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@@ -1,4 +1,140 @@
-# EP analog-hardware collaboration — outreach targets (2026-06-21)
+# EP analog-hardware collaboration — outreach targets (2026-06-21; REVISED 2026-07-12)
+
+## ⚡ 2026-07-12 REVISION — gate LIFTED, story changed by the clockless MVP
+**User instruction 2026-07-12: outreach begins now.** Gate artifacts in hand: 42.75M full-epoch
+"能看" demo (first BP-free transformer LM, gap 0.05 nats), E-tier tolerance ledger, cost model,
+and **CLOCKLESS_ANALOG_MVP_PLAN.md** (user-authored) which replaces the $5–20k CIM Demo-0 with a
+**$170–300 clockless twin-network tile** (Dillavou-lineage; EP-current vs CL-voltage on one board;
+exact vs sign local update; no processor/converter/clock in the learning loop).
+
+**What the MVP changes about outreach:**
+1. The ask shrinks from "help us engineer a CIM demonstrator" to "host/advise a $300, six-week,
+   scope-and-DMM bench build" — any analog lab can say yes.
+2. The scientific lineage points at the **physical-learning community (Penn/Dillavou)**, not only
+   CIM-VLSI. Dillavou becomes a wave-1 target (his PNAS 2024 board is the design's ancestor; our
+   deltas: true EP current nudge, exact-vs-sign matrix, the LM program + β-SNR law transfer).
+   Affiliation note (checked 2026-07-12): LinkedIn = "Independent Researcher, ARIA R&D Creator";
+   Penn pages still list postdoc (Durian/Liu). Email the Penn address; keep title-neutral wording.
+3. Substrate groups (Shanbhag CIM, Zhu FeFET, THU, Stanford) are **rung-5 partners** (CIM block) —
+   still first-mover whitespace, pitched as the rung AFTER the tile, which makes us look staged
+   rather than speculative. Hanumolu's converter relevance drops (MVP deletes converters) → wave-2.
+4. Shared quantitative hook everywhere: the board's Factor-4 (bias-vs-nudge-magnitude) = the wall-1
+   β-SNR law we measured in fp32 + the E-tier error-channel result (10% relative noise free) —
+   "the same law, measured in simulation and in physics."
+
+**Revised sequencing:** wave-1a **Dillavou** (design review + natural collaborator; fastest
+credible yes/no) → wave-1b **Shanbhag trio** (local bench + rung-5 CIM; E-tier speaks compute-SNR)
+→ wave-2 Zhu (nonvolatile-weight rung: film cap → FeFET conductance), Hanumolu (rung-5 mixed-signal
+glue), Mingu Kang, Stanford → unicorns (Grollier/Querlioz) once the 32-edge board exists.
+**Ben/Rain thread stays separate** — the MVP plan flows there after the current Overleaf beat.
+
+**Attachments per send:** COLLABORATOR_BRIEF (rev. 2026-07-12, rewritten to cascade-era) +
+CLOCKLESS_ANALOG_MVP_PLAN.md (Dillavou/Shanbhag) — render to PDF and VISUALLY VERIFY before send.
+Sender-title TODO still open. Current drafts: §"Email drafts v2" below; the 2026-06-21 drafts at
+the bottom are SUPERSEDED (looped-era framing, CIM-first ask).
+
+---
+
+## Email drafts v2 (2026-07-12) — copy-paste after title/attachment check
+
+### Draft B (wave-1a) — Sam Dillavou · To: dillavou@sas.upenn.edu
+Subject: A true-EP current nudge on a twin-network learning circuit — building on your PNAS design
+
+Hi Dr. Dillavou,
+
+I'm Yuren Hao (UIUC). Two results may interest you. On the algorithm side, we recently trained a
+standard 12-layer transformer language model entirely without backpropagation — equilibrium
+propagation on a layered energy, all updates local — to within 0.05 nats of a tuned backprop
+control over a full epoch; it generates coherent text. On the hardware side, we are starting a
+physical-learning build whose design descends directly from your processor-free network: two
+continuously-running replicas, shared weight capacitors, local contrast updates, no clock or
+processor in the learning loop.
+
+The planned departures from your architecture are the reason I'm writing. First, an OTA current
+nudge alongside the voltage clamp, so EP's force nudge and Coupled Learning's constraint can be
+compared on the same board — the distinction McGinnis, Li and Mori recently formalized. Second, one
+exact difference-of-squares contrast channel running in parallel with sign-only update cells, for a
+continuous exact-versus-sign comparison. Third, a measured bias-versus-nudge-magnitude curve: in
+simulation we find the EP error channel tolerates 10% relative noise, while an additive precision
+floor sets a hard threshold on the nudge amplitude — the board should exhibit the same law in
+physics, and your imperfection-characterization paper is the closest existing treatment.
+
+Would you have 20–30 minutes to talk? We would value your judgment on the design before we commit
+the board, and there may be a natural collaboration — we bring the transformer/LM program and the
+simulation tolerance data; the physical-learning lineage is yours. A one-page brief and the build
+plan are attached.
+
+Best, Yuren
+
+### Draft A (wave-1b) — Shanbhag group · To: Soonha Hwang (soonhah2@), Mihir Kavishwar (mihirvk2@) · cc: Shanbhag
+Subject: Backprop-free transformer training — GPU-scale results and a staged path to CIM
+
+Hi Soonha and Mihir,
+
+I'm Yuren Hao, working on backprop-free training in ChengXiang Zhai's group at UIUC. The project
+recently crossed a threshold worth reporting: we trained a standard 12-layer transformer language
+model with equilibrium propagation — no backpropagation anywhere in training, every update local —
+to within 0.05 nats of a tuned backprop control over a full epoch, and it generates coherent text.
+Inference is an ordinary forward pass. Every operation in the recipe was chosen to have a known
+analog implementation, and we have measured the fault tolerances the learning rule actually needs:
+8-bit effective weights are lossless and 6-bit marginal; the error channel tolerates 10% relative
+noise; 1% forward state noise costs nothing.
+
+We are deliberately starting the hardware small: a ~$300 clockless twin-network tile (descended
+from the Penn processor-free learning circuits) that validates the physical learning rule with no
+processor, converter, or clock in the loop. The reason to write to your group is the rung after
+that: a CIM transformer block with in-situ EP updates — analog MVM plus a local two-phase weight
+update. Your DiT accelerator and the compute-SNR ADC line are the closest existing substrate for
+that rung, and the tolerance table above is, in effect, its SNR budget.
+
+Could I grab 20 minutes to show the results and the staged plan? A one-page brief is attached.
+(cc'ing Prof. Shanbhag.)
+
+Thanks, Yuren
+
+### Draft C (wave-2) — Wenjuan Zhu · To: wjzhu@illinois.edu
+Subject: Nonvolatile analog weights for a physical equilibrium-propagation learner — FeFET fit?
+
+Dear Prof. Zhu,
+
+I'm Yuren Hao, working on backprop-free training in ChengXiang Zhai's group at UIUC. We train
+transformers with equilibrium propagation — no backpropagation; each weight updates from a local
+contrast between two settled states — and recently demonstrated this at language-model scale in
+simulation (a 12-layer model within 0.05 nats of its backprop control). We are now building a small
+clockless analog learning network in which each weight is a capacitor charged by its own local
+update circuit.
+
+The capacitor is the honest weakness: it is volatile. The natural upgrade is exactly your group's
+territory — a nonvolatile, electrically-programmable, multilevel conductance, and your vdW /
+CuInP2S6 FeFETs are the closest devices I know of. I realize that work has centered on memory and
+logic rather than training; the question is whether a FeFET conductance could replace the weight
+capacitor in a continuously-learning analog network, with the update current driving the gate.
+
+Would you have 20 minutes to discuss feasibility? A one-page brief and the build plan are attached.
+
+Best, Yuren
+
+### Draft D (wave-2) — Hanumolu · To: hanumolu@illinois.edu
+Subject: Mixed-signal partner for the CIM phase of an analog learning program — student pointer?
+
+Dear Prof. Hanumolu,
+
+I'm Yuren Hao, working on backprop-free training in ChengXiang Zhai's group at UIUC. We train
+transformers with equilibrium propagation (no backpropagation; local two-phase updates), recently
+at language-model scale in simulation, and are starting the hardware side with a deliberately
+minimal clockless analog tile — no converters at all in the learning loop.
+
+The phase where your group's expertise becomes central is the one after: an in-memory-compute
+transformer block, where settled-state readout, nudge injection, and loop stability are
+mixed-signal problems. Nearer-term, the tile itself has one control-loop question — enforcing a
+100–1000× time-scale separation between state settling and weight motion — that a student who
+enjoys discrete analog and feedback loops might find fun as a side project.
+
+Could you point me to a student for either, or spare 15 minutes? One-page brief attached.
+
+Best, Yuren
+
+---
 Per-group PhD/PI profiles from 5 research agents. Accuracy discipline: emails only where published or netid on an
 official directory; "—" = not public, route via PI (no invented addresses). Verify "current" status before sending —
 students graduate. Companion: COLLABORATOR_BRIEF.md (the one-pager), HW_RESEARCH_FINDINGS.md (citations).
@@ -133,7 +269,8 @@ world — EP-rich, mostly device-light. Pair one of each.
 
