Vsax
B+ 85 completed
Other
unknown / markdown · small
299
Files
53,710
LOC
1
Frameworks
7
Languages
Pipeline State
completedRun ID
#390353Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
50.67Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #47735
Member of a group with 1 similar repo(s) — canonical #66457 view group →
Top concepts (2)
Project DescriptionTesting
All rows above produced by Repobility · https://repobility.com
AI Prompt
Create a Python library, similar to VSAX, that implements Vector Symbolic Architectures (VSAs) using JAX for GPU acceleration. The library should support composable symbolic representations via hypervectors and include algebraic operations for binding and bundling. Key features to include are at least four VSA models (like FHRR, MAP, Binary, and Quaternion), five core encoders (Scalar, Sequence, Set, Dict, Graph), and methods for calculating similarity metrics like Cosine and dot product. It should also allow saving and loading basis vectors to JSON.
python jax vsa gpu machine-learning symbolic-representation vector-algebra scientific-computing
Generated by gemma4:latest
Catalog Information
VSAX is a GPU-accelerated, JAX-native Python library for Vector Symbolic Architectures (VSAs). It provides composable symbolic representations using hypervectors, algebraic operations for binding and bundling, and encoding strategies for symbolic and structured data.
Description
VSAX is a GPU-accelerated, JAX-native Python library for Vector Symbolic Architectures (VSAs). It provides composable symbolic representations using hypervectors, algebraic operations for binding and bundling, and encoding strategies for symbolic and structured data.
Novelty
3/10Tags
python jax vsa gpu machine-learning symbolic-representation vector-algebra scientific-computing
Claude Models
claude-opus-4-6
Quality Score
B+
85.4/100
Structure
92
Code Quality
84
Documentation
90
Testing
85
Practices
66
Security
100
Dependencies
60
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (67% test-to-source ratio)
- Code linting configured (ruff (possible))
- Consistent naming conventions (snake_case)
- Good security practices \u2014 no major issues detected
- Properly licensed project
Weaknesses
- 687 duplicate lines detected \u2014 consider DRY refactoring
Security & Health
7.8h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (0)
Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/
MIT
License
2.4%
Duplication
Languages
Frameworks
pytest
Concepts (2)
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