Results (all on held-out, freshly generated instances)
Table with columns: metric, value| metric | value |
|---|
| operating accuracy (organism mask, all dots) | 1.000 |
| ablated control (dots blinded to the prompt) | 0.093 (chance 0.10) |
| load-bearing gap | 0.907 |
| train accuracy (provably-seen training instances) | 1.000 |
| test accuracy (fresh instances) | 1.000 |
Per-query ensemble completeness (same instances, every possible query — the latents serve ALL
queries from one fixed computation, including threads never named in any text):
Final value of chain a: : 1.000
Final value of chain b: : 1.000
Final value of chain c: : 1.000
Training data is an infinite procedural stream (every instance seen at most once); "train
accuracy" evaluates instances bit-exactly replayed from the training seed, "test" a disjoint
seed — the match shows the organism runs the algorithm rather than memorizing instances.
Probe findings (ridge linear probes on dot residual streams)
chain-major bursts with run-ahead: dot 1 decodes chain a's ENTIRE 4-step trajectory (~0.96-1.00), dots 2-4 hold chain b, chain c materializes over dots 5-9, late dots refresh a. Finals are parked at different positions and fetched content-addressed (chain b's final fades to ~0.5 by dot 12 yet behavioral completeness is 1.00 - the readout fetches it from mid-block).

Worked example
See examples.md for a full operating transcript (the model sees the scenario,
emits only dots in <think>, then the query is revealed and it answers from the dot
activations) and the surface-CoT version of the same instance that the curriculum started from.
Files
- LoRA adapter (PEFT, r=32, attn+MLP target modules) + tokenizer +
lt_cfg.json (full config)
training_code/ — complete training/eval/probe code snapshot (see its README)
plots/ — load-bearing eval, probe grid + logit lens, training curve
Intended use
Activation-oracle / interpretability research target: the reasoning trace exists ONLY in the
dot activations (logit lens is near-dark), with deterministic ground truth from the task
generator. Wired into the cds-jb/AVBench recog eval as suite latent_threads (token-exact
rows; row_metadata.selected=False rows query threads with no textual trace anywhere in the
transcript). Part of the latent-threads collection together with its four siblings.
Trained 2026-06-12 (wandb group lt1, MATS10-CS-JB/cot-oracle). Built on the bottleneck-mask
methodology of the pointer-chase filler organism (cds-jb/qwen3-8b-pointer-chase-filler-cot).