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title: Saccadic | IRIS Training Observations
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short_description: A closer look inside neural network training with IRIS
IRIS™ · A closer look at AI training
A better view while the run is still underway.
Saccadic LLC develops instruments for observing neural network training. IRIS, the Intrinsic Rank Inspection System, adds a measured view of internal structure, specialization, and coordination alongside loss and validation curves.
Explore the styled IRIS Space · IRIS product details · Discuss an evaluation
Look inside the run
- Flow Map: inspect measured connections between layers and experts for different input domains.
- Representation structure: observe how activation variation concentrates and how dominant directions move.
- Expert routing: compare which subjects reach which experts, separately for each layer.
- Domain interaction: inspect sharing and training-gradient agreement alongside loss and task results.
Capture. Record. Inspect.
The tracker captures measurements using consistent probe inputs. A self-contained .iris file records the run. The local Bench reads that growing record during training and opens it afterward without loading the model.
Each graph has an Explain guide. Teams can define labeled scalar signals, derive formulas from captured channels, and export custom charts.
Follow the signal through the model
Figure 10.1. A snapshot during training at step 49,300. Green links show mechanics; orange links show arithmetic. Line thickness represents measured signal, and expert circles are sized by traffic share. This is a static image of the IRIS Flow Map.
The map provides an internal view alongside the accuracy curves: which experts carry each domain’s measured signal, where paths overlap, and how traffic is distributed across layers. Revisit these measurements across the recorded run to inspect how the patterns change.
A measured example
Figure 4.4. Training and held-out accuracy for the mathematical operations in this measured run. Read the experiment and its conditions.
Work in your own environment
IRIS fits existing PyTorch training loops, with capture and inspection on CPU, Apple MPS, or NVIDIA CUDA. Coverage depends on model layout; shared-weight configurations are currently unsupported.
Model weights, activations, and probe text are not sent to Saccadic. Licensing and metering contact Saccadic. Optional configured relays send selected scalar measurements to existing experiment trackers.
One model. One run. One concrete investigation.
We welcome research and joint evaluations. Academic researchers can request credit grants; requests are reviewed individually. See the public product site for availability and licensing terms.
info@saccadic.ai · www.saccadic.ai/iris
IRIS pre-alpha. Product overview based on the presentation dated 24 October 2026. IRIS records observations; interpret readings alongside gradients, loss, validation, and task results.