Update README.md

README.md

@@ -10,7 +10,7 @@ tags:
 - arfbench
 - observability
 paper:
-- Link to Paper
+- https://arxiv.org/abs/2604.21199
 datasets:
 - Datadog/ARFBench
 leaderboards:
@@ -56,7 +56,7 @@ This model repository stores inference artifacts, including:
 
 The example below assumes you already have:
 
-- time-series tensors (`series`, `padding_mask`, `id_mask`, `timestamp_seconds`, `time_interval_seconds`),
+- time-series tensors,
 - one or more image paths,
 - a text question.
 
@@ -65,10 +65,8 @@ import torch
 from transformers import AutoProcessor
 from qwen_vl_utils import process_vision_info
 
-# Replace this import with your runtime package/module that provides:
-# - TotoAnomalyQAModel
-# - TimeSeriesData
-from your_runtime_package import TotoAnomalyQAModel, TimeSeriesData
+# From our Github repository (https://github.com/DataDog/arfbench)
+from model.toto_vlm_components import TotoAnomalyQAModel, TimeSeriesData
 
 repo_id = "Datadog/Toto-1.0-QA-Experimental"
 
@@ -90,7 +88,7 @@ id_mask = ...  # torch.Tensor, shape: [n_channels, n_timesteps], float/bool
 timestamp_seconds = ...  # torch.Tensor, shape: [n_channels, n_timesteps]
 time_interval_seconds = ...  # torch.Tensor, shape: [n_channels]
 group_names = ...  # list[str], length n_channels
-question = "Which component is most likely causing the anomaly?"
+question = "In the following time-series, does the anomaly in this time-series correlate with the anomaly in the other time-series, if anomalies exist??"
 image_paths = ["./image_1.png", "./image_2.png"]
 
 ts_data = TimeSeriesData(