Active project
Ten-SON-LM
A recurrent language architecture with a fixed-size semantic workspace where learned tension controls revision, sparse routing, relaxation, and readout.
What problem it addresses
Calling an internal gate tension means little unless controlled interventions show that it contributes something beyond ordinary learned gating.
How it works
Route token-derived proposals into a fixed workspace, update selected slots through learned tension, relax state recurrently, and read output from workspace context.
What exists now
A working scientific substrate with promising but incomplete causal evidence.
Evidence / receipts
Milestone 1 learned delayed recall, balanced brackets, and a synthetic next-token task. Copy failed its threshold; tension evidence was strong on delayed recall but mixed elsewhere.
Limits
The validation result is Partial. Slot-specific tension is not yet proven, and the architecture has not established an advantage over matched recurrent or learned-gate baselines.
Next milestone
Milestone 1.1: explain the copy failure and run pre-registered causal interventions against equal-sized GRU/LSTM/workspace learned-gate baselines.
How to run / inspect
Install / open
git clone https://github.com/BoggersTheFish/Ten-SON-LM && cd Ten-SON-LMInspect
Read the Milestone 1 report, inspect task-level receipts, and compare learned, shuffled, frozen, constant, and inverted tension interventions.
Links
Focus tags
Related receipts