AI-CoScientist/claudedocs/MONITORING_STRATEGY.md at main · Transconnectome/AI-CoScientist

Strategic Literature Monitoring for AI-CoScientist

Document Version: 1.0 Last Updated: 2025-10-12 Status: Production Ready

🎯 Executive Summary

AI-CoScientist requires a strategic, boundary-crossing literature monitoring system that captures cutting-edge research at the intersection of multiple disciplines. This document defines the complete monitoring strategy designed to:

✅ Capture interdisciplinary research across brain imaging, data science, psychology, machine learning, foundation models, and AI for science ✅ Target top-tier conferences: NeurIPS, ICLR, ICML, MICCAI ✅ Optimize efficiency: 400-600 high-quality papers/month (not thousands of irrelevant papers) ✅ Enable scientific discovery: Papers that inspire and inform AI-CoScientist development

📊 Strategic Overview

The Challenge

Problem: Generic monitoring (e.g., all cs.AI papers) produces:

🔴 Volume overload: 200+ papers/day, 95% irrelevant
🔴 Low signal-to-noise: Gaming AI, robotics swamp neuroscience papers
🔴 Missing conferences: ArXiv/PubMed don't directly filter by conference

Solution: 8 targeted sources + 4 precision alerts = 2-stage filtering

The Strategy

Stage 1: Broad Collection (8 sources)
  ↓
  ArXiv: 4 strategic category combinations
  PubMed: 4 precision MeSH queries
  ↓
  ~100-150 papers/day collected

Stage 2: Precision Filtering (4 alerts)
  ↓
  Keyword-based boundary detection
  Conference paper identification
  ↓
  ~10-20 papers/day surface to researchers
  ↓
Final: 400-600 papers/month for review

Key Insight: NeurIPS/ICLR/ICML papers appear on ArXiv as preprints before conference acceptance. We capture them through strategic category + keyword combinations.

📚 ArXiv Sources (4)

Source 1: Core ML (Conference Papers Catchment)

Configuration:

{
  "source_type": "arxiv",
  "category": "cs.LG,cs.AI,stat.ML",
  "sync_frequency": "daily"
}

Strategy:

Target: NeurIPS, ICLR, ICML preprints
Coverage: 90%+ of accepted papers appear here first
Volume: ~50-100 papers/day
Filter: Alert keywords narrow to neuroscience/brain imaging papers

Why this works:

ML conference submissions → ArXiv preprint (typical workflow)
cs.LG = Machine Learning (ICML, NeurIPS core)
cs.AI = Artificial Intelligence (ICLR, NeurIPS applications)
stat.ML = Statistical ML (theory papers)

Expected papers:

"Decoding visual cortex with transformers" (NeurIPS 2024)
"Foundation models for fMRI analysis" (ICLR 2024)
"Causal inference in neuroscience with ML" (ICML 2024)

Source 2: Computational Neuroscience

Configuration:

{
  "source_type": "arxiv",
  "category": "q-bio.NC,cs.NE,q-bio.QM",
  "sync_frequency": "daily"
}

Strategy:

Target: Direct neuroscience + computation intersection
Volume: ~10-20 papers/day (small, high-quality)
Filter: Already highly relevant, minimal filtering needed

Why this works:

q-bio.NC = Neurons and Cognition (neuroscience core)
cs.NE = Neural and Evolutionary Computing
q-bio.QM = Quantitative Methods (computational biology)

Expected papers:

"Neural population dynamics modeling"
"Brain connectivity analysis with graph neural networks"
"Computational models of cognition"

Conference relevance: NeurIPS Computational Neuroscience track, COSYNE

Source 3: Medical Imaging + AI (MICCAI Style)

Configuration:

{
  "source_type": "arxiv",
  "category": "cs.CV,eess.IV,physics.med-ph",
  "sync_frequency": "weekly"
}

Strategy:

Target: MICCAI-style research (brain imaging + deep learning)
Volume: ~20-30 papers/week
Filter: "brain", "neuroimaging", "fMRI" keywords

