Post
21
VQASynth is the open source implementation of the SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning
Capabilities (2401.12168) paper, putting together the data synthesis pipeline behind remyxai/SpaceQwen2.5-VL-3B-Instruct, remyxai/SpaceThinker-Qwen2.5VL-3B, and several other spatial reasoning models we've shared on here on HF.
Here's how we use Remyx AI to build and improve VQASynth from the original concept forward.
Stage 1: When you connect a repo to Remyx, we extract development milestones from the commit history. For VQASynth, that surfaces the moments we changed how scenes get parsed, how captions get generated, how spatial relations get encoded. Those milestones power personalized recommendations for methods semantically relevant to improving your system.
Stage 2: When the model is serving in production, that same commit history delineates changes so you can learn from quasi-experiments through observational outcomes. This generates causal evidence about which changes drove which outcomes, sharpens recommendations, and supports inference on questions you haven't directly tested.
Stage 3: Once teams are running controlled experiments, the intervention outcomes tighten those estimates further.
Stage 4: When A/B testing becomes the operational bottleneck, we instrument decision points in the production system to explore via counterfactual perturbations. Initially in shadow mode, and after passing audits, with live traffic.
If you want recommendations tuned to your own project context, you can set up a feed here: https://docs.remyx.ai/platform/discover/feed
Here's how we use Remyx AI to build and improve VQASynth from the original concept forward.
Stage 1: When you connect a repo to Remyx, we extract development milestones from the commit history. For VQASynth, that surfaces the moments we changed how scenes get parsed, how captions get generated, how spatial relations get encoded. Those milestones power personalized recommendations for methods semantically relevant to improving your system.
Stage 2: When the model is serving in production, that same commit history delineates changes so you can learn from quasi-experiments through observational outcomes. This generates causal evidence about which changes drove which outcomes, sharpens recommendations, and supports inference on questions you haven't directly tested.
Stage 3: Once teams are running controlled experiments, the intervention outcomes tighten those estimates further.
Stage 4: When A/B testing becomes the operational bottleneck, we instrument decision points in the production system to explore via counterfactual perturbations. Initially in shadow mode, and after passing audits, with live traffic.
If you want recommendations tuned to your own project context, you can set up a feed here: https://docs.remyx.ai/platform/discover/feed