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, …
This paper presents Janus-Q, a novel framework that uses hierarchical-gated reward modeling to train large language models for event-driven financial trading, achieving superior performance by directly mapping financial …
This comprehensive guide examines the complete lifecycle of code large language models, from pre-training and supervised fine-tuning to reinforcement learning and deployment as autonomous agents. The paper provides …
AutoTTS introduces an environment-driven framework for automatically discovering test-time scaling strategies for LLMs, shifting from manual heuristic design to automated controller synthesis through offline replay …
This paper introduces AutoTTS, an environment-driven framework that automatically discovers test-time scaling strategies for large language models, replacing manual heuristic design with automated strategy discovery. The …
A comprehensive architectural analysis of Claude Code’s agentic coding tool, examining its design principles, permission systems, context management, and extensibility mechanisms through source code analysis. The …
AgentFlow introduces a typed graph DSL and feedback-driven optimization loop for automatically synthesizing multi-agent harnesses that discover security vulnerabilities, achieving state-of-the-art results on …
LeWorldModel introduces a stable end-to-end method for learning latent world models from raw pixels using only two loss terms, achieving competitive planning performance while being 48× faster than foundation-model-based …
A comprehensive analysis of AI usage patterns based on 100 trillion tokens of real-world LLM traffic from OpenRouter, revealing the shift toward reasoning models and agentic inference workflows. The study examines how …
RustAssistant is a tool that leverages Large Language Models (LLMs) to automatically suggest fixes for Rust compilation errors, achieving an impressive 74% accuracy on real-world compilation errors through careful prompt …
This article explores how ontological engineering borrows concepts from other software disciplines like modeling, object-oriented design, and software architecture to build better knowledge-based systems. It demonstrates …
