docs: correct known-issue mechanism (shape-signature specialization cache, guided affected equally at depth)

README.md

@@ -28,14 +28,17 @@ table as a *static graph input* rather than carrying it in the graph. That table
 `ios-frontend/` and **the bundles do not load without it**; a missing table produces a bare
 input-arity error naming `ple_table`/`ple_scale`.
 
-> ⚠️ **Known issue — unguided generation.** On the current reference engine
-> implementation, free-form (unguided) decoding exhibits unbounded Metal memory growth:
-> approximately **80 MB of process `phys_footprint` per generated token**, on both the
-> sequential and pipelined engines, scaling with prompt length. Long generations exhaust
-> memory and the process is terminated. **Constrained/guided decoding is unaffected** and
-> plateaus at 8–10 GB. A fix is in progress. Until it lands, use these bundles for
-> guided/constrained generation only. A fixed token cap is not a reliable mitigation,
-> because the safe cap shrinks as the prompt grows.
+> ⚠️ **Known issue — memory growth on long generations.** The Core AI runtime caches a
+> graph specialization per input-shape signature, and this export makes the sequence
+> length load-bearing: every generated token adds one token to `position_ids` and thus
+> mints a new signature, retaining roughly **80 MB of GPU allocations per generated
+> token** (both engines, guided and unguided alike, at equal context). Workloads whose
+> prompt/output lengths repeat — e.g. short constrained extraction rows — stay bounded,
+> because previously seen lengths replay from cache at no cost. Long free-form
+> generations grow without bound until the process exits; no in-process mitigation
+> exists (token caps only postpone it, and shape-length reuse is the only stable state).
+> A re-export onto a shape-stable contract (fixed-capacity KV read with an explicit
+> attention mask) is planned and will replace these bundles' decode graphs.
 
 ## Provenance