6 dépôts
Structured approaches for identifying critical scientific questions and proposing solutions.
Distinct from Project Ideas: Candidates are project prompts or research agents; none provide a structured methodology for idea generation.
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AutoResearchClaw is an agentic system designed to automate the scientific research process. It functions as an autonomous research agent and workflow automator that manages the entire lifecycle of a project, from initial hypothesis generation and literature review to experimental execution and the production of LaTeX-formatted academic papers. The system distinguishes itself through a multi-agent research pipeline that utilizes structured debates for hypothesis refinement and peer review. It employs a branch-and-merge architecture to explore parallel research directions and integrates human-i
Collaboratively brainstorms and evaluates initial ideas to sharpen the research direction.
This project is an academic research framework and PhD mentorship roadmap designed to guide the transition from basic technical concepts to independent scientific research. It serves as a research workflow guide and project management system for identifying scientific problems, designing technical solutions, and executing experiments for academic publication. The system provides a structured methodology for translating long-term scientific objectives into actionable roadmaps, publications, and technical milestones. It includes a scientific writing guide and a set of presentation toolkits cont
Offers a structured approach to identifying critical scientific questions and proposing effective technical solutions.
AI-Scientist-v2 is an autonomous research agent designed to conduct scientific discovery through an agentic framework. It specializes in generating hypotheses, executing machine learning experiments, and drafting structured academic manuscripts. The system utilizes an agentic tree search to explore branching research paths and hypotheses. This process includes automated code synthesis and iterative debugging of Python scripts to perform data analysis and machine learning trials. The framework integrates tool-based hypothesis verification against academic databases and maintains state-based m
Uses AI-driven processes to automatically identify research gaps and propose novel scientific hypotheses.
Ce projet est une bibliothèque de prompts structurés conçus pour aider à la recherche universitaire et à la rédaction académique en utilisant des grands modèles de langage. Il fournit un ensemble de modèles et de frameworks pédagogiques pour guider le processus d'écriture à travers diverses étapes académiques. La collection inclut des boîtes à outils spécialisées pour gérer les citations académiques et convertir les bibliographies entre différents styles savants. Elle propose un framework de méthodologie de recherche pour concevoir des objectifs d'étude et des plans expérimentaux, aux côtés d'un framework dédié à la revue de littérature pour synthétiser les thèmes issus de textes académiques et identifier les lacunes de recherche. L'ensemble de prompts couvre un large éventail de fonctionnalités, incluant la planification de la recherche, la rédaction de sections spécifiques de documents et l'affinage du ton académique formel. Il inclut également des instructions pour la communication de la recherche, transformant des résultats techniques complexes en communiqués de presse et en contenu destiné au public.
Produces brainstormed topics and structured plans to initiate new academic projects.
AI-Researcher is an LLM research automation framework and scientific workflow orchestrator designed to automate the end-to-end discovery process. It employs autonomous AI research agents to identify research gaps, formulate hypotheses, and execute scientific discovery workflows independently. The system integrates an automated literature review tool for gathering and analyzing academic papers and code repositories with an AI-driven manuscript generator that synthesizes research motivations and experimental results into full-length academic papers. The framework covers a modular research pipe
Generates testable predictions based on identified gaps and prior knowledge to guide experimental design.
This project is an LLM research orchestrator and autonomous AI agent framework designed to automate the scientific lifecycle. It functions as an end-to-end research pipeline and model training toolkit, managing everything from initial literature reviews and hypothesis testing to the final drafting of academic papers. The system is distinguished by its ability to convert unstructured academic PDFs into machine-executable knowledge layers, allowing agents to reproduce and extend research findings. It employs a two-loop orchestration architecture and a specialized research engineering skill libr
Generates a volume of structured research directions and boundaries for human review.