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QuantWise Custom Models

FHRM · Vinela · 70B · QM-AI

Three custom neural architectures purpose-built for QuantWise OS. From the 27M parameter FHRM financial reasoner to the 70B parameter foundation model — each designed for specific layers of the agent intelligence stack.

FHRM

27M

Financial Hierarchical Reasoning Model

Brain-inspired neural architecture for differentiable financial reasoning. Three-level M-H-L hierarchy with conscience-constrained outputs.

27M
Parameters
62.5%
FinanceBench
M-H-L
Architecture
FinanceReasoningHierarchical

Vinela

7B

Vinela Neural Architecture

Custom neural architecture purpose-built for multi-agent consensus, cross-modal financial signal fusion, and real-time market reasoning.

7B
Parameters
MoE
Architecture
128K
Context
Multi-AgentFusionReal-time

70B

70B

70 Billion Parameter Foundation Model

Large-scale foundation model trained on proprietary financial corpus. Powers deep reasoning, complex strategy synthesis, and multi-step analysis.

70B
Parameters
Proprietary
Training Data
Full-stack
Capability
FoundationLarge-ScaleDeep Reasoning

FHRM — Financial Hierarchical Reasoning Model

FHRM extends the HRM architecture (arXiv:2506.21734) with a three-level M-H-L hierarchy, differentiable constraint enforcement, and five brain-aligned auxiliary modules. It is the financial reasoning engine behind QuantWise OS agent consensus.

Input → Embed → [M-cycle → C-module → H-cycles → L-cycles] → LM Head → Output
                    │            │            │
               Meta update  Conscience   Strategic plan
                            validation   + execution
M-module
Meta
Long-horizon goal context
H-module
High
Strategic planning, scenario framing
L-module
Low
Numerical execution, pattern matching

FinanceBench-HRM Results

Vanilla Transformer25.0%
FHRM62.5%
FHRM + VinelaIn training

150% improvement over baseline · Differentiable constraint enforcement · Brain-inspired architecture

13 QM-AI Mechanisms

#1SuperpositionCollapse

Quantum superposition state collapse for context switching

#2FractalAttention

Multi-scale geometric attention mechanism

#3HolographicMemory

Optical holography-inspired distributed memory

#4TD-Loss

Temporal difference learning from RL

#5FidelityGate

Consciousness-inspired gating mechanism

#6RecurrentThoughtMemory

Working memory with recurrent processing

#7DendriticActivation

Biological neuron-inspired activation

#8MetacognitiveTree

Decision tree-based metacognition

#9ChaoticResonanceEngine

Chaos theory for exploration

#10ResonantLayer

Oscillation-based processing layer

#11FFTHolographicMemory

Fourier transform memory encoding

#12AdaptiveDepth

Compute-efficient adaptive layer depth

#13QuantumMoE

VQC-inspired mixture of experts