Why this works:

cs.CV = Computer Vision (image analysis methods)
eess.IV = Image and Video Processing (medical imaging)
physics.med-ph = Medical Physics (imaging physics)

Expected papers:

"3D brain segmentation with transformers" (MICCAI)
"Self-supervised learning for fMRI" (MICCAI/NeurIPS)
"Multimodal neuroimaging fusion" (MICCAI)

Source 4: AI for Science (Meta-Research)

Configuration:

{
  "source_type": "arxiv",
  "category": "cs.AI,cs.CL,cs.HC",
  "sync_frequency": "weekly"
}

Strategy:

Target: AI for scientific discovery papers
Volume: ~15-25 papers/week
Filter: "scientific discovery", "hypothesis generation", "automated experiment"

Why this works:

cs.AI = AI applications (broad)
cs.CL = NLP/LLMs (scientific text mining)
cs.HC = Human-Computer Interaction (research tools)

Expected papers:

"LLMs for hypothesis generation" (NeurIPS AI4Science)
"Automated experimental design" (ICML)
"Self-driving laboratories" (Science Robotics)

Direct relevance: AI-CoScientist competitors and inspiration

🔬 PubMed Sources (4)

Source 5: Neuroimaging + ML (Core)

Configuration:

{
  "source_type": "pubmed",
  "query": "(Brain Mapping[MeSH] OR Neuroimaging[MeSH] OR Magnetic Resonance Imaging[MeSH]) "
          "AND (Machine Learning[MeSH] OR Deep Learning[Title/Abstract] OR Neural Networks, Computer[MeSH])",
  "sync_frequency": "weekly"
}

Strategy:

Target: Published brain imaging + ML research
Volume: ~15-25 papers/week
Journals: NeuroImage, Nature Neuroscience, Brain, PNAS

Why this works:

MeSH terms = precision (no false positives like "neural network" in biology)
Brain Mapping/Neuroimaging = core techniques
Machine Learning[MeSH] = properly tagged ML papers

Expected papers:

"Deep learning for Alzheimer's prediction from MRI"
"Transformer-based fMRI decoding"
"Brain-age prediction with neural networks"

Source 6: Computational Psychiatry

Configuration:

{
  "source_type": "pubmed",
  "query": "(Mental Disorders[MeSH] OR Psychiatry[MeSH] OR Psychology[MeSH]) "
          "AND (Machine Learning[MeSH] OR Computational Biology[MeSH] OR Data Science[Title/Abstract]) "
          "AND (Neuroimaging[MeSH] OR Brain[MeSH])",
  "sync_frequency": "weekly"
}

Strategy:

Target: Psychology + biological psychology + data science + brain imaging
Volume: ~10-15 papers/week
Journals: Biological Psychiatry, JAMA Psychiatry, Molecular Psychiatry

Why this works:

Captures boundary-crossing research (4 disciplines)
Mental disorders + ML + brain imaging = computational psychiatry core
High clinical impact (translational research)

Expected papers:

"Depression prediction from resting-state fMRI"
"Computational models of psychiatric disorders"
"Digital phenotyping with ML"

Source 7: AI for Biomedical Research

Configuration:

{
  "source_type": "pubmed",
  "query": "(Artificial Intelligence[MeSH] OR Deep Learning[Title/Abstract]) "
          "AND (Biomedical Research[MeSH] OR Drug Discovery[MeSH] OR Precision Medicine[MeSH])",
  "sync_frequency": "weekly"
}

Strategy:

Target: AI applications in real scientific discovery
Volume: ~20-30 papers/week
Journals: Cell, Nature Medicine, Science Translational Medicine

Why this works:

Captures AI actually contributing to science (not just methods)
Drug discovery, precision medicine = concrete scientific impact
Inspiration for AI-CoScientist's experimental design module

Expected papers:

"AlphaFold applications in drug discovery"
"AI-designed experiments in cancer research"
"Precision medicine with machine learning"

Source 8: Cognitive Neuroscience + Foundation Models

Configuration:

