TypeScript multi-agent orchestration engine — one runTeam() call from goal to result. Multi-model teams, auto task decomposition, parallel execution. 3 runtime dependencies.

Welcome to the official repository of SINQ! A novel, fast and high-quality quantization method designed to make any Large Language Model smaller while preserving accuracy.

Your AI friend right in your browser

OS-agnostic, model-agnostic desktop automation server. Gives any AI agent eyes, hands, and ground-truth verification on Windows, macOS, and Linux.

mkdir beats vector DB. B-tree NeuronFS: 0-byte folders govern AI — ₩0 infrastructure, ~200x token efficiency. OS-native constraint engine for LLM agents.

This is an official repository for "LAVA: Data Valuation without Pre-Specified Learning Algorithms" (ICLR2023).

Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University

Dragon Brain — persistent long-term memory for AI agents via MCP (Model Context Protocol). Knowledge graph (FalkorDB) + vector search (Qdrant) + CUDA GPU embeddings. Works with Claude, Gemini CLI, Cursor, Windsurf, VS Code Copilot. 30 tools, 1121 tests.

The scaffold for your ultra-personalized, multi-model AI harness in pure natural language.

Framework de Ontologia Operacional — Pin/Spec/Handoff protocol for stateful AI agents. CC BY 4.0.

[ICLR24] "AutoVP: An Automated Visual Prompting Framework and Benchmark" by Hsi-Ai Tsao*, Lei Hsiung*, Pin-Yu Chen, Sijia Liu, and Tsung-Yi Ho.

Official implementation of FedGAT: Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction (https://arxiv.org/abs/2502.04521)

✨ Official code for our paper: "Uncertainty-o: One Model-agnostic Framework for Unveiling Epistemic Uncertainty in Large Multimodal Models".

Official project website for the AAAI 2022 paper "Stereo Neural Vernier Caliper"

NeurIPS 2025: Graph Your Own Prompt

A model-agnostic library for generating explanations of machine learning predictions, supporting diverse XAI methods like CEM and LIME.

Post-hoc prototype-based explanations with rules for time-series classifiers

Robust regression algorithm that can be used for explaining black box models (Python implementation)

Codebase for CIKM '24 paper -- PARs: Predicate-based Association Rules for Efficient and Accurate (Model-Agnostic) Anomaly Explanation

Robust regression algorithm that can be used for explaining black box models (R implementation)