arXiv (All)
open access
Numbat: Building and Verifying a Self-Contained Machine-Learning Stack
Machine-learning systems are built almost exclusively on a few large Python-orchestrated frameworks, and they inherit those stacks' engineering costs: environments of hundreds of version-coupled packages, separate export toolchains for deployment, and the split between the language research is writt
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling#1000403
- TTSR: Test-Time Self-Evolving via Reflection#1014626
- Code Is Not the Answer: On Systemic Output-Modality Bias in Large Language Models#690046
- RLX: A Unified Multi-Backend Tensor Compiler and Distributed Runtime in Rust#1142221
- RLX: A Unified Multi-Backend Tensor Compiler and Distributed Runtime in Rust#1122249
Record · ID 997632
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive