Overview
A research agent with memory transforms literature review and knowledge synthesis. Instead of starting each search from scratch, the agent builds cumulative understanding of your research domain, remembers what you've read, and tracks how your thinking evolves.
Core Capabilities
Literature Tracking
The agent maintains your reading history:
Papers read and their key findings
Your annotations and highlights
Connections between papers you've noted
Papers to read (your backlog)
Knowledge Synthesis
Building understanding over time:
Key concepts and their definitions across sources
Contradictions and debates in the field
Evolution of ideas chronologically
Gaps in current research
Citation Management
Intelligent reference handling:
Papers you've cited before and in what context
Citation networks and influential works
Relevant papers for current writing
Formatting preferences by venue
Research Context
Understanding your specific project:
Your research questions and hypotheses
Methodology choices and rationale
Data sources and their limitations
Advisor feedback and revisions
Memory-Enabled Workflows
Literature Discovery
"Find papers related to my transformer efficiency research"
With memory:
Knows your specific focus (attention mechanisms, not general transformers)
Excludes papers you've already read
Weights toward authors whose work you've found valuable
Considers your methodology preferences
Writing Assistance
"Help me write the related work section"
With context:
Knows which papers are most relevant to your contribution
Remembers how you've positioned your work
Maintains consistent framing across sections
Suggests citations from your library
Research Evolution
Track how understanding develops:
"What did I think about X six months ago vs. now?"
"When did I first encounter this concept?"
"What changed my mind about this approach?"
Memory Structure
Paper Representations
For each paper:
Full text or abstract
Your summary and key takeaways
Relevance to your research questions
Connections to other papers
Quality assessment
Concept Graph
Key terms and their definitions
Relationships between concepts
Sources for each concept
Your understanding level
Research Timeline
Chronological reading history
Idea evolution over time
Decision points and pivots
Milestone achievements
Example Interaction
**First Encounter:**
User: "Summarize this paper on memory-augmented transformers"
Agent: [Provides summary, stores in memory, notes relevance to user's research]
**Weeks Later:**
User: "How does the paper I read last month compare to this new one?"
Agent: "The Chen et al. paper you read on March 15th proposed using external memory banks, while this new paper from Lee et al. uses learned memory tokens. Both address the context length limitation you're investigating, but Chen's approach showed better results on long-document tasks - which aligns more with your QA dataset. However, Lee's method is more parameter-efficient, which you noted as a concern in your methodology notes."
Research Integrity
Track provenance of all claims
Distinguish your ideas from sourced ones
Maintain citation accuracy
Flag potential self-plagiarism