license: mit
language:
- en
pretty_name: Sentiance traces
tags:
- cognitive-architecture
- affective-computing
- synthetic
- sentiance
task_categories:
- text-generation
Sentiance traces
Self-labeled deliberation traces exported from the
Sentiance cognitive architecture — the
data used to train the fused mind
(sentiance-film).
Each line is one moment of the cognitive cycle: what the mind was aware of, the
prompt its inner voice saw, the thought it produced, and the numeric state
vector m_t (valence, arousal, emotion, drives, and per-faculty signals) for
that moment.
Format (JSONL)
One JSON object per line:
| field | meaning |
|---|---|
agent |
which named mind produced it (Iris, Milo, Rhea, Cass, Aria, …) |
system, prompt |
the (system, user) prompt the inner voice saw |
prompt_blind |
the same prompt with the felt state removed (state-blind training) |
thought |
the produced inner thought (the supervised target) |
state |
human-readable inner context (emotion, valence, drives, goals, signals) |
state_vec |
the 41-dim numeric m_t — the differentiable conditioning input |
Use
- Path A (voice): fine-tune a small model on
(prompt → thought). - Path B (fused mind): train a cognition-conditioned transformer on
(prompt_blind, state_vec) → thought.
The full collect → prepare → train → eval pipeline is in the Sentiance repo.
Provenance & honesty
Fully synthetic — generated by Sentiance's own (partly rule-based) cognitive cycle, so it reflects that system's regularities, not the world's. No personal data. These are functional state variables, not evidence of subjective experience (see the repo's ADR-0002).
Author
Dr. Sanjay Anbu — creator of Sentiance. github.com/sanjaydoc/Sentiance
License
MIT (the Sentiance repo).