FROLIM

FROLIM is the next step beyond LLMs: moving AI from generating answers to understanding, predicting, and acting on the world. Built on a brain-inspired cognitive architecture, FROLIM enables AI to learn from experience, identify why its predictions fail, and correct its understanding in a single learning cycle—creating systems that not only know and communicate, but anticipate consequences, adapt to change, and interact intelligently with the real world.

Four family members

FROLIM_01: Cognitive Governance API

Governed memory — a salience write-gate decides what experience becomes knowledge
Causal failure diagnosis — prediction-error arbitration
One-trial correction — a single correction permanently changes behavior
Consolidation without forgetting — memory is compressed and reorganized over time
Full audit trail — every write, correction, and decision is versioned and inspectable
Model-agnostic by design — attaches to any host LLM or framework; swap the model, keep the memory and corrections.

FROLIM_02: Data privacy-centric SMB agent

Memory — a salience write-gate decides what business experience becomes knowledge (clients, prices, procedures, tone)
One-trial correction — a single correction permanently changes customer-facing behavior
Causal failure diagnosis — prediction-error arbitration: wrong belief, failed tool, or changed world, with an honest “unknown”
FROLIM-side data ownership — the knowledge store lives on the business’s side, not in any provider’s cloud; providers see only minimal task slices
Model-agnostic by design — attaches to any host LLM via API; swap the model, keep the knowledge and corrections
Full audit trail — every write, correction, and decision is versioned, inspectable, and exportable
Scoped permissions — per-action approval gates; sensitive-tagged knowledge never leaves, or routes only to local/EU models

FROLIM_03: World Model add-on for LLM

LLM as input — the host LLM parses language into structured world knowledge: entities, relations, and events enter the world model as an explicit, inspectable graph
LLM as output — the world model’s predictions, plans, and corrections are rendered back through the host LLM as language; the model speaks, the world model knows
Discovered state space — the world model infers its own states and rules from what happens, instead of inheriting a fixed schema
Parametric failure attribution — when a prediction fails, arbitration names the cause: wrong belief, failed tool, or changed world — and points at the specific parameter at fault
One-trial world updates — a single disconfirmation revises exactly one parameter, in one event, without retraining
Imagination before action — planning runs as rollouts over the world graph: consequences are foreseen and confidence-scored before the LLM commits an answer or an agent acts
Model-agnostic by design — attaches to any host LLM via API; swap the model, keep the world model, its history, and its corrections

FROLIM_04: Multimodal World Model (vision in, renderable imagery out)

Visual + language input — camera and text feed one world model: perception turns scenes into physics-annotated graphs, language turns meaning into the same graph
Optical output — images and video are rendered from the world graph, not sampled from diffusion noise
Generation with provenance — each output frame is auditable against the beliefs that produced it
Parametric failure attribution, made visible — when a rendered prediction is wrong, arbitration points at the parameter
One-trial revision, end to end — a single disconfirmation updates the world parameter and the next render reflects it immediately, without retraining
Consistent long rollouts — multi-step predictions and rendered scenario videos stay stable over 100+ steps because dynamics run on the graph, not on pixels

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