De novo concept creation for brain-modeled agentic AI — the 200th LegionIO cognitive extension. Seeds proto-concepts, germinates them into novel cognitive categories, and tracks their adoption.

Langacker-inspired cognitive grammar engine for LegionIO — construal operations, construction entrenchment, and meaning as construal

Attractor basins in thought space — cognitive gravity wells for LegionIO agentic AI

Protected environment for growing ideas from seeds to mature concepts — temperature, humidity, and light as nurturing conditions, with seasonal cycles

Holographic memory model for the LegionIO cognitive architecture — each fragment contains information about the whole, enabling partial recall, interference pattern detection, and reconstruction from fragments

Maintains balance across cognitive variables via setpoint tracking, deviation detection, and automatic correction for LegionIO agents.

Temporal planning horizon modeling for brain-modeled agentic AI — dynamic expansion/contraction based on stress, success, and construal level theory

Time-bound cognitive resource model for brain-modeled agentic AI — sand grains flow through a narrowed neck, depleting attention resources until the hourglass is flipped to renew the cycle

T-cell/B-cell persistent threat recognition with secondary response amplification for the LegionIO cognitive architecture

Models an adaptive immune system for AI agents. Detects adversarial inputs, builds learned immunity through antigen/antibody pattern matching, and provides graduated immune responses from tolerance to full rejection.

Cognitive immune system for brain-modeled agentic AI — defense against memetic threats, manipulation, and cognitive viruses

Models resistance to changing established beliefs and cognitive patterns

Models convergence zones that bind information from different cognitive modalities into unified multi-modal representations with binding strength and coherence tracking.

Models perspective rotation and pattern emergence through facet brilliance and symmetry dynamics for brain-modeled AI agents

Maze-like problem spaces for agentic reasoning — paths, dead ends, backtracking, breadcrumb trails, Ariadne's thread (guiding heuristic), and Minotaur encounters (dangerous misconceptions)

Cognitive lenses that filter, focus, or distort perception for brain-modeled agentic AI

Models guiding beacons that periodically sweep their beam to illuminate areas of uncertainty, helping navigate complex decision spaces. Beacons can be steady or rotating, can dim in fog, and can guide other agents through ambiguity.

Models liminality — the creative in-between states during cognitive mode transitions with ambiguity, dissolution, and crystallization

Sweller's Cognitive Load Theory modeled for brain-based agentic AI: intrinsic, extraneous, and germane load tracking with capacity management

Dynamic distribution of cognitive work across subsystems with overload detection, auto-assignment, and rebalancing.