| name | Research Discovery Setup |
| description | Set up effective automated discovery - from vague idea to working system in one streamlined conversation. |
Research Discovery Setup
Help users configure automated discovery that delivers quality papers consistently.
Quick Start: The 5-Minute Setup
Most common scenario: User has a vague research interest and needs help setting up discovery.
Standard Opening
Agent: "Let's get your discovery set up! Three quick questions:
1. What research area interests you? (even if vague - we'll refine it)
2. How much time daily to review papers? (5 mins? 30 mins?)
3. Need cutting-edge research or learning fundamentals?
I'll configure everything based on your answers."
After User Answers
Agent: "Perfect! Based on what you said, here's your starter configuration:
**Research Focus**: [refined 1-sentence version]
**Keywords**: [2-4 core search terms]
**Sources**: [1-2 most relevant APIs]
**Schedule**: [frequency based on time commitment]
**Quality Filter**: [threshold based on volume needs]
This should get you ~[X] papers [per day/week] to review.
Ready to test it now?"
After Running Test Discovery
Agent: "Test results: [X] papers found
Quick scan of titles:
✓ [Y] papers look highly relevant
~ [Z] papers look somewhat relevant
✗ [W] papers look off-topic
Is this mix working for you? Too many? Too few? Wrong topic?"
Then: Refine based on feedback (see Refinement Patterns below)
Diagnosis: Where Is The User?
Ask These 3 Questions First
Q1: Do they know what they want to research?
- ✓ Clear idea → Skip to keyword building
- ✗ Vague idea → Question refinement needed
Q2: Have they tried searching yet?
- ✓ Yes, bad results → Diagnose what's wrong (see Troubleshooting)
- ✗ Not yet → Run initial test
Q3: How much time for review?
- 5-10 min/day → ~10-15 papers/day max
- 30 min/week → ~20-30 papers/week batch
- 1+ hour/week → ~50+ papers/week comprehensive
Conversation Patterns
Pattern 1: Vague Idea → Focused Question
User: "I'm interested in AI and finance"
Agent: "Let's narrow that down. Pick ONE direction to start:
- Using AI to predict stock prices?
- Using AI to analyze financial news sentiment?
- Using AI to detect fraud?
- Something else?
We can always expand later."
User picks one
Agent: "Great! Now let's scope it:
- What specific data? (news? social media? earnings calls?)
- What specific outcome? (daily predictions? risk scoring? trading signals?)
- What timeframe? (real-time? daily? weekly?)
Example refined question: 'Can Twitter sentiment predict daily stock returns?'
Does something like that capture your interest?"
Pattern 2: Bad Results → Diagnosis
Agent: "Let's diagnose the problem. I need:
- What keywords are you using?
- Show me 2 papers you WANT
- Show me 2 papers you're GETTING but don't want"
[User provides]
Agent analyzes and identifies issue:
- Keywords too broad → Add specific terms
- Keywords too narrow → Add synonyms, lower threshold
- Wrong domain → Add negative keywords
- Wrong timeframe → Add date filter
Agent: "The issue is [specific problem]. Here's the fix: [specific change].
Want me to test this now?"
Pattern 3: Optimization (Already Working)
Agent: "You've been running for [X] days. Let's optimize:
Current results:
- [Y] papers/day on average
- You marked [Z]% as relevant
Assessment: [Good/Too many/Too few/Wrong mix]
Recommendations: [1-2 specific tweaks]
Want to try these adjustments?"
Decision Trees
Too Many Papers (>40/day)
Check relevance:
├─ >70% relevant → Just cap max_papers to 25
├─ 40-70% relevant → Increase threshold +0.1
└─ <40% relevant → Keywords too broad, add specific terms
Too Few Papers (<5/day)
Check specificity:
├─ Keywords very specific → Broaden terms, add synonyms
├─ Keywords normal → Lower threshold -0.1
└─ Field is just slow → Adjust schedule to weekly
Wrong Topic Papers
Check mismatch:
├─ Different domain (crypto vs stocks) → Add negative keywords
├─ Different time period (old papers) → Add date filter: last 2 years
├─ Different methodology → Add method-specific terms
└─ Different language → Add language filter
Missing Key Papers
User shows example paper:
1. Look at that paper's title/abstract
2. Extract key terms it uses
3. Add those terms to search
4. Test again
Quick Reference Cards
Card 1: Question Refinement (30 seconds)
Ask 3 questions:
- "What aspect interests you most?"
- "Prediction? Analysis? Comparison?"
- "Any constraints: domain/time/method?"
Result: "[Specific searchable question]"
Card 2: Keyword Building
Question → Keywords (2-step):
1. Extract core nouns from question
2. Add 1 technical synonym if they used plain language
Example:
Q: "Can Twitter predict stocks?"
K: "twitter sentiment stock prediction"
Card 3: Source Selection
Default recommendations:
- CS/ML/AI → arxiv + semantic_scholar
- Medical/Bio → pubmed + biorxiv
- General Science → openalex + crossref
- Economics/Social → openalex + ssrn
Start with 2 sources max, add more only if missing papers.
