This paper introduces SDOF, a framework that treats multi-agent execution as a constrained state machine to enforce real business process constraints. It incorporates an RLHF-trained intent router and a state-aware dispatcher, outperforming GPT-4o on an adversarial routing benchmark in a recruitment system.
LlamaFactory is introduced as a unified and efficient framework designed for fine-tuning over 100 different language models. It aims to streamline and optimize the process of adapting a diverse range of large language models.
This practical guide details the process of setting up Large Language Models (LLMs) locally, aiming for cost savings and data privacy. It covers hardware requirements, compares frameworks like llama.cpp and Ollama, and provides a step-by-step installation process.
This article discusses the application of a framework that aligns AI autonomy with business risk and competitive differentiation. The author used AI Gateway cost controls as an example to illustrate how a single feature can touch various strategic quadrants.
This empirical study evaluates 22 agentic frameworks across three reasoning benchmarks (BBH, GSM8K, ARC) to compare their performance, efficiency, and practical suitability. Results indicate that 19 frameworks completed all tasks, with 12 demonstrating stable performance at 74.6-75.9% accuracy, 4-6 seconds execution time, and 0.14-0.18 cents per task cost.
O título sugere uma pesquisa sobre um framework neuro-simbólico de memória dupla para agentes LLM, visando alinhar progresso e viabilidade em tarefas de longo horizonte. Ele aborda a melhoria da capacidade de agentes de IA para planejar e executar ações complexas ao longo do tempo.
This paper introduces OpsLLM, an end-to-end framework for building large language models (LLMs) specifically for software operations. It addresses challenges like low-quality data and fragmented knowledge, detailing a workflow that includes data curation, supervised fine-tuning, and a domain process reward model.
Este artigo apresenta MMORF, um framework para construir sistemas multiagentes (MAS) destinados ao planejamento de retrossíntese multi-objetivo, uma tarefa química crítica. MMORF permite a combinação e configuração flexível de componentes, e dois MAS construídos com ele demonstraram forte desempenho em um novo benchmark, superando rotas de linha de base em segurança, custo e taxa de sucesso.
TABQAWORLD é um novo framework para otimizar o raciocínio multimodal em perguntas e respostas sobre tabelas multi-turn. Ele aborda erros de representação e altos custos de inferência ao empregar uma política de seleção multimodal que alterna dinamicamente entre representações visuais e textuais para maximizar a confiabilidade do estado da tabela.
The author initially used "setup" for their AI agent configurations, later discovering "harness" from an arXiv paper. This term is a more accurate description for integrating various AI agent components and is treated as a first-class research object.
This article explains AI agents, detailing how they surpass chatbots by thinking, planning, and collaborating autonomously to solve complex problems. It covers five types, components, frameworks like ReAct and ReWOO, multi-agent systems, and guardrails.
The Model Context Protocol (MCP) standardizes AI systems' access to external resources, solving integration issues. By 2026, frameworks like LangChain, CrewAI, OpenAI Agents SDK, and Microsoft Semantic Kernel will lead the MCP ecosystem, reducing hallucinations by providing real-time context.
Multi-agent AI systems involve specialized AI agents collaborating to handle complex tasks, dividing work across roles like research or code review. They are used in production for various workflows, including software development and content creation, with orchestration provided by frameworks like OpenClaw and CrewAI.
The AI Index 2026 reveals a significant shift in AI development, with 68% of developers prioritizing efficiency over raw performance due to staggering cost implications. The report also highlights the rise of new frameworks like MosaicML, though PyTorch and TensorFlow still dominate the market.
The article clarifies that MCP and Agent Skills are fundamentally different, despite a common misunderstanding framing them as rivals. It highlights that Skills are essentially on-demand prompts, differentiating them from MCP.