Agent Solve-Limitations Command¶

The agent solve-limitations command provides practical solutions and workarounds for paper limitations, helping you adapt research to real-world constraints.

Basic Usage¶

scoutml agent solve-limitations ARXIV_ID [OPTIONS]

Examples¶

General Solutions¶

# Get solutions for all limitations
scoutml agent solve-limitations 2010.11929

Focused Solutions¶

# Focus on computational limitations
scoutml agent solve-limitations 2103.00020 \
  --focus computational \
  --tradeoffs speed \
  --tradeoffs memory

Options¶

Option	Type	Default	Description
`--focus`	TEXT	None	Specific limitation to focus on
`--tradeoffs`	TEXT	None	Acceptable tradeoffs (multiple): accuracy/speed/memory/complexity/data_requirements/quality
`--output`	CHOICE	rich	Output format: rich/json
`--export`	PATH	None	Export solutions to file

Limitation Types¶

Computational¶

scoutml agent solve-limitations 2103.00020 --focus computational

Addresses: - High memory usage - Long training time - Inference speed - Hardware requirements

Data Requirements¶

scoutml agent solve-limitations 1810.04805 --focus "data requirements"

Solves: - Large dataset needs - Annotation costs - Data quality issues - Domain specificity

Scalability¶

scoutml agent solve-limitations 2010.11929 --focus scalability

Handles: - Model size constraints - Batch processing - Distributed training - Production deployment

Accuracy/Performance¶

scoutml agent solve-limitations 1906.08237 --focus accuracy

Improves: - Model performance - Generalization - Robustness - Edge cases

Tradeoff Options¶

Speed Optimization¶

scoutml agent solve-limitations 2103.00020 \
  --tradeoffs speed \
  --tradeoffs accuracy

Willing to trade accuracy for speed.

Memory Efficiency¶

scoutml agent solve-limitations 2010.11929 \
  --tradeoffs memory \
  --tradeoffs quality

Reduce memory at cost of quality.

Simplicity¶

scoutml agent solve-limitations 1706.03762 \
  --tradeoffs complexity \
  --tradeoffs accuracy

Simpler model, acceptable accuracy loss.

Solution Components¶

1. Limitation Analysis¶

Identified limitations
Root causes
Impact assessment

2. Solution Strategies¶

Immediate fixes
Architectural changes
Alternative approaches
Hybrid solutions

3. Implementation Details¶

Code modifications
Configuration changes
Tool suggestions
Library recommendations

4. Tradeoff Analysis¶

What you gain
What you lose
When to use
Quantitative impacts

5. Case Studies¶

Real implementations
Success stories
Benchmark results
Practical examples

Use Cases¶

Production Deployment¶

# Optimize for production
scoutml agent solve-limitations 2103.00020 \
  --focus computational \
  --tradeoffs speed \
  --tradeoffs memory \
  --export production_optimizations.md

Resource-Constrained Environment¶

# Mobile/edge deployment
scoutml agent solve-limitations 2010.11929 \
  --focus "model size" \
  --tradeoffs accuracy \
  --tradeoffs memory

Limited Data Scenarios¶

# Few-shot/low-resource
scoutml agent solve-limitations 1810.04805 \
  --focus "data requirements" \
  --tradeoffs quality

Real-time Applications¶

# Latency-critical systems
scoutml agent solve-limitations 2103.00020 \
  --focus "inference speed" \
  --tradeoffs accuracy \
  --tradeoffs quality

Advanced Usage¶

Progressive Optimization¶

# Start with original
scoutml agent implement 2103.00020

# Get initial optimizations
scoutml agent solve-limitations 2103.00020 \
  --focus computational \
  --tradeoffs accuracy

# Further optimize
scoutml agent solve-limitations 2103.00020 \
  --focus "inference speed" \
  --tradeoffs memory \
  --tradeoffs quality

Multi-Model Solutions¶

# Compare solutions across models
models=("2010.11929" "2102.05918" "2105.08050")

for model in "${models[@]}"; do
    scoutml agent solve-limitations "$model" \
        --focus computational \
        --tradeoffs speed \
        --output json \
        --export "solutions_${model}.json"
done

