Implements the English Supervised Seeding Epoch (V1):
- language_packs/en_seeder.py: downloads GloVe-6B-50d, projects each
token embedding through a CGA lift into Cl(4,1) via construction_seed_versor,
validates the versor invariant, and registers the word in VocabManifold.
- scripts/run_pulse.py: replaces the mock 10-word hash vault with the
live VocabManifold. Injection now uses TextProjectionHead.project()
against the seeded vocab; vault_recall queries VocabManifold.nearest().
Hash fallback retained for words absent from GloVe (OOV tagged fallback).
The CGA lift preserves semantic neighbourhood: words close in GloVe
cosine space map to versors that are geometrically proximate in Cl(4,1)
inner product space, so nearest() returns semantically coherent results
rather than hash-proximity artefacts."
Add ManifoldState (N,32) versor field over graph edges, GraphDiffusionOperator
with damped convergence via construction_seed_versor closure, deterministic
hash-to-versor stub, and run_pulse.py end-to-end script proving injection →
propagation → vault recall → token output. 24 new tests, zero regressions
on architectural invariants.
run_examples.py
Runs a curated set of example conversations through ChatRuntime,
writing one JSONL trace file per scenario to traces/. Each line in the
file is one TurnEvent serialised as JSON, giving the complete
determinism record for that turn. Scenarios cover:
- single-turn field probe
- multi-turn dialogue with memory (vault recall across turns)
- identity alignment pressure (input designed to approach the flag threshold)
- fatigue arc (many turns to observe ExertionMeter drain)
- versor drift (watches versor_condition across a session)
Run with: python scripts/run_examples.py
Output: traces/<scenario>.jsonl
review_trace.py
CLI reader for JSONL trace files produced by run_examples.py or
`core session`. Supports:
--summary one-line-per-turn table (turn, surface, role, score, cost, flagged)
--turn N full detail for a single turn
--flagged show only flagged turns
--drift print versor_condition per turn (tracks algebraic drift)
--identity print identity_score + alignment per turn
--fatigue print cycle_cost_total per turn (exertion arc)
Run with: python scripts/review_trace.py traces/<scenario>.jsonl [options]
cli: cmd_trace now includes identity_score, flagged, cycle_cost (from turn_log[-1])
cli: new cmd_session subcommand - multi-turn REPL that writes a trace file on exit
