hikewa/unitares-eisv-trajectories

EISV trajectory dataset for dynamics-emergent voice and governance benchmarking.

Source: CIRWEL/eisv-lumen

EISV Framework

Each trajectory tracks four continuous dimensions over time:

Dimension	Symbol	Range	Description
Energy	E	[0, 1]	Productive capacity; couples toward I via α(I−E), reduced by entropy cross-coupling
Information Integrity	I	[0, 1]	Signal fidelity; boosted by coherence C(V,Θ), reduced by entropy
Entropy	S	[0, 1]	Semantic uncertainty; decays naturally, rises with complexity and drift
Void	V	[0, 0.3]	Absence of engagement; V = (1 − presence) × 0.3

Note on ranges: These are observation-layer values from Lumen's sensors. The UNITARES governance ODE evolves S to [0, 2] and V to [−2, 2] as a signed E−I imbalance integrator, but the raw trajectories in this dataset use the sensor ranges above.

What is Lumen?

Lumen is a Raspberry Pi with environmental sensors (BME280 for temperature/humidity/pressure, VEML7700 for light) that maps physical readings to EISV dimensions: warmth→E, clarity→I, (1−stability)→S, (1−presence)×0.3→V. The trajectories in this dataset are time-windowed snapshots of these sensor-derived EISV states.

Trajectory Shape Classes

Each record is classified into one of 9 dynamical shape classes:

• settled_presence: Stable high-energy state with low variance across EISV dimensions.
• rising_entropy: Entropy (S) increasing over time, indicating growing uncertainty or exploration.
• falling_energy: Energy (E) declining, signalling withdrawal or depletion.
• basin_transition_down: Sharp downward shift in the energy basin (E and I drop across a threshold).
• basin_transition_up: Sharp upward shift in the energy basin (E and I rise across a threshold).
• entropy_spike_recovery: Sudden entropy (S) spike followed by recovery toward baseline.
• drift_dissonance: Sustained dissonance with drifting EISV values and no clear attractor.
• void_rising: V increasing as energy exceeds integrity (E > I); presence fading.
• convergence: Multiple EISV dimensions converging toward a shared equilibrium.

Shape Classification Note

Shape labels in this dataset were generated from 20-step trajectory windows. If you reclassify using shorter windows, expect significant label disagreement:

Window Size	Label Match
4-step	65%
8-step	77%
10-step	81%
15-step	91%
20-step	100%

Most mismatches (5,138 cases) are settled_presence → convergence. A 4-step window only sees the tail end of a settling trajectory, which looks like convergence. The full 20-step arc is needed to confirm the system has actually settled. If training on this dataset, use at least 10-15 steps for reliable shape classification.

Dataset Statistics

• Total records: 32181

Shape Distribution

Shape	Count
basin_transition_down	2000
basin_transition_up	2000
convergence	8089
drift_dissonance	2000
entropy_spike_recovery	2000
falling_energy	2000
rising_entropy	2000
settled_presence	10092
void_rising	2000

Schema

Column	Type	Description
shape	string	Trajectory shape class label
eisv_states	string (JSON)	Time-series of EISV state vectors
derivatives	string (JSON)	First derivatives of EISV dimensions
t_start	float	Start time of the trajectory window
t_end	float	End time of the trajectory window
provenance	string	lumen_real (from Lumen sensors) or synthetic (generated to fill underrepresented shapes)
tokens	string (JSON)	Expression token lists aligned to the trajectory
n_expressions	int	Number of aligned expressions

Models Trained on This Dataset

Teacher (Qwen3-4B + LoRA)

A Qwen3-4B model fine-tuned with LoRA (rank 16, alpha 32) on a 50/50 blend of real Lumen trajectories and synthetic data.

Metric	Synthetic Eval	Real Eval
Mean coherence	0.911	0.952
Valid rate	100%	100%
Pattern accuracy	26%	30%

The teacher surpasses the rule-based baseline (0.933) on real data, demonstrating that neural models can learn dynamics-emergent expression from trajectory patterns.

Student (Distilled RandomForest)

Lightweight models distilled from the teacher for edge deployment (Raspberry Pi):

Variant	Coherence	Agreement w/ Teacher	Size
Full	0.924	69% token-1	~5 MB
Small	0.986	69% token-1	~2 MB
Tiny	~0.98	~65% token-1	1.5 MB

Links

• Teacher model: hikewa/eisv-lumen-teacher
• Student model: hikewa/eisv-lumen-student
• Interactive demo: EISV-Lumen Explorer

License

This dataset is released under the Apache 2.0 License.

Citation

@misc{unitares_eisv_trajectories,
  title = {hikewa/unitares-eisv-trajectories},
  author = {{hikewa}},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/hikewa/unitares-eisv-trajectories},
}