The prompt can vanish.
Its geometry need not.

A minimal formal model

Separate three objects that policy language often collapses: the string, the class, and the parameter update.

Let a request draw content \(X\) (prompt and context), optional hidden reasoning \(Z\), and answer \(Y\). Let \(C = c(X)\) be a latent skill class: not the proprietary tokens, but the region of task space (e.g. multi-constraint SoC signoff, CDC under DVFS, IR-aware STA). Serving may compute a residue \(R = f_\phi(X,Z,Y;\theta)\) via probes, process scores, cluster ids, length bins, principle tags, sketches, or clipped gradient energy. Under a strict content-deletion regime, the durable server-side objects after the request are a subset of \(\{R\}\) (plus ops metrics), not \(\{X,Z,Y\}\).

Three lenses experts keep collapsing

Arguments go wrong when one sentence mixes legal corpora, optimizer steps, and distributional reweighting.

Public post-training already lives in Lens C: Constitutional AI / RLAIF preference synthesis, process supervision datasets, rejection sampling, and curriculum mixtures are distribution machines. The only additional hypothesis is that inference telemetry of the kind a ZDR stack may still keep can enter the same control plane.

Hot path versus egress

While the request is live, the model is a full instrument. Egress policy decides which readings persist.

Alpha traffic: a silicon signoff session

Rare, long-horizon, numeric-constraint prompts are exactly the mass curricula seek. Proprietary tokens can die; the skill neighborhood can thicken.

Imagine an SoC physical-design engineer on a ZDR-flagged API, pasting internal-style constraints for a tapeout candidate. All codenames and numbers below are fictional composites, not real Apple (or any vendor) data. The point is the channel geometry under realistic alpha engineering traffic.

Cordillera revB2 · Hesperides-2 signoff brainstorm (not final STA).

Floorplan: 4×P + 2×E + shared SLC; Hesperides east of SLC.
Mesh ring, 4-hop worst case; QoS ME_RT vs ME_BG.

Internal figures (multi-corner model):
• ME dispatch queue path ~1.84 ns @ 0.72 V SSG; budget ≤ 1.70 ns for ~3.05 GHz issue window
• HES_RF0 SRAM min-Vdd ~0.61 V before bit-line develop fails
• M4 IR drop ~38 mV on vector mm_outer_product_thrash
• CDC ME_clk→SLC_clk: rare recapture when ME DVFS steps 50 mV in <80 ns
• Thermal: Hesperides+ISP hotspot; Tdie_est +11 °C vs north edge in sustained GEMM

1) Rank first-order lever for 1.84→1.70 ns among {queue depth, mesh hop, RF floorplan, DVFS ramp}.
2) CDC harden without unifying ME/SLC clocks.
3) Measurement plan: probes / FSDB / corners for PD+DVFS this week.

Do not invent external PDK numbers; flag uncertainty.

How modern stacks already consume sketches

You do not need a novel learning rule, only a control plane that already reweights data from telemetry-like signals.

What the channel cannot do, and what breaks it

Experts should leave with sharper negatives, not only the positive possibility.

Ten instruments, one ledger

Each row is a candidate f: what it reads on the hot path, what it can export, and which lens it feeds.

Qualitative only: illustrates relative roles. Real I(·;·) depends on featurization, N, and joins.

How you would test the claim

A possibility result earns respect when it states its own falsification.

Anchors, not an exhaustive survey

Public literature for the pieces; the full ZDR→synth composition remains a systems hypothesis.

• PRM · Lightman et al., Let’s Verify Step by Step (PRM800K). Process vs outcome supervision.
• Auto process · Math-Shepherd; OmegaPRM. Step rewards without full human labels.
• RLAIF · Bai et al., Constitutional AI. Principle-conditioned synthetic preferences.
• DPO · Rafailov et al. Preference optimization on pairs.
• Probes · Alain & Bengio; Belinkov surveys; RepE (Zou et al.). Linear structure in activations.
• Faithfulness · Turpin et al.; Lanham et al. Unfaithful CoT as validity threat.
• Serving · PagedAttention / vLLM-class continuous batching. KV as hot-path structure.
• MIA · Shokri et al. membership inference. Attacks on model/output channels.
• DP · Dwork et al. Histogram noise vs utility.
• LoRA · Hu et al.; grad inversion literature. PEFT + leakage of updates.
• Active learning · classical hard-example mining. Mass in error regions → label/synth budget.
• Contrastive · InfoNCE / SimCLR lineage. Aux representation objectives.