:19 | On-topic Introduces a human-in-the-loop oversight architecture to mitigate agentic AI security risks and ensure auditability. | |
| SHIELD: Security against Harmful Prompt Injection Evaluation and Language Detection Leveraging Ensemble Approach Detects prompt injection attacks using a hybrid pipeline that combines supervised models, zero-shot classification, and rule-based heuristics. Ranked #777Peer-reviewed0/10 suggestedOlder paper (2025) Older paper (2025) — discussed this week | AI safety and reliability in deployment via ieee · 19 Oct, 06:19 | On-topic Proposes an ensemble detection pipeline and benchmark for harmful prompt injection attacks against LLMs. | |
| Hybrid AI and Computer Vision Framework for PCB/SMD Defect Detection with GUI-Enabled IoT Deployment Combines YOLO architectures, attention mechanisms, and classical computer vision features to detect defects on printed circuit boards. Peer-reviewedOlder paper (2026) Older paper (2026) — discussed this week | AI safety and reliability in deployment via ieee · 19 Oct, 06:19 | Not relevant Presents industrial defect detection for PCBs, not AI safety, guardrails, or alignment. | |
| WIP: Active Learning Through Prompt Engineering and Agentic AI Simulation-A Pilot Project in Computer Networks Education Simulates classroom interactions using multiple language model agents configured to support student learning in computer networks education. Peer-reviewedOlder paper (2024) Older paper (2024) — discussed this week | AI safety and reliability in deployment via ieee · 19 Oct, 06:19 | Not relevant Covers classroom simulation for education, not AI safety, jailbreaks, or deployment guardrails. | |
| Landscape and Taxonomy of Prompt Engineering Patterns in Software Engineering Surveys and categorizes prompt engineering patterns used to improve large language model performance in software engineering tasks. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week | AI safety and reliability in deployment via ieee · 19 Oct, 06:19 | Not relevant Surveys general prompt engineering patterns for software engineering, not safety or guardrails. | |
| Safety-Aware Imitation Learning via MPC-Guided Disturbance Injection Injects model-predictive control disturbances into expert robot demonstrations to train imitation learning policies that recover safely from errors. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week | AI safety and reliability in deployment via ieee · 19 Oct, 06:19 | Not relevant Addresses physical robotic control safety rather than AI system safety and reliability deployment. |
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