Search:
Match:
2 results
Safety#Code Generation🔬 ResearchAnalyzed: Jan 10, 2026 13:24

Assessing the Security of AI-Generated Code: A Vulnerability Benchmark

Published:Dec 2, 2025 22:11
1 min read
ArXiv

Analysis

This ArXiv paper investigates a critical aspect of AI-driven software development: the security of code generated by AI agents. Benchmarking vulnerabilities in real-world tasks is crucial for understanding and mitigating potential risks associated with this emerging technology.
Reference

“The research focuses on benchmarking the vulnerability of code generated by AI agents in real-world tasks.”

PermalinkArXiv
Research#LLM Planning🔬 ResearchAnalyzed: Jan 10, 2026 14:12

Limitations of Internal Planning in Large Language Models Explored

Published:Nov 26, 2025 17:08
1 min read
ArXiv

Analysis

This ArXiv paper likely delves into the inherent constraints of how Large Language Models (LLMs) plan and execute tasks internally, which is crucial for advancing LLM capabilities. The research likely identifies the specific architectural or algorithmic limitations that restrict the models' planning abilities, influencing their task success.
Reference

“The paper likely analyzes the internal planning mechanisms of LLMs.”

PermalinkArXiv