Overview

This repository offers a sophisticated, AI-powered toolkit designed to augment the academic research and writing process. It guides researchers through the entire lifecycle, from initial exploration and deep research to meticulous writing, rigorous review, and final publication. The suite emphasizes a "human-in-the-loop" approach, positioning AI as an indispensable copilot rather than an autonomous agent, thereby mitigating common pitfalls of full automation.

Key Features

• Comprehensive Pipeline: Covers research, writing, review, and revision stages with specialized agent ensembles for each.
• Human-in-the-Loop Design: Prioritizes AI as an assistant to human intellect, focusing on automating arduous tasks like reference hunting, citation formatting, and data verification, allowing researchers to concentrate on critical thinking, interpretation, and argumentation.
• Integrity and Verification: Implements robust quality gates and verification mechanisms, including citation trust chains, claim-faithfulness audits, and multi-perspective peer review, directly addressing issues like hallucinated references and fabricated methodologies.
• Style Calibration & Writing Quality: Tools to ensure AI-generated prose aligns with the researcher's voice and to identify and correct patterns indicative of machine generation, focusing on enhancing overall writing quality.
• Deep Research Capabilities: Features a 13-agent research team with Socratic guidance, PRISMA systematic reviews, and Semantic Scholar API integration.
• Advanced Writing Assistance: Includes style calibration, writing quality checks, LaTeX hardening, visualization support, revision coaching, citation conversion, and VLM figure verification.
• Multi-Perspective Peer Review: A 7-agent reviewer system providing criterion-bound, evidence-anchored judgments, including a Journal-Fit reviewer and dynamic reviewers.
• Experimental Agent Integration: Seamlessly integrates with the companion experiment-agent for managing and validating code or human studies, ensuring verified experiment results feed into the writing process.
• Reproducibility and Provenance: Supports artifact reproducibility lockfiles and experiment provenance intake to track and audit the research process.
• Model Tiering: Offers options to optimize for cost (economy) or leverage advanced capabilities (quality-boost) by adjusting AI model usage.

Typical Use Cases

• Streamlining Literature Reviews: Automating the search, verification, and formatting of countless academic sources.
• Enhancing Paper Drafting: Assisting with structure, content generation, citation management, and ensuring logical consistency.
• Rigorous Manuscript Review: Providing multi-faceted peer review simulations and detailed integrity reports to catch errors and omissions before submission.
• Improving Writing Quality and Style: Calibrating AI output to match authorial voice and adhering to academic writing standards.
• Facilitating Reproducible Research: Documenting experimental provenance and aiding in the creation of verifiable research artifacts.
• Academic Research Pipeline Orchestration: Managing the entire research workflow from inception to final submission with adaptive checkpoints and integrity gates.