JSON Reference

JSON reference

Analysis files are UTF-8 JSON, optionally wrapped in gzip when the filename ends in .json.gz. Current normal-chat exports use aibrain.session-analysis.v2; Infinite-mode Analysis+ exports use aibrain.infinite-analysis-plus.v2. Both schemas describe real-time inference telemetry.

Files are deliberately compact. They do not contain renderer arrays, per-node frames, model weights, source prompts, hidden states, attention matrices, MLP activations, or chain-of-thought.

Field	Meaning	Type / units	Classification
schema	Export contract identifier.	string	status
created_at	Export creation time.	ISO-8601 UTC	measured
conversation	Visible chat or Infinite simulation turns.	array	measured
graph.kind	real_time_inference_telemetry_map.	string	status
graph.topology	Always display_only; nodes and links are not model topology.	string	status
graph.nodes, graph.edges	Size of the rendered display layout.	counts	display-only
graph.measurement_channels	Ordered names of the nine normalized telemetry inputs.	string array	status
graph.cluster_colours	Stable display colour for each channel.	hex colour map	display-only
measurement_integrity	Human-readable measurement source and graph limitation.	string	status
integrity	Analysis+ statement identifying autoencoder inputs and excluded model internals.	string	status
recorded_frame_summary	Compact findings over the retained telemetry frames.	object	derived
frames	Number of retained telemetry frames included in the result.	count	measured
mean_novelty, peak_novelty	Autoencoder embedding-distance/reconstruction findings.	unitless	derived
mean_reconstruction_error, mean_coherence	Autoencoder reconstruction quality for this export.	unitless	derived
most_active_channel	Channel with the highest mean normalized measurement across retained frames.	channel name	derived
maturity	Persisted training-health state, conditions, and readiness.	object	health/status
smart_analysis	Present only in aibrain.infinite-analysis-plus.v2.	object	derived
analysis_storage	Paged/resident counts, cache limit and use, and data-loss status.	object	measured/status
smart_analysis.neural_network	Autoencoder architecture, feature schema, calibration, and lifetime health.	object	health/status
session_findings	Compact channel and temporal-pattern findings.	object	derived
channel_profile	Mean and peak normalized value for each measurement channel.	ratio 0–1	normalized measurement
key_events	Up to 24 notable telemetry frames with raw metrics and learned findings.	object array	mixed

Each key event identifies its generated step, visible output_text chunk, dominant channel, Real-time source, raw telemetry, embedding novelty, reconstruction error, coherence, and compact embedding. Raw telemetry uses these fields when a live logits snapshot is available:

Telemetry field	Meaning	Units
context_tokens, context_limit	Evaluated context position and requested context size.	tokens
output_tokens, chunk_tokens	Retokenized visible count in this reply and current chunk.	tokens
stream_latency_ms	First-chunk latency or observed time since the previous visible chunk.	milliseconds
retokenized_tokens_per_second	Current visible chunk's retokenized count divided by its latency.	tokens/second
vocabulary_size	Number of raw logits inspected.	entries
raw_logits_available	Whether the chunk carried a valid raw-logit snapshot.	0 or 1
raw_logit_entropy_bits	Shannon entropy of the raw-logit softmax.	bits
raw_top_probability	Largest raw-logit softmax probability.	ratio 0–1
raw_top_five_mass	Sum of the five largest raw-logit softmax probabilities.	ratio 0–1
raw_confidence_margin	Difference between the largest and second-largest raw probabilities.	ratio 0–1
recent_output_occurrences	Matches for this emitted text chunk in the previous 32 visible chunks.	count

The raw-logit fields describe the distribution before penalties, grammar, top-k/top-p/min-p/typical filtering, temperature, and token selection. They are not final sampling probabilities.

maturity.state is Baby, Teen, Adult, or Elder. Baby requires at least 2,048 persisted frames and sustained recent consistency before Teen. Adult additionally requires at least 32,768 frames and sustained consistency/learning slowdown. Elder requires a sustained post-Adult overfitting signal and freezes training weights. This is not an accuracy claim. Baby and Teen exports carry readiness: "caution"; Adult and Elder carry readiness: "ready".

The NPZ model stores feature_schema=aibrain.realtime-inference-telemetry.v1. An older NPZ without that identifier may contain retired simulated features and is rejected instead of mixing those weights with real-time telemetry. Generate a new response to create a compatible model. The standalone inspector reports the reason explicitly.