 ---
 
-## ⏸ STATUS (2026-06-21): HOLD — DO NOT SEND until the 33M demo + scaling dossier
+## ~~⏸ STATUS (2026-06-21): HOLD~~ → **GATE LIFTED 2026-07-12 (user instruction; artifacts delivered). Use "Email drafts v2" above; everything below is the superseded 06-21 record.**
+## (superseded) ⏸ STATUS (2026-06-21): HOLD — DO NOT SEND until the 33M demo + scaling dossier
 **User decision (CONFIRMED 2026-06-21): outreach is gated on the ~33M "能看" demo + scaling-law dossier (task #15) — NOT the
 C512/2.09 milestone.** Send nothing until there's a readable-generation ("能看") demo + a scaling-law dossier to lead with.
 (C512 EP descending past the 2.09 wall toward ~1.8 is a prerequisite step that validates the recipe, NOT the outreach gate —
@@ -141,7 +278,7 @@ the gate is the bigger, showable 33M artifact.) Until then: no contact with anyo
 When the bar is met: set sender title, render COLLABORATOR_BRIEF.pdf, attach + ept_method_intro.pdf, optionally ask Prof. Zhai
 for a warm intro to Shanbhag/Hanumolu first. All profiles/contacts/pairing/drafts above are durable and ready.
 
-## Email drafts (READY, gated — copy-paste when the bar is met)
+## Email drafts v1 (2026-06-21) — SUPERSEDED by v2 above (looped-era framing, CIM-first ask; kept for the record)
 
 ### Draft 1 — Shanbhag group · To: Soonha Hwang (soonhah2@), Mihir Kavishwar (mihirvk2@) · cc: Shanbhag
 Subject: Backprop-free (Equilibrium-Propagation) transformer training — a fit for your in-memory CIM work?