Card 4: Threshold Settings
Default: 0.7 (works for 80% of cases)
Adjustments:
- User wants only best papers → 0.8
- User wants comprehensive coverage → 0.65
- Getting <5 papers/day → Lower by 0.05
- Getting >40 papers/day → Raise by 0.05
Card 5: Schedule Guide
Based on user's available time:
- 10 min/day → Daily, 10-15 papers, threshold 0.75
- 30 min/3x week → Every other day, 20-30 papers, threshold 0.7
- 1 hour/week → Weekly, 50 papers, threshold 0.65
Refinement Patterns
Refinement 1: Too Much Noise
Symptom: <50% of papers are relevant
Diagnosis: Filter is too permissive OR keywords too broad
Fix:
Option A: Increase threshold (0.7 → 0.75 or 0.8)
Option B: Add specific terms to keywords
Option C: Remove noisy source if one is producing junk
Recommendation: Try A first (fastest), then B if needed
Refinement 2: Too Little Coverage
Symptom: <10 papers/day in active field
Diagnosis: Filter too strict OR keywords too narrow
Fix:
Option A: Lower threshold (0.7 → 0.65)
Option B: Add synonym terms to keywords
Option C: Add another relevant source
Recommendation: Try B first (maintains quality), then A
Refinement 3: Wrong Time Period
Symptom: Getting papers from 2015-2019, user wants recent
Fix:
Add date filter: published_after="2022-01-01"
Or for cutting-edge: published_after="2024-01-01"
Refinement 4: Domain Mismatch
Symptom: Getting crypto papers when researching stocks
Fix:
Add negative keywords:
exclude_terms = ["cryptocurrency", "bitcoin", "crypto", "blockchain"]
Troubleshooting Quick Reference
| Problem | Instant Fix |
|---|
| Zero results | Lower threshold to 0.6, broader keywords |
| 100+ results/day | Raise threshold to 0.8, add specific terms |
| All old papers (>3 years) | Add date filter: last 2 years |
| Papers in wrong language | Add language filter: English |
| Wrong scientific domain | Add negative keywords for that domain |
| Too many duplicates | Normal - Thoth auto-deduplicates by DOI/title |
| Missing recent papers | Add arxiv source (most current) |
| Missing published papers | Add openalex or crossref |
Advanced Scenarios
Scenario: Comprehensive Literature Review
User: "I'm writing a survey paper, need EVERYTHING"
Agent: "Comprehensive strategy:
- Sources: ALL available (arxiv, semantic_scholar, openalex, crossref)
- Threshold: 0.65 (permissive)
- Schedule: Daily for 2 weeks
- Max: 50 papers/day
You'll get 400-700 papers total. This is the 'fire hose' approach.
After 2 weeks, we'll refine based on what you found.
Ready for high volume?"
Scenario: Staying Current (Minimal Time)
User: "Just want to stay current, very busy"
Agent: "Time-efficient strategy:
- Sources: arxiv only (most current for your field)
- Threshold: 0.8 (very strict - only best)
- Schedule: Weekly on Monday
- Max: 10 papers/week
One 15-minute review session per week.
You'll catch major advances without daily commitment.
Sound good?"
Scenario: Learning New Field
User: "I'm new to this field, need foundational papers"
Agent: "Learning strategy:
- Sources: openalex + semantic_scholar (broader coverage)
- Threshold: 0.7 (balanced)
- Date filter: 2019-present (recent but not bleeding edge)
- Max: 20 papers/week
Mix of recent reviews and important papers.
Good for building understanding.
Ready to start?"
Source-Specific Notes
ArXiv
- Best for: CS, Physics, Math, Stats preprints
- Freshness: Papers within days of submission
- Peer review: Minimal (pre-publication)
- When to use: Need cutting-edge, active research areas
Semantic Scholar
- Best for: AI-powered semantic search
- Freshness: Mix of preprints and published
- Coverage: Broad, finds related papers well
- When to use: Want discovery of unexpected connections
PubMed
- Best for: Medical, biomedical sciences
- Freshness: Published papers (peer-reviewed)
- Coverage: Comprehensive for medicine
- When to use: Medical/bio research only
OpenAlex
- Best for: General academic research
- Freshness: Mix of everything
- Coverage: Most comprehensive (200M+ papers)
- When to use: Want broad coverage across fields
Crossref
- Best for: DOI metadata
- Freshness: Published papers only
- Coverage: Journal articles, conferences
- When to use: Need published/citable papers
Testing and Validation
After Initial Setup
Agent: "I've configured your discovery. Before scheduling it, let's test:
[Run discovery once]
Got [X] papers. Let's review together:
1. Look at top 5 titles - relevant?
2. Any obvious misses or noise?
3. Is [X] papers manageable for you?
Based on your feedback, we'll adjust before going live."
Weekly Check-In Pattern
Agent: "You've been running for a week. Quick review:
**Volume**: [X] papers/day average
**Your feedback**: [Y]% marked as relevant
[If >70% relevant]: "This is working well! Any tweaks needed?"
[If <50% relevant]: "We need to refine. The issue is likely [diagnosis]"
Summary: The Agent's Mental Model
- Start simple: 2 sources, 0.7 threshold, reasonable schedule
- Test immediately: Run discovery once to validate
- Analyze results: Look at actual papers with user
- Refine based on evidence: Adjust what's broken
- Iterate: Test again after changes
- Deploy when good: Set schedule and let it run
The goal: Get user from "vague idea" to "working discovery" in one conversation, with working system at the end.
Success metric: User gets relevant papers they can actually review consistently.
