Benchmark Deep Dive — Technical Overview
For CTOs & ML Engineering Leads | March 2026
ARYA Labs PBC — Confidential
Technical Brief
This document presents rigorous benchmark results across 16 evaluation domains, comparing ARYA's composable architecture against leading foundation models including V-JEPA 2 (AMI), DeepSeek-R1, GPT-4, and Claude Opus 4.6.
Architecture
ARYA's 6-Layer Composable Architecture
A fully composable, layered system designed for deterministic performance, safety, and vertical specialization — with zero neural network parameters at its core.
L0 — Infrastructure
APIs, GPU, Kubernetes, VPC — the foundational compute and networking substrate.
L1 — Orchestration
AARA daemon, Safety Kernel, Constraint Layer — unfireable safety enforcement at the core.
L2 — World Model
Nano ensemble, Context Net, Belief Net — physics-grounded world representation.
L3 — Learning
Meta-learning, genetic algorithms, RSI — continuous self-improvement without retraining.
L4 — Capabilities
7 autonomous engines: Discovery, Invention, Constraint Breaker, Vertical Builder, Code Synthesizer, AI Model Generator, Cybersecurity.
L5 — Verticals
Pharma, aerospace, oil & gas, defense — domain-specific deployment at production scale.
Methodology
Benchmark Methodology & Rigor
Every result is statistically validated across multiple replicates, with ARYA achieving full determinism on nearly half of all benchmarks.
5×
Replicates Per Benchmark
Statistical validation across all 16 benchmarks ensures reproducibility and eliminates noise.
16
Total Benchmarks
Spanning 10 distinct evaluation domains from causal reasoning to video understanding.
7/16
Fully Deterministic
0.00 standard deviation on 7 of 16 benchmarks — a property impossible in stochastic LLMs.
99.34%
Mean Accuracy
Across all production models in the evaluation suite.
527ms
End-to-End Latency
Full request flow from API ingestion to formatted response delivery.
Evaluation Suite
Benchmarks 1–8: Evaluation Domains
The first half of the evaluation suite spans causal reasoning, physics, PhD-level science, workflow performance, robotics navigation, and AI safety.
Evaluation Suite
Benchmarks 9–16: Video & Temporal Understanding
The second half of the suite focuses heavily on video comprehension, temporal reasoning, and multi-modal perception — domains where traditional LLMs struggle fundamentally.
Head-to-Head
ARYA vs. V-JEPA 2 — Robustness Part 1
V-JEPA 2 powers AMI — Yann LeCun's $3.5B startup ($1.03B seed round). ARYA beats AMI's foundation model on 13 of 15 benchmarks with zero neural network parameters vs. 300M–1.2B.
Head-to-Head
ARYA vs. V-JEPA 2 — Robustness Part 2
Continuing the head-to-head comparison across causal, physics, and perception benchmarks. Final score: ARYA 13 — V-JEPA 2: 2.
🏆 Final Score
ARYA 13 — V-JEPA 2: 2
Zero NN Parameters
ARYA achieves this with 0 neural network parameters vs. 300M–1.2B in V-JEPA 2.
Full Determinism
0.00 std dev across all ARYA results — stochastic variance is structurally eliminated.
Architecture Comparison
87.5% Sparse Activation & Cross-System Comparison
Sparse Activation Advantage
Only 0.0001% of ~542K models activated per query.
25 MB memory footprint vs. 2,475 MB dense equivalent.
99× memory reduction — sub-second at massive scale without GPU clusters.
Production Performance
Production Performance & End-to-End Request Flow
0.0002ms
P50 Inference
0.0007ms
P99 Inference
3.28ms
Z3 Verify P50
0.43MB
Median Model Size
64.6/hr
Training Throughput
End-to-End Request Flow (~527ms total)
01
API → Event Bus
~5ms ingestion and event dispatch
02
Queue
≤100ms buffering and prioritization
03
Entity Extraction
~450ms — natural language to structured goal
04
DECIDE (6-Stage Routing)
~5ms — model selection and dispatch
05
Solver Execution
~12–45ms — physics-grounded computation
06
Constraint Validation (Z3) + Lineage + Format
~2ms + ~3ms + ~5ms — verify, record, respond
Safety & Compliance
Safety Gauntlet, Compliance & Solver Ecosystem
5-Stage Safety Pipeline
1
S1 Static
~1ms — rule-based pre-flight checks
2
S2 Formal (Z3)
~3ms — mathematical proof of constraint
3
S3 Kernel
~10ms — unfireable safety kernel enforcement
4
S4 Sandbox
~1–10s — isolated execution environment
5
S5 Regression
~10–60s — full regression suite validation
100% Grade A Safety — 40/40 bypass attempts blocked.
Regulatory Compliance
EU AI Act · FDA 21 CFR Part 11 · NIST AI RMF · ICH Guidelines · W3C PROV-DM · GDPR
25+ Solver Packages
Physics-first computation across 10 constraint domains:
Quantum
QuTiP, QMsolve
Materials
pycalphad, Pymatgen, ASE, OVITO
CFD / Fluids
OpenFOAM, SU2, Basilisk, PySPH
FEM / Structural
FEniCS, Firedrake, Code_Aster, Elmer
Molecular Dynamics
LAMMPS, GROMACS, OpenMM
Scientific Compute
NumPy, SciPy, JAX, CuPy, RAPIDS, DeepXDE
10 Physics Constraint Domains
Mechanics · Thermodynamics · CFD/Fluids · Electromagnetics · Quantum · Biophysics · Materials · Acoustics · Optics · Nuclear
ARYA Labs PBC — Confidential. All benchmark results validated across 5× replicates with full statistical documentation available upon request.