---
name: literature-review-with-traceable-ai-evidence
version: 1.0.0
description: Enable researchers to ingest documents, asynchronously index them, and
  interact with multi-agent AI to answer questions with verifiable citations to original
  text.
inputs:
- Documents in PDF, Office, image, or text formats
- Research questions or topics of interest
- 'Optional: user model configuration via .env or settings'
steps:
- Upload documents to a project (via desktop app or web UI)
- System asynchronously converts Office docs to PDF if needed, runs OCR to extract
  text with coordinates, chunks and embeds into vector store
- User starts a main research session or creates exploration branches without waiting
  for indexing to finish
- Leader agent receives query and delegates subtasks to researcher, reviewer, writer
  subagents
- Subagents perform hybrid retrieval and rerank to find relevant chunks with source
  coordinates
- Agents synthesize answers and return evidence with clickable citations that highlight
  original pages
- User verifies conclusions by navigating to cited source locations and can save notes
  to research memory or mind map
outputs:
- AI-generated answers with traceable evidence (coordinates, page highlights)
- Research session history with branches
- Indexed document library for future queries
- Mind maps or structured notes
- Persistent run events for resuming sessions
tags: []
metadata:
  source_repo: https://github.com/0verL1nk/PaperSage.git
  extracted_at: ''
  confidence: 0.85
---

# literature-review-with-traceable-ai-evidence

Enable researchers to ingest documents, asynchronously index them, and interact with multi-agent AI to answer questions with verifiable citations to original text.

## Steps

1. Upload documents to a project (via desktop app or web UI)
2. System asynchronously converts Office docs to PDF if needed, runs OCR to extract text with coordinates, chunks and embeds into vector store
3. User starts a main research session or creates exploration branches without waiting for indexing to finish
4. Leader agent receives query and delegates subtasks to researcher, reviewer, writer subagents
5. Subagents perform hybrid retrieval and rerank to find relevant chunks with source coordinates
6. Agents synthesize answers and return evidence with clickable citations that highlight original pages
7. User verifies conclusions by navigating to cited source locations and can save notes to research memory or mind map

## Inputs

- Documents in PDF, Office, image, or text formats
- Research questions or topics of interest
- Optional: user model configuration via .env or settings

## Outputs

- AI-generated answers with traceable evidence (coordinates, page highlights)
- Research session history with branches
- Indexed document library for future queries
- Mind maps or structured notes
- Persistent run events for resuming sessions

## Failure Modes

- Missing local Office/LibreOffice converter causes document conversion failure
- First-time model download may be slow or require network
- OCR may have low confidence on poor quality scans
- Retrieval might miss context if chunking splits semantics
- Multi-agent coordination could produce conflicting intermediate results

## Source

Extracted from: [https://github.com/0verL1nk/PaperSage.git](https://github.com/0verL1nk/PaperSage.git)
Confidence: 0.85