SUSS v0.1.0 (preprint)

Preprint of SUSS: what a transformer learns when it adapts to unknown physics in-context (L4DC 2027 format, not peer reviewed).

Headline results (numbers from results/summary.json; exploratory analyses are labelled as such):

• On linear systems with unknown (A, B), the transformer is Bayes-optimal while data are scarce, then settles at an approximation floor: 1.59x the Bayes excess risk (pre-registered tolerance 1.25x, rejected). Training 3x longer or doubling depth brings it to about 1.22x.
• Pre-registered mechanism rejected: no linearly readable, causally used parameter estimate in the residual stream (probe R^2 = 0.19; steering and subspace patching have no effect).
• Exploratory, with dimension-matched controls: the sufficient statistics live in the attention key-value memory of the last two blocks (R^2 0.92 from fast weights, 0.84-0.87 after projection to 128 dims, at most 0.58-0.61 from the residual stream; shuffled labels 0.00). Patching only the context keys and values of blocks 3-4 transfers 91-94% of another episode's behaviour; replicated on 7 more seeds.
• MuJoCo: on a block with Coulomb friction the transformer beats physics-feature RLS by 3.3x and structured system identification with the exact simulator (0.0058 vs 0.011).
• In-context control: the controller excites the plant without an identification objective (+4.2 nats over exploration-free certainty equivalence, 1.4 less than dithered CE), is competitive in median cost, but fails on 10% of plants (1.4% for dithered CE), almost all open-loop unstable. Fine-tuning on raw cost or on CVaR diverges.

Reproduce: pip install -r requirements.txt, python -m pytest -q tests, bash scripts/run_all.sh.

Pre-registration: tag prereg-d134c5a (git history; an external Zenodo snapshot is planned). Deviations: docs/DEVIATIONS.md.

Still running at release time and to be added in the next release: three more scale-study runs (wider, smaller, two more seeds of the 4-state system) and the fast-weight probe replication on the 7 extra seeds.