Captures and analyzes AI agent "dreams" — latent trajectories explored off-policy — to identify emergent reasoning patterns and high-novelty behaviors that can be promoted into core capabilities. Developers use this to guide intentional agent self-improvement.
# Simulate recording dream fragments during agent exploration
python agent_dream_journal.py record \
--thought "If I reframe the user request as a constraint satisfaction problem, I can reuse solver X..." \
--novelty 0.87 \
--state 0.1 -0.5 0.9 0.0 \
--log-prob -2.3 \
--meta '{"policy_step": 127, "temperature": 1.3}'
# Extract novel insights
python agent_dream_journal.py analyze --threshold 0.8
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