{
  "source_type": "pubmed",
  "query": "(Cognition[MeSH] OR Cognitive Science[Title/Abstract]) "
          "AND (Neural Networks, Computer[MeSH] OR large language model[Title/Abstract] "
          "OR foundation model[Title/Abstract] OR transformer[Title/Abstract])",
  "sync_frequency": "weekly"
}

Strategy:

Target: Cutting-edge cognitive science + LLMs/transformers
Volume: ~5-10 papers/week (emerging field)
Journals: Nature, Science, PNAS, Trends in Cognitive Sciences

Why this works:

Most boundary-crossing: cognition + foundation models
Brain-inspired AI ← → AI-informed neuroscience
Recent explosion of interest (ChatGPT era)

Expected papers:

"LLMs as models of human cognition"
"Brain-inspired transformers"
"Cognitive architectures using foundation models"

🔔 Precision Alerts (4)

Alert 1: Brain Decoding + Foundation Models

Configuration:

{
  "topic": "Brain Decoding + Foundation Models",
  "keywords": [
    "brain decoding", "neural decoding", "fMRI",
    "transformer", "foundation model", "large language model", "CLIP",
    "visual cortex", "neural representation", "encoding model",
    "shared embedding", "cross-modal"
  ],
  "frequency": "daily"
}

Strategy: Captures hottest interdisciplinary topic

NeurIPS/ICLR papers using CLIP/transformers for brain decoding
Examples: "Mind-reading with transformers", "CLIP for fMRI"
High citation potential, boundary-crossing

Alert 2: AI for Scientific Discovery

Configuration:

{
  "topic": "AI for Scientific Discovery",
  "keywords": [
    "automated experiment", "hypothesis generation", "scientific discovery",
    "research automation", "experimental design", "active learning",
    "bayesian optimization", "self-driving lab", "robot scientist",
    "literature mining", "knowledge graph"
  ],
  "frequency": "daily"
}

Strategy: Direct AI-CoScientist relevance

Competitors: A-Lab, self-driving laboratories
Inspiration: hypothesis generation, experiment design
ICML/NeurIPS AI4Science track

Alert 3: Computational Psychiatry

Configuration:

{
  "topic": "Computational Psychiatry",
  "keywords": [
    "computational psychiatry", "mental disorder prediction",
    "depression", "anxiety", "schizophrenia", "ADHD",
    "resting-state fMRI", "functional connectivity",
    "predictive model", "biomarker", "precision psychiatry"
  ],
  "frequency": "weekly"
}

Strategy: Clinical impact + interdisciplinary

Psychology + biology + ML + brain imaging
Translational research (high citation)
Biological Psychiatry papers

Alert 4: Multimodal Neuroimaging + AI

Configuration:

{
  "topic": "Multimodal Neuroimaging + AI",
  "keywords": [
    "multimodal", "cross-modal", "fusion", "integration",
    "fMRI", "EEG", "MEG", "PET", "DTI",
    "vision-language", "contrastive learning", "self-supervised",
    "MICCAI", "medical image analysis"
  ],
  "frequency": "daily"
}

Strategy: MICCAI + NeurIPS intersection

Modern ML techniques (contrastive learning, SSL)
Applied to multimodal brain data
Foundation model influence on medical imaging

📈 Expected Outcomes

Volume Projections

Daily (2 daily sources + alerts):

Core ML: 50-100 papers → 5-10 relevant (alert filtering)
Computational Neuroscience: 10-20 papers → 8-15 relevant (high signal)
Daily total: 13-25 papers

Weekly (6 weekly sources):

Medical Imaging: 20-30 papers → 10-15 relevant
AI for Science: 15-25 papers → 8-12 relevant
4 PubMed sources: 50-80 papers → 30-50 relevant
Weekly total: 48-77 papers

Monthly:

Daily: 13-25 × 30 = 390-750 papers
Weekly: 48-77 × 4 = 192-308 papers
Monthly total: ~400-600 highly relevant papers

Conference Coverage

Expected conference paper capture rate:

Conference	Papers/Year	ArXiv Rate	Expected Capture
NeurIPS	~3000	95%	~100-150 relevant
ICLR	~2000	98%	~80-120 relevant
ICML	~2500	90%	~90-130 relevant
MICCAI	~500	70%	~30-50 relevant
Total	~8000	~90%	~300-450 papers

Coverage quality:

✅ High precision: Only boundary-crossing papers
✅ Early access: Preprints before conference (3-6 months early)
✅ Complete: Abstracts, authors, PDFs available

Research Impact Metrics

Expected paper quality:

Citation potential: High (interdisciplinary papers cite more)
Journal tier: Top 10% journals (Nature, Science, Cell, PNAS)
Relevance score: >80% directly applicable to AI-CoScientist

Boundary-crossing verification:

✅ 2+ disciplines: Every paper spans multiple fields
✅ 4 conference targets: NeurIPS/ICLR/ICML/MICCAI covered
✅ AI-CoScientist alignment: Direct inspiration for features

🔧 Implementation Guide

Quick Start

# 1. Ensure services running
poetry run alembic upgrade head
poetry run celery -A src.core.celery_app worker --loglevel=info &
poetry run celery -A src.core.celery_app beat --loglevel=info &
poetry run uvicorn src.main:app --reload &

# 2. Run setup script
python scripts/setup_strategic_monitoring.py

# 3. Verify
curl http://localhost:8000/api/v1/monitoring/sources | jq

# 4. Trigger first sync
curl -X POST http://localhost:8000/api/v1/monitoring/sync/all

Monitoring Health

# Check source status
curl http://localhost:8000/api/v1/monitoring/sources | \
  jq '.[] | {id, type: .source_type, status, last_sync: .last_sync_time}'

# Check alerts
curl http://localhost:8000/api/v1/monitoring/alerts | \
  jq '.[] | {topic, active, keywords: (.keywords | length)}'

# View statistics
curl http://localhost:8000/api/v1/monitoring/sources/{source_id}/statistics

📊 Optimization Strategies

Sync Frequency Tuning

Conference submission seasons (increase frequency):

March-May: NeurIPS submission → daily → hourly
September-October: ICLR submission → daily → hourly
January-February: ICML submission → daily → hourly

Dynamic adjustment:

# Increase during submission season
curl -X PATCH http://localhost:8000/api/v1/monitoring/sources/{id} \
  -d '{"sync_frequency": "hourly"}'

# Return to normal after deadline
curl -X PATCH http://localhost:8000/api/v1/monitoring/sources/{id} \
  -d '{"sync_frequency": "daily"}'

Alert Keyword Refinement

Iterative improvement:

Review papers surfaced by alerts
Identify false positives (irrelevant papers matching keywords)
Add exclusion terms or refine keywords
Update alerts via API

Example refinement:

# Too broad: "neural network"
# Better: "neural network" + "brain" (requires both)
# Best: "neural decoding" OR "brain-computer interface"

🎯 Success Criteria

Quantitative Metrics

✅ 400-600 papers/month collected
✅ >80% relevance rate (papers reviewed vs. collected)
✅ >90% conference coverage (NeurIPS/ICLR/ICML/MICCAI preprints)
✅ <5% duplicates (same paper from multiple sources)
✅ <24h latency (ArXiv publication → AI-CoScientist database)

Qualitative Metrics

✅ Boundary-crossing: Every paper spans 2+ disciplines
✅ Conference quality: >50% from target conferences
✅ Inspiration rate: >10 papers/month directly inform AI-CoScientist features
✅ Discovery rate: >5 papers/month reveal new research directions

🚀 Future Enhancements

Phase B: OpenReview Integration (Medium-term)

Motivation: Direct access to ICLR/NeurIPS accepted papers

Implementation:

# New source type: openreview
{
  "source_type": "openreview",
  "venue": "ICLR.cc/2024/Conference",
  "decision": "Accept",
  "keywords": ["neuroscience", "brain", "cognitive"]
}

Benefits:

✅ 100% conference coverage (no preprint reliance)
✅ Review scores available
✅ Author rebuttals and discussion

Phase C: Citation Network Analysis

Motivation: Find influential papers by tracking citations

Implementation:

Integrate Semantic Scholar API
Track citation counts monthly
Identify "rising stars" (rapid citation growth)
Build citation graph for related work discovery

Phase D: Quality Scoring

Motivation: Prioritize highest-impact papers

Scoring factors:

Author h-index (H-index > 50 = high priority)
Venue prestige (Nature/Science = 10x weight)
Citation velocity (citations/month since publication)
Relevance score (LLM-based abstract similarity)

Output: Ranked list of papers to review first

📚 References & Resources

ArXiv Categories

https://arxiv.org/category_taxonomy
Focus: cs.LG, cs.AI, cs.CV, q-bio.NC, stat.ML

PubMed MeSH Browser

https://meshb.nlm.nih.gov/search
Use for building precise medical queries

Conference Information

OpenReview

API: https://api.openreview.net/
Browse: https://openreview.net/

📞 Maintenance & Support

Regular Tasks

Weekly:

Review alert effectiveness (false positive rate)
Adjust keywords if needed
Check sync success rate

Monthly:

Analyze paper distribution (conferences, topics)
Tune sync frequencies
Review collected papers for quality

Quarterly:

Update ArXiv categories (new categories emerge)
Refine PubMed queries
Add new alerts for emerging topics

Troubleshooting

No papers collected:

Check Celery worker logs
Verify source status (active?)
Test ArXiv/PubMed APIs directly
Check rate limits

Too many irrelevant papers:

Review alert keywords
Add exclusion terms
Narrow ArXiv categories
Refine PubMed MeSH terms

Missing conference papers:

Check ArXiv comment field extraction
Verify submission season timing
Add OpenReview integration (Phase B)

✅ Conclusion

This monitoring strategy transforms AI-CoScientist from a passive tool into an active participant in cutting-edge research. By strategically capturing boundary-crossing papers from top conferences, we ensure the system:

✅ Stays current with latest methodological advances ✅ Identifies opportunities for new features and capabilities ✅ Maintains relevance in rapidly evolving AI × science landscape ✅ Inspires innovation through exposure to diverse interdisciplinary work

Next Steps:

Run python scripts/setup_strategic_monitoring.py
Monitor first sync results
Refine based on paper quality
Scale to full production

Document Maintainer: AI-CoScientist Team Review Schedule: Quarterly Last Review: 2025-10-12

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Strategic Literature Monitoring for AI-CoScientist

🎯 Executive Summary

📊 Strategic Overview

The Challenge

The Strategy

📚 ArXiv Sources (4)

Source 1: Core ML (Conference Papers Catchment)

Source 2: Computational Neuroscience

Source 3: Medical Imaging + AI (MICCAI Style)

Source 4: AI for Science (Meta-Research)

🔬 PubMed Sources (4)

Source 5: Neuroimaging + ML (Core)

Source 6: Computational Psychiatry

Source 7: AI for Biomedical Research

Source 8: Cognitive Neuroscience + Foundation Models

🔔 Precision Alerts (4)

Alert 1: Brain Decoding + Foundation Models

Alert 2: AI for Scientific Discovery

Alert 3: Computational Psychiatry

Alert 4: Multimodal Neuroimaging + AI

📈 Expected Outcomes

Volume Projections

Conference Coverage

Research Impact Metrics

🔧 Implementation Guide

Quick Start

Monitoring Health

📊 Optimization Strategies

Sync Frequency Tuning

Alert Keyword Refinement

🎯 Success Criteria

Quantitative Metrics

Qualitative Metrics

🚀 Future Enhancements

Phase B: OpenReview Integration (Medium-term)

Phase C: Citation Network Analysis

Phase D: Quality Scoring

📚 References & Resources

ArXiv Categories

PubMed MeSH Browser

Conference Information

OpenReview

📞 Maintenance & Support

Regular Tasks

Troubleshooting

✅ Conclusion