Research Framework Overview

The Research Framework automates academic research management across an 8-stage workflow: discover papers via semantic search, acquire PDFs with integrity validation, summarize using retrieval-augmented generation, track citations, assess quality with GRADE methodology, archive for long-term preservation, and maintain provenance. It eliminates manual busywork while enforcing standards that prevent the most common research failure mode — hallucinated citations.

Why It Exists

Literature reviews are slow and error-prone when done manually. The framework addresses specific pain points:

Problem	What the Framework Does	Time Savings
Manual paper search across databases	Semantic search with relevance ranking	60% faster
Tracking where papers came from	Persistent REF-XXX identifiers assigned on acquisition	Always organized
Paywalled paper handling	Unpaywall integration + manual upload workflow	Streamlined
Reading full papers	RAG-grounded summarization at 3 levels	~75% faster (20min → 5min)
Fabricated citations	All claims validated against source text	0% vs 56% hallucination rate
Inconsistent quality assessment	Automated GRADE scoring	Consistent standards
Fragmented notes	Zettelkasten literature notes with atomic linking	Scalable knowledge base

The 8-Stage Workflow

1. Discovery      Find papers via semantic search, gap detection
2. Acquisition    Download PDFs, assign REF-XXX, validate integrity
3. Documentation  RAG summarization, structured extraction, lit notes
4. Citation       Format citations, build citation networks, bibliographies
5. Quality        GRADE assessments, FAIR validation, quality scoring
6. Synthesis      Create permanent notes, link related work
7. Gap Analysis   Identify research gaps and contradictions
8. Archival       OAIS-compliant archiving, integrity verification, provenance

All 8 stages are implemented in v1.0.0.

Agent Catalog

Eight agents, one per workflow stage:

Agent	Purpose	Key Capability
`discovery-agent`	Semantic search, gap detection	200M+ papers via Semantic Scholar; citation network traversal
`research-acquisition-agent`	Download PDFs, assign IDs	FAIR validation, SHA-256 checksums, deduplication
`documentation-agent`	RAG summarization	Zero-hallucination target; multi-level summaries; Zettelkasten notes
`citation-agent`	Format citations	9000+ citation styles; citation network analysis
`quality-agent`	Assess paper quality	GRADE methodology (High/Moderate/Low/Very Low)
`archival-agent`	Long-term preservation	OAIS compliance; SIP/AIP/DIP packages
`provenance-agent`	Lineage tracking	W3C PROV logging; reproducibility packages
`workflow-agent`	Orchestrate pipelines	DAG-based execution; parallel stages; failure recovery

Key Design Choices

REF-XXX Identifiers

Every paper gets a persistent `REF-XXX` identifier when acquired. This ID appears in summaries, citations, notes, and provenance records, providing a stable cross-reference that survives database reorganizations and file moves.

Zero-Hallucination Target for Summaries

The documentation agent validates every claim in a summary against the source PDF before including it. Claims with confidence below the threshold are flagged for user review rather than included silently. This is the reason the framework requires actual PDFs rather than just metadata.

FAIR Compliance

Acquired papers are scored on Findability, Accessibility, Interoperability, and Reusability (0-100). Papers below threshold are flagged rather than silently accepted. This matters for systematic reviews where you need to demonstrate that your corpus meets quality standards.

GRADE Quality Assessment

The quality agent applies the GRADE framework used in clinical and policy research to assess evidence quality. This is more useful than star ratings or citation counts because it evaluates the type and reliability of evidence, not just its popularity.

Storage Structure

All research artifacts go in `.aiwg/research/`:

.aiwg/research/
├── discovery/              # Search results, strategies, gap reports, acquisition queues
├── sources/                # Stage 2 output
│   ├── pdfs/               # REF-XXX-{slug}.pdf
│   ├── metadata/           # REF-XXX-metadata.json
│   └── checksums.txt       # SHA-256 integrity verification
├── knowledge/              # Stage 3 output
│   ├── summaries/          # REF-XXX-summary.md
│   ├── extractions/        # REF-XXX-extraction.json (structured data)
│   └── notes/              # REF-XXX-literature-note.md + permanent notes
└── config/                 # Per-agent configuration YAML files

Integration with SDLC

The research framework can be used alongside sdlc-complete. The artifact directories do not overlap (`.aiwg/research/` vs `.aiwg/requirements/` etc.). A common pattern is using the discovery and documentation agents during Inception and Elaboration phases to ground architectural decisions in the literature:

# During Inception: research relevant patterns
aiwg research search "event sourcing CQRS patterns" --year 2020-2024

# During Elaboration: research authentication approaches
aiwg research search "OAuth2 security vulnerabilities" --venue conference

References

• `@$AIWG_ROOT/agentic/code/frameworks/research-complete/docs/quickstart.md` — Deploy and first literature review
• `@$AIWG_ROOT/agentic/code/frameworks/research-complete/agents/` — Agent definitions
• `@$AIWG_ROOT/agentic/code/frameworks/research-complete/inception/vision-document.md` — Framework vision