A Fluid Goal Modeler (FGM) is a targeted, self-updating research agent built around our architecture:
•AIManager AutonomousMind coordinate cognitive mode (analytical/creative/reflective) and keep the loop coherent.
•PlannerSifter → TemporalPlanner turns a goal into an evolving multi-horizon plan (explore, deepen, synthesize), then continuously re-schedules steps as evidence changes.
•DomainIntelligence FreeWill select where to look next (sources, URLs, environments), balancing novelty, authority, and expected value.
•ContentSifter filters noise and extracts high-signal claims, producing clean evidence units.
•Neural core HyperMorphic substrate (Φ–Ψ, ε zero-free) trains task-specific models and stabilizes representation shifts as the domain evolves.
•SemanticMemoryModule stores embeddings/keywords/graph links so learning accumulates rather than resets.
•ImaginationEngine generates counterfactuals, detects contradictions, and proposes new tests to shrink uncertainty.
•MetaLearning AdaptiveLearning MetaEvolution tune learning dynamics and, when needed, restructure strategies/modules over longer timescales.
•Checkpointing Dashboard make progression persistent, inspectable, and recoverable.
Definition (tight):
An FGM is an agent architecture that repeatedly plans → acquires evidence → quality-filters → trains targeted models → updates memory and strategy → re-plans across time horizons, using self-tuning and evolution to reduce uncertainty and improve decisions in a specific domain.