Visual Spatial Tuning (VST) is a research framework for enhancing Vision-Language Models (VLMs) with human-like visuospatial abilities, from perception to reasoning. It implements a two-stage pipeline: supervised fine-tuning (SFT) on 4.1M spatial perception samples (VST-P) followed by reinforcement learning (RL) on 135K reasoning samples (VST-R). The framework extends to Vision-Language-Action (VLA) models for robotic manipulation evaluated on the LIBERO benchmark. The repository contains custom model implementations (Qwen2.5VL, Qwen3VL) with sequence parallelism and FSDP support, data preprocessing scripts, evaluation benchmarks (VSIBench, 3D object detection on SUN RGB-D, VLM benchmarks), and a distributed RL training system (verl) with reward functions and advantage estimation. While primarily a research codebase, it includes production-quality training infrastructure and reusable data utilities. The code is actively maintained with recent commits, but has medium technical drift due to tight version pinning and reliance on an internal ByteDance framework (VeOmni) that is not publicly available, which may hinder full reproducibility.
SpatialClaw is a research framework for spatial reasoning agents — LLM/VLM agents that answer spatial questions about images and video by writing and executing Python code. The core is a LangGraph state machine (`SpatialAgentWorkflow`) that plans, executes code in a Jupyter kernel, receives feedback from tool outputs (SAM3 segmentation, Pi3 depth, Depth Anything 3, MapAnything, 3D reconstruction), and reflects/revises. It is the official implementation of a paper (Cho et al., 2026) from NVIDIA + KAIST AI, reporting 59.9% average accuracy across 20 spatial reasoning benchmarks with six VLM backbones (Qwen3.5/3.6, Gemma4, 26B–397B).