01 / POST-TRAINING
Post-Training
Investigating reinforcement learning, fine-tuning, distillation, and model specialization to advance reasoning, coding, and task-specific capabilities.
Reinforcement learning · Fine-tuning · Distillation · Evaluation
Our mission
Accelerating
intelligence.
We develop methods to make AI models more capable, efficient, and faster to run.
Explore our missionMission
Developing systems that
move AI forward.
We are developing Sinefour Intelligence, a unified research initiative connecting post-training, intelligence optimization, and inference acceleration. Our aim is to advance what models can do while reducing the compute, memory, and time they need.
Aiming for more capable AI with less compute. Early-stage research; not a claim of production-ready performance.
Research
From ideas to systems.
One research initiative across three connected directions: improving capability, optimizing computation, and accelerating execution.
02 / OPTIMIZATION
Intelligence
Optimization
Exploring model compression, quantization, adaptive optimization, and hardware-aware optimization to improve AI performance and computational efficiency.
Model compression · Quantization · Adaptive optimization · Hardware-aware optimization
03 / INFERENCE
Inference
Acceleration
Exploring high-throughput token generation through compute-memory co-design, optimized dataflow, compiler-driven execution, and specialized inference architectures.
Token generation acceleration · Memory-centric computing · Dataflow optimization · Specialized accelerators
Sinefour IntelligenceOne unified research initiativePost-TrainingImprove model capabilitiesIntelligence OptimizationOptimize computationInference AccelerationAccelerate execution
Selected work
Research in motion.
Early-stage experiments and prototypes across AI execution and engineering. This work informs our direction; it is not a claim of production-ready performance.
EXPLORATION
Searching the design space
Architecture-search experiments explore tradeoffs across throughput, power, physical constraints, and model-specific execution. Results are analytical estimates, not silicon measurements.
PROTOTYPING
From architecture to RTL
We have built and exercised RTL components, simulation workflows, and early physical-design flows to connect architectural ideas to implementable hardware.
EVALUATION
Grounding experiments in real models
Work with an open-weight coding model includes a GPU inference baseline and a separate RTL-simulation effort, supporting a more disciplined path toward future comparisons.
The Sinefour approach
Explore · Evaluate · Learn · Optimize
Contact
Build what's next.
Research conversations, collaborations, and new ideas start here.