ATLAS
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INDEPENDENT AI RESEARCH / KST

Efficient
intelligence.

Engineering efficient AI systems through advanced model compression, adaptive optimization, and intelligent runtime architectures.

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01Researching compression
without compromise.
SYSTEM / ATLAS COREv0.1 / concept
Aadaptive
systems
precisionmemoryruntime
COMPUTE / SIGNAL / CONTROL
01Research focus

Five levers.
One direction.

ATLAS studies the layers between model quality and compute cost. The work is intentionally modular: measure first, compress with context, and keep runtime behavior visible.

01

Adaptive Quantization

Precision follows sensitivity, not habit.

02

Error Compensation

Approximation remains visible and measurable.

03

Unified Memory Runtime

Movement is part of the model of computation.

04

GPU Optimization

Throughput is evaluated with transfer cost.

05

Intelligent Execution

Scheduling is constrained by evidence.

02System view
Explore the architecture

From model
to movement.

The useful unit of optimization is not a single tensor or kernel. It is the path a signal takes through a constrained system.

01Model input
02Quantization planner
03Memory runtime
04Execution scheduler
05Model output
SIGNAL PATH / DECLARED STATEExperimental
03Research posture

Unverified is
a valid status.

ATLAS does not fill evidence gaps with attractive numbers. Benchmark values, implementation states, and system claims are designed to be updated when reproducible material exists.

Inspect benchmark status
EVIDENCE BOARDLIVE / 2026
Model sizenot reported—
Memory usagenot reported—
Inference speednot reported—
Measurement contract pending
Public claims follow reproducible evidence.
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Build a more efficient
intelligence layer.

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