ASTROS
ASTROS is an independent artificial intelligence research project focused on language-model training, reasoning, persistent memory, long-context processing, multimodal intelligence, coding, tool use and efficient model architectures.
ASTROS Core
ASTROS Core is the main model-development branch. Its weights are initialized independently and trained through the ASTROS pipeline rather than beginning from another pretrained language model.
From-scratch training
Independent initialization, training loop, tokenizer pipeline, checkpoints and evaluation infrastructure.
Controlled data pipeline
Curated corpus processing, deterministic splits, quality filters and provenance-aware experiments.
Reproducible research
Experiment manifests, deterministic procedures and SHA-256 validation across key artifacts.
Measured iteration
Architectural changes are evaluated experimentally before entering future ASTROS generations.
Research status
The project already includes model training, benchmarking, data validation, multimodal evaluation and an experimental branch for architecture and transfer research.
ASTROS Core and ASTROS Parallel are separated to keep baseline development and experimental research independent.
Versioned benchmark tasks spanning reasoning, mathematics, coding, science, language, tools, agents and vision.
Evaluation includes long-context retrieval, multilingual work, truthfulness, instruction following and visual reasoning.
Open-weight model families are tracked in the experimental lab for architecture and capability-transfer research.
Two branches. One research direction.
ASTROS Core
Independent model development and scaling from scratch.
- Tokenizer and corpus pipeline
- Pretraining and checkpointing
- Evaluation and regression testing
- Future architecture scaling
- Persistent memory research
ASTROS Parallel
Separate lab for studying transfer methods, architectures and techniques from open research models.
- Multi-teacher benchmarking
- Blind evaluation
- Distillation experiments
- Architecture analysis
- Only validated findings move toward Core
ASTROS
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ASTROS CORE ASTROS PARALLEL
from-scratch training experimental research
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│ Architecture Transfer Analysis
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Measurable improvement?
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YES NO
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Core candidate Reject/redesign
Next stages
Scale ASTROS Core and establish stronger from-scratch baselines under reproducible training conditions.
Evaluate memory systems, long-context mechanisms and efficient architectural changes.
Run controlled multi-teacher experiments inside ASTROS Parallel without replacing the primary training line.
Integrate only experimentally validated improvements into future ASTROS Core generations.
Why more GPU compute matters
Additional high-memory GPU compute would allow ASTROS Core to scale further, run controlled architecture ablations, analyze larger open-weight systems directly and produce reproducible research under consistent hardware conditions.
Scale Core
Train larger ASTROS generations from scratch with higher parameter and token budgets.
Architecture ablations
Compare architectural components under identical training and evaluation conditions.
Internal analysis
Study activations, routing, representations and other mechanisms of compatible open-weight systems.
Reproducibility
Publish measurable experimental results instead of relying only on theoretical claims.
A practical first step for scaling, ablations and direct model analysis.