6 repositorios
Awesome GitHub RepositoriesSelf-Correction Loops
Recursive reasoning processes where models verify and correct their own internal logic before finalizing an answer.
Distinct from Multi-step Goal Execution: Specifically targets the recursive internal verification loop rather than autonomous external goal execution.
Explore 6 awesome GitHub repositories matching artificial intelligence & ml · Self-Correction Loops. Refine with filters or upvote what's useful.
Awesome Self-Correction Loops GitHub Repositories
Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
- richards199999/thinking-claude
richards199999/Thinking-Claude
17,061Ver en GitHubThinking-Claude is a browser extension and reasoning interface designed to compel large language models to perform deep inner monologues before delivering final responses. It functions as a thought process formatter that organizes internal AI reasoning into a structured format. The tool uses systematic reasoning prompts and instruction-based injection to force models through a multi-step verification process. This ensures the AI executes a recursive loop of reasoning and self-correction prior to emitting an answer. To manage this output, the project employs client-side response interception
Directs the AI to execute a recursive loop of reasoning and self-correction prior to delivering a response.
TypeScript - jiayi-pan/tinyzero
Jiayi-Pan/TinyZero
13,168Ver en GitHubTinyZero is a reinforcement learning framework and implementation designed to train language models to develop reasoning and self-verification abilities. It provides a training pipeline to optimize model performance on mathematical and logical tasks. The project serves as a minimal reproduction of the DeepSeek R1 architectural and training approach. It focuses on creating reasoning models that can solve structured problems through autonomous chain-of-thought discovery. The framework incorporates group relative policy optimization and reward-based self-correction to improve accuracy on logica
Provides self-correction loops that use reward signals to refine internal reasoning logic.
Python - sarwarbeing-ai/agentic_design_patterns
sarwarbeing-ai/Agentic_Design_Patterns
9,498Ver en GitHubThis project is a collection of architectural templates and design patterns for building autonomous AI agents. It provides a framework for transitioning from simple prompt-response loops to goal-oriented systems that utilize structural patterns to increase autonomy and improve the reliability of complex task completion. The framework focuses on reasoning orchestration, specifically through the implementation of reflection and self-correction cycles. It enables the coordination of specialized agents via task delegation and state sharing to solve complex problems. The architectural surface cov
Implements loops where models review and correct their own outputs against specific criteria.
Jupyter Notebook - trigaten/learn_prompting
trigaten/Learn_Prompting
4,709Ver en GitHubLearn_Prompting es un proyecto educativo centrado en la ingeniería de prompts, que proporciona los principios y técnicas necesarios para elaborar entradas efectivas y mejorar la calidad de las salidas de la IA generativa. El proyecto cubre estrategias avanzadas de prompting para mejorar el razonamiento, la fiabilidad y la calidad de la salida. Esto incluye técnicas para la descomposición de tareas, razonamiento de cadena de pensamiento (chain-of-thought) y el uso de guías few-shot y zero-shot. También aborda la seguridad del modelo mediante el estudio de prompt hacking, análisis de vulnerabilidades y auditorías de privacidad para prevenir fugas de datos sensibles. El alcance se extiende a la aplicación práctica de la IA generativa en diversos medios y flujos de trabajo, incluyendo generación de texto, creación de imágenes fotorrealistas y producción audiovisual. Además, cubre el desarrollo de agentes autónomos, programación asistida por IA y la automatización de flujos de trabajo empresariales para marketing y comunicaciones. El proyecto proporciona recursos para la optimización de modelos, evaluación y gestión de ciclos de vida de prompts dentro de un entorno de experimentación interactivo.
Implements internal critique and recursive reasoning loops where models verify and correct their own logic.
MDXchatgptchatgpt-apideep-learning - panaversity/learn-agentic-ai
panaversity/learn-agentic-ai
3,908Ver en GitHubThis project is an educational curriculum and architectural framework for building autonomous AI agents and multi-agent systems. It provides a structured learning path focused on the development of independent software components capable of planning, executing tasks, and utilizing external tools to achieve high-level goals. The framework emphasizes multi-agent system orchestration through distributed architectures where specialized agents collaborate using standardized communication protocols. It details specific design patterns such as dual-memory systems for maintaining short-term plans and
Integrates reflection and monitoring to detect failures and automatically replan or retry tasks.
Jupyter Notebooka2aagentic-aidapr - fareedkhan-dev/all-agentic-architectures
FareedKhan-dev/all-agentic-architectures
3,670Ver en GitHubThis project is a comprehensive framework for building, evaluating, and connecting autonomous agent systems. It provides a library of standardized architectural patterns for implementing complex agent workflows, including multi-agent orchestration, iterative reasoning, and memory management. By offering a unified interface for model providers, the framework allows for consistent agent execution across different artificial intelligence services. The framework distinguishes itself through a focus on rigorous benchmarking and deterministic control. It includes a suite of tools for evaluating age
Enables recursive refinement cycles where agents evaluate and correct their own outputs against quality criteria.
Jupyter Notebookagentic-aiai-agentslangchain
