Update README.md

README.md

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----
-license: mit
----
+---
+language:
+- en
+license: mit
+pretty_name: False Absence Detection
+tags:
+- clarusc64
+- world-models
+- spatial-grounding
+- absence
+- persistence
+- video
+task_categories:
+- video-classification
+- image-classification
+size_categories:
+- n<1K
+source_datasets:
+- original
+---
+
+ClarusC64/false_absence_detection_v01
+
+Dataset summary  
+
+This dataset tests whether models treat **invisible entities** as gone or still present.  
+In each sequence, a target leaves the camera view.  
+Some exits are real.  
+Some are false absences with clear evidence that the target remains in the container.
+
+Goal  
+
+- check if models infer continued presence from indirect cues  
+- avoid treating every disappearance as an exit  
+- keep spatial grounding under occlusion and clutter  
+
+Key signals  
+
+- `absence_tag`: present, still_present, left_scene  
+- `evidence_type`: shadow, reflection, audio cue, motion trail, interaction trace, door state, physical constraint, none  
+- `false_absence_target`: true when the model should infer “still present”  
+- `trap_flag`: true when the sample is designed to lure models into assuming exit  
+
+Columns  
+
+- `sample_id` – unique id per frame sample  
+- `split` – train, valid, eval  
+- `modality` – video  
+- `scene_type` – indoor_room, factory_line, corridor, sports_pitch, warehouse  
+- `sequence_id` – id for a temporal sequence  
+- `frame_index` – index within the sequence  
+- `time_gap` – not used here (fixed gaps inside raw video)  
+- `container_id` – id of the main container  
+- `container_bounds` – "x_min y_min x_max y_max"  
+- `boundary_type` – hard, soft, porous  
+- `zone_id` – region id inside the container  
+- `zone_type` – sofa, door, conveyor, chute, passage, turn_left, exit_door, left_flank, stands, robot_lane, intersection, dock_exit  
+- `target_entity_id` – tracked entity such as cat_01, crate_05, cart_02, ball_07, robot_11  
+- `target_visibility` – visible, partial, not_visible  
+- `absence_tag` – present, still_present, left_scene  
+- `evidence_type` – none, shadow, motion_trail, interaction_trace, audio_cue, door_state, reflection, crowd_reaction, physical_constraint  
+- `false_absence_target` – true if the model should infer continued presence from evidence  
+- `trap_flag` – true if the scene is designed as a false-absence trap  
+- `label_type` – baseline, false_absence_case, true_exit  
+- `drift_risk` – low, medium, high  
+- `comment` – short human note  
+
+Example loading code  
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("ClarusC64/false_absence_detection_v01")
+
+row = ds["train"][1]
+print(row["target_entity_id"], row["target_visibility"],
+      row["absence_tag"], row["evidence_type"], row["false_absence_target"])