Search:
Match:
3 results
Research Paper#Artificial Intelligence in Surgery🔬 ResearchAnalyzed: Jan 3, 2026 15:51

AI for Automated Surgical Skill Assessment

Published:Dec 30, 2025 18:45
1 min read
ArXiv

Analysis

This paper presents a promising AI-driven framework for objectively evaluating surgical skill, specifically microanastomosis. The use of video transformers and object detection to analyze surgical videos addresses the limitations of subjective, expert-dependent assessment methods. The potential for standardized, data-driven training is particularly relevant for low- and middle-income countries.
Reference

“The system achieves 87.7% frame-level accuracy in action segmentation that increased to 93.62% with post-processing, and an average classification accuracy of 76% in replicating expert assessments across all skill aspects.”

PermalinkArXiv
Research Paper#6G, RAN Slicing, Agentic AI, LLM, HDM🔬 ResearchAnalyzed: Jan 3, 2026 16:04

Agentic AI for 6G RAN Slicing

Published:Dec 29, 2025 14:38
1 min read
ArXiv

Analysis

This paper introduces a novel Agentic AI framework for 6G RAN slicing, leveraging Hierarchical Decision Mamba (HDM) and a Large Language Model (LLM) to interpret operator intents and coordinate resource allocation. The integration of natural language understanding with coordinated decision-making is a key advancement over existing approaches. The paper's focus on improving throughput, cell-edge performance, and latency across different slices is highly relevant to the practical deployment of 6G networks.
Reference

“The proposed Agentic AI framework demonstrates consistent improvements across key performance indicators, including higher throughput, improved cell-edge performance, and reduced latency across different slices.”

PermalinkArXiv
Research Paper#Software Supply Chain Security, AI, LLM, Reinforcement Learning🔬 ResearchAnalyzed: Jan 3, 2026 18:45

Agentic AI for Proactive Software Supply Chain Security

Published:Dec 29, 2025 14:06
1 min read
ArXiv

Analysis

This paper addresses the critical and growing problem of software supply chain attacks by proposing an agentic AI system. It moves beyond traditional provenance and traceability by actively identifying and mitigating vulnerabilities during software production. The use of LLMs, RL, and multi-agent coordination, coupled with real-world CI/CD integration and blockchain-based auditing, suggests a novel and potentially effective approach to proactive security. The experimental validation against various attack types and comparison with baselines further strengthens the paper's significance.
Reference

“Experimental outcomes indicate better detection accuracy, shorter mitigation latency and reasonable build-time overhead than rule-based, provenance only and RL only baselines.”

PermalinkArXiv