One of four token-matched arms ablating what a futurelens learns from J-lens/answer-agreement-filtered training data. ENTROPY-MATCHED arm — uniform random within entropy < 1.0, bin-matched to arm A's entropy histogram. The control that separates 'J-lens agreement' from 'model-confident positions'; A − P = +0.000 workspace (p=1.0) is the experiment's load-bearing null.

All four arms are identical except the data filter: 244,367 on-policy 12-token spans each (model's own continuations, temp 1.0 top-p 0.95) from a fresh 200k-doc FineFineWeb slice (md5-disjoint from prior skip-lens pools, 67 domains), activations = exact full-context layer-42 residuals. bs 64 (no accumulation), lr 1e-4, LoRA r=64 α=16 rsLoRA scope-all (12 module types), 1 epoch = 3,818 steps, seed 0. Arms A/D/P share the entropy<1.0 gate with bin-matched entropy histograms. Filter reference: the official released J-lens (camilablank/workspace-lenses qwen3.6-27b/j-lens/lens.pt).

Full experiment + all 12 workspace-bench families: http://5.78.192.0/reports/view/agreelens-tokmatch/report.html (internal) — key findings: agree-filtering does NOT add J-lens agreement over entropy-matched random (A−P = +0.000, p=1.0) but produces the best surfacer of imminent/held content on workspace-bench; disagree/span-miss training degrades workspace readout on both harnesses while learning non-J-space reading.

PEFT LoRA adapter on Qwen/Qwen3.6-27B. Inject a raw layer-42 residual at the marker token ㈜ (id 158983) via the Karvonen norm-matched hook (h'_p = h_p + ||h_p||·v/||v||), prompt = prompt_templates.actor in the bundled nla_meta.yaml (chat template, enable_thinking=False), then generate (temp 1.0, top-p 0.95). Sibling arms: ceselder/skip-lens-qwen36-27b-agreelens-{a,d,g,p}.