HKUDS/AI-Researcher

Features

• Autonomous Research Frameworks - Orchestrates the full research lifecycle from literature review to manuscript generation using autonomous AI agents.
• Agentic Workflow Orchestration - Orchestrates multi-step research pipelines by chaining autonomous AI agents through literature review, hypothesis generation, and manuscript creation.
• End-to-End Pipelines - Orchestrates the full discovery pipeline from literature review through experimentation to manuscript generation.
• Scientific Discovery Pipelines - Orchestrates the complete scientific discovery pipeline from literature review and idea generation through algorithm design, implementation, validation, and manuscript creation.
• Research Automation Frameworks - Orchestrates autonomous research agents to automate the full scientific discovery pipeline from literature review to manuscript generation.
• Manuscript Generation - Synthesizes research motivations, experimental results, and algorithm designs into complete academic papers.
• Autonomous Research Agents - Deploys LLM-powered agents to independently identify research gaps, formulate hypotheses, and execute experimental workflows.
• Automated Algorithm Implementation - Transforms conceptual research ideas into functional code implementations with automated testing and iterative refinement.
• Automated Hypothesis Generation - Generates testable predictions based on identified gaps and prior knowledge to guide experimental design.
• Experimental Implementations - Implements research ideas as code and iteratively refines them based on experimental performance metrics.
• Automated Literature Reviewers - Collects and analyzes academic papers and code repositories to identify research gaps and relevant prior work.
• Literature Collectors - Collects and filters research materials from academic databases and code platforms, evaluating papers and datasets for relevance and quality.
• Literature Synthesizers - Collects, filters, and evaluates academic papers and code repositories to identify research gaps and relevant prior work.
• Research Orchestration - Manages and executes multi-step scientific discovery pipelines through a web-based interface with configurable environment settings.
• Scientific Discovery Acceleration - Orchestrates the full research lifecycle from literature review to manuscript generation using autonomous AI agents.
• Scientific Workflow Automators - Coordinates literature collection, algorithm implementation, performance validation, and manuscript creation in a closed-loop process.
• Modular Pipeline Architectures - Structures the research process into interchangeable stages for material collection, algorithm implementation, and validation.
• Gap Identifiers - Analyzes existing literature to pinpoint underexplored areas and open questions for new investigations.
• Closed-Loop Code Iteration - Repeatedly refines algorithm designs and implementations based on automated testing and performance metrics in a feedback loop.
• Agent Performance Benchmarks - Assesses agent performance on correctness, completeness, and quality using systematic benchmarking tools.
• Algorithmic Refinement Tools - Executes systematic experiments to evaluate algorithm performance, collects metrics, and iteratively optimizes implementations.
• Research Workflow Launchers - Provides a Gradio-based web UI for configuring environment variables and triggering end-to-end research workflows.
• Research Agent Systems - End-to-end automation from hypothesis generation to peer review.

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