Framework NN primitives — attention, the KV-cache family, embedding, norms, RoPE, SwiGLU/GeGLU FFN, residual, linear projection — extracted from llm-core so they build on the full Kotlin target matrix including androidNativeArm32/Arm64 (the on-device ARM path). Depends only on skainet-lang-core (which has androidNative); no io/compile/backend deps.

llm-core api-depends on this module and re-exports it, so existing consumers are unaffected. ARM-native consumers (e.g. skainet-whisper-kmp) depend on transformer-core directly and reuse KV-cache/attention instead of reimplementing.

llm-core's primitives only need lang-core, but were trapped there: llm-core's other deps (io-gguf, io-core, compile-*, backend-cpu) lack androidNative, so ARM-native consumers couldn't depend on it. The primitives are dtype-agnostic (just call ops.*), so this target generalization is orthogonal to the quant/dtype generalization (issue #178) — they meet cleanly at these primitives.

transformer/* (KVCache, RoPE, ResidualAdd, MultiHeadAttention, GeGLUFFN, SwiGLUFFN, XIELUActivation, LayerScalarMul, LinearProjection, VoidDense), layers/* (Embedding*), normalization/RMSNormalization, dsl/TransformerDsl. Kept in llm-core: dsl/decoder/* (DecoderTransformerNetwork needs apps.llm.HybridTransformerBlock, which is compile-opt-coupled).

One back-reference decoupled: MultiHeadAttention's diagnostic dumpStats → a settable mhaStatSink (default no-op) that HybridTransformerBlock wires to llm-core's platform dumpStats (no behaviour lost).

:transformer-core: compiles for jvm + androidNativeArm32 + arm64; :llm-core:jvmTest green (5/5) via the re-export.

Branch feature/transformer-core was cut from release/0.31.0. To land on develop (which has #178's merged #179/#180):

1. git fetch origin && git rebase origin/develop — no conflicts expected on the moved files: #178's merged work is in the model layer (GemmaPackedWeights) + engine (ops.transpose Q8_0/Q4_0), not these primitives. (Verified against local refs; re-check against fresh develop.)
2. Build the full target matrix + :llm-core: tests; PR; CI-publish; bump the skainet/transformers pins.
3. Note for future quant work: the pre-transpose-marker (#178 "Solution C") will land in LinearProjection.kt, which now lives here, not llm-core. And RowDequantSource + packed-weight packing (today in sk.ainet.models.gemma) are the next candidates to hoist into a shared quant layer or this module — that's what makes quant reusable across models and whisper.