This paper presents a multi-agent AI architecture that achieves 92.1% reliability by organizing specialized AI agents into teams with opposing roles and hierarchical oversight, similar to corporate organizational …
GEPA introduces a novel prompt optimization approach that uses natural language reflection and Pareto-based evolutionary search to optimize compound AI systems, achieving superior performance compared to reinforcement …
A randomized controlled study examining how AI assistance affects skill formation in software development, finding that while AI can improve productivity, it significantly impairs conceptual understanding, code reading, …
Simon Willison explores how AI coding agents have made writing code nearly free, while good code still requires significant effort and new engineering practices. The article discusses the shift from expensive code …
Andrej Karpathy discusses the emergence of ‘Claws’ as a new layer on top of LLM agents, providing orchestration, scheduling, and persistence capabilities while highlighting security concerns with current …
This research paper presents six principled design patterns for building AI agents with provable resistance to prompt injection attacks, demonstrating their practical applicability through ten case studies across diverse …
SWE-Lancer introduces a comprehensive benchmark of over 1,400 real freelance software engineering tasks from Upwork worth $1 million USD, evaluating frontier language models on both individual contributor coding tasks …
SpeCrawler is a comprehensive system that leverages large language models to automatically generate OpenAPI Specifications from diverse API documentation through a carefully crafted multi-stage pipeline. The system …
Google Threat Intelligence Group reports on the increasing integration of AI by state-sponsored threat actors for reconnaissance, social engineering, and malware development, including model extraction attacks and …
A comprehensive analysis of AI market trends showing accelerated revenue growth, improved operational efficiency, and the transformation of both AI-native and traditional companies in the current technology cycle.
This research paper presents a domain-specific AI content safety taxonomy for financial services and demonstrates that general-purpose guardrail systems fail to identify most domain-specific risks. The authors evaluate …
This paper presents an LLM-agent driven framework for dynamically discovering and organizing financial topics from quarterly earnings calls into a hierarchical ontology. The system enables analysts to track emerging …
This paper introduces FINTAGGING, the first comprehensive benchmark for evaluating large language models on XBRL tagging tasks, decomposing the complex process into financial numeric identification and concept linking …
