Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
The English summary is being prepared. Showing the original description.
OpenRaiser/NanoResearch☆ 1.3KUpdated Oct 9, 2026
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
The English summary is being prepared. Showing the original description.
OpenRaiser/NanoResearch☆ 1.3KUpdated Oct 9, 2026
Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.
The English summary is being prepared. Showing the original description.
OpenRaiser/NanoResearch☆ 1.3KUpdated Oct 9, 2026
Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.
The English summary is being prepared. Showing the original description.
OpenRaiser/NanoResearch☆ 1.3KUpdated Oct 9, 2026
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
The English summary is being prepared. Showing the original description.
OpenRaiser/NanoResearch☆ 1.3KUpdated Oct 9, 2026
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
The English summary is being prepared. Showing the original description.
OpenRaiser/NanoResearch☆ 1.3KUpdated Oct 9, 2026