Constraint-Based Selection¶

# Find model that fits constraints
# First, get limitations
scoutml agent critique 2103.00020 --aspects reproducibility

# Then solve for your constraints
scoutml agent solve-limitations 2103.00020 \
  --focus "memory usage" \
  --tradeoffs accuracy \
  --export memory_optimized.md

Solution Examples¶

Model Compression¶

For high memory usage: - Quantization techniques - Pruning strategies - Knowledge distillation - Architecture search

Data Efficiency¶

For large data requirements: - Data augmentation - Semi-supervised learning - Transfer learning - Synthetic data

Speed Optimization¶

For slow inference: - Model optimization - Caching strategies - Batch processing - Hardware acceleration

Output Examples¶

Rich Output¶

Structured display with: - Problem → Solution mapping - Implementation code - Benchmark comparisons - Decision trees

JSON Output¶

{
  "limitations": [
    {
      "type": "computational",
      "description": "Requires 32GB GPU memory",
      "severity": "high"
    }
  ],
  "solutions": [
    {
      "name": "Gradient checkpointing",
      "tradeoffs": {
        "memory": "-60%",
        "speed": "+40% training time"
      },
      "implementation": "...",
      "effectiveness": "high"
    }
  ]
}

Best Practices¶

Choosing Tradeoffs¶

Be realistic about acceptable losses
Prioritize critical metrics
Consider cascade effects
Test empirically

Implementation Strategy¶

Start with easy wins - Config changes
Measure baseline - Know starting point
Apply incrementally - One change at a time
Validate results - Ensure quality

Production Considerations¶

Test thoroughly - Edge cases matter
Monitor metrics - Track tradeoffs
Have rollback plan - Safety first
Document changes - Future reference

Common Workflows¶

Optimization Pipeline¶

# 1. Identify limitations
scoutml agent critique 2103.00020

# 2. Get solutions
scoutml agent solve-limitations 2103.00020 \
  --focus computational

# 3. Implement optimizations
scoutml agent implement 2103.00020 --level advanced

# 4. Compare results
scoutml compare 2103.00020 2111.06377  # Compare with efficient variant

Feasibility Analysis¶

# Check if paper fits constraints
PAPER="2301.08727"
MAX_MEMORY="8GB"
MAX_LATENCY="100ms"

scoutml agent solve-limitations $PAPER \
  --focus computational \
  --tradeoffs accuracy \
  --output json | \
  jq '.solutions[] | select(.requirements.memory <= "8GB")'

Tips and Tricks¶

Solution Selection¶

Match constraints to production needs
Consider compound solutions
Benchmark everything
Read case studies

Common Optimizations¶

Mixed precision - Often free speedup
Batch size tuning - Memory/speed balance
Model pruning - Reduce size/compute
Caching - Reuse computations

Avoiding Pitfalls¶

Don't over-optimize early
Maintain accuracy thresholds
Consider maintenance cost
Document all changes

agent critique - Identify limitations
agent implement - Get base implementation
compare - Compare with alternatives

Agent Solve-Limitations Command¶

Basic Usage¶

Examples¶

General Solutions¶

Focused Solutions¶

Options¶

Limitation Types¶

Computational¶

Data Requirements¶

Scalability¶

Accuracy/Performance¶

Tradeoff Options¶

Speed Optimization¶

Memory Efficiency¶

Simplicity¶

Solution Components¶

1. Limitation Analysis¶

2. Solution Strategies¶

3. Implementation Details¶

4. Tradeoff Analysis¶

5. Case Studies¶

Use Cases¶

Production Deployment¶

Resource-Constrained Environment¶

Limited Data Scenarios¶

Real-time Applications¶

Advanced Usage¶

Progressive Optimization¶

Multi-Model Solutions¶

Constraint-Based Selection¶

Solution Examples¶

Model Compression¶

Data Efficiency¶

Speed Optimization¶

Output Examples¶

Rich Output¶

JSON Output¶

Best Practices¶

Choosing Tradeoffs¶

Implementation Strategy¶

Production Considerations¶

Common Workflows¶

Optimization Pipeline¶

Feasibility Analysis¶

Tips and Tricks¶

Solution Selection¶

Common Optimizations¶

Avoiding Pitfalls¶

Related Commands¶