We lifted 16 functions out of this paper's own repositories and ran 12 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| microsoft/Magma | canonical | 12 of 16 |
| Function | Status | Where it lives |
|---|---|---|
| IoU | Ran | microsoft/Magma/agents/ui_agent/util/box_annotator.py code served (permissive licence) · get_code("9a3400bbcbc29023") |
| box_area | Ran | microsoft/Magma/agents/ui_agent/util/box_annotator.py code served (permissive licence) · get_code("f631c3a13425e610") |
| draw_circle | Ran | microsoft/Magma/agents/robot_traj/utils/visualizer.py code served (permissive licence) · get_code("7f5963ad8bf61e08") |
| draw_line | Ran | microsoft/Magma/agents/robot_traj/utils/visualizer.py code served (permissive licence) · get_code("5caa64d42eda8676") |
| get_libero_obs | Ran | microsoft/Magma/agents/libero/libero_env_utils.py code served (permissive licence) · get_code("bc05055a3a84f2c2") |
| get_magma_prompt | Ran | microsoft/Magma/agents/libero/libero_magma_utils.py code served (permissive licence) · get_code("5e5dfc359305fb78") |
| intersection_area | Ran | microsoft/Magma/agents/ui_agent/util/box_annotator.py code served (permissive licence) · get_code("23efd84e20b32497") |
| process_anyres_image | Ran | microsoft/Magma/magma/image_processing_magma.py code served (permissive licence) · get_code("691aa4a9f8305f89") |
| process_anyres_image_global | Ran | microsoft/Magma/magma/image_processing_magma.py code served (permissive licence) · get_code("d132cfaf9e0185f1") |
| resize_image | Ran | microsoft/Magma/agents/libero/libero_env_utils.py code served (permissive licence) · get_code("090fa82a9a0f5b4b") |
| select_best_resolution | Ran | microsoft/Magma/magma/image_processing_magma.py code served (permissive licence) · get_code("3999ff487573f32c") |
| split_to_even_chunks | Ran | microsoft/Magma/trainer/trainer.py code served (permissive licence) · get_code("10893c4608c08075") |
| get_magma_action | Not yet run | microsoft/Magma/agents/libero/libero_magma_utils.py code served (permissive licence) · get_code("d31031274c15dc34") |
| get_mm_adapter_state_maybe_zero_3 | Not yet run | microsoft/Magma/trainer/trainer.py code served (permissive licence) · get_code("bb35e3ac741bb2c9") |
| maybe_zero_3 | Not yet run | microsoft/Magma/trainer/trainer.py code served (permissive licence) · get_code("735025744c1ab0cf") |
| read_video_from_path | Not yet run | microsoft/Magma/agents/robot_traj/utils/visualizer.py code served (permissive licence) · get_code("574c10b6f4d920f3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Figure 1. We introduce Magma, the first foundation model that is capable of interpreting and grounding multimodal inputs, and taking actions towards a goal in both digital and physical environments. With our newly proposed pretraining techniques, Magma learns effectively from images, videos and robotics data to bridge verbal and spatial intelligence, taking a step further to an intelligent multimodal AI agent.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2502.13130")
get_code_for_paper("2502.13130")
have("2502.13130")
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