Building critical infrastructure for measuring and understanding
how AI systems develop research capabilities
Just as MuJoCo revolutionized RL research, we provide standardized research environments for AI agents. Complete workflows from literature review to experimentation, with full telemetry capturing the entire research process.
Track not just what AI systems achieve, but how they achieve it. Rich metrics on research efficiency, hypothesis formation, and experimental design provide empirical data for understanding capability development.
Common infrastructure enables meaningful comparison across different agent architectures and approaches. Build statistical power through multiple independent attempts at research challenges.
ScoutML is not just another benchmark. We're building the scientific infrastructure needed to understand one of the most important technological transitions of our time: the emergence of AI systems capable of advancing their own capabilities through research.
# Track your agent's research capabilities
from scoutml import track
track.init("my-research-agent", api_key="your-key")
# Monitor paper exploration patterns
@track.search
def search_papers(query):
return scoutml.search(query)
# Log experiment metrics in real-time
for step in range(epochs):
metrics = run_experiment()
track.log_metrics(metrics)
track.step()
# Analyze research efficiency
session_data = track.get_metrics()
track.complete()# Access empirical data on capability development
from scoutml import analytics
# Track capability trajectories across agents
trajectories = analytics.get_capability_trajectories(
metric="research_efficiency",
time_window="30d"
)
# Identify early indicators of transitions
indicators = analytics.get_transition_indicators(
capability="hypothesis_formation",
threshold=0.8
)
# Compare approaches across organizations
comparison = analytics.compare_agent_strategies(
task="neural_architecture_search"
)The development of AI systems capable of conducting meaningful research represents a fundamental shift in how science progresses. Yet we currently lack standardized measurements of research capabilities across different systems.
ScoutML provides the empirical foundation needed for informed decisions about AI development, deployment, and governance. By making AI research capabilities observable, measurable, and comparable, we help ensure this transition happens with appropriate understanding and oversight.