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:

1. •What keywords are you using?
2. •Show me 2 papers you WANT
3. •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:

1. •"What aspect interests you most?"
2. •"Prediction? Analysis? Comparison?"
3. •"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

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

1. •Start simple: 2 sources, 0.7 threshold, reasonable schedule
2. •Test immediately: Run discovery once to validate
3. •Analyze results: Look at actual papers with user
4. •Refine based on evidence: Adjust what's broken
5. •Iterate: Test again after changes
6. •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.