DGR-006 — architecture-defined boundary input/output

Status: complete deterministic/offline contract and dense-fixture evidence.

Result

The native protocol now carries a versioned TensorBundle on the decode fast path. It includes explicit architecture and boundary-point metadata. Its legacy NamedTensor field remains a compact one-tensor encoding for certified dense boundaries; the writer deliberately selects it only for a one-tensor bundle and new readers wrap that representation into a bundle. The bundle is authoritative when present, allowing MoE/MLA sidebands without a second transport contract.

architecture_boundary.py is the fail-closed adapter boundary. Dense head Shards accept token IDs and own embedding. Middle/tail Shards accept only a validated bundle. Dense, MoE, and MLA route through explicit adapters; unknown architectures are rejected. The dense F32 fixture proves whole-model versus two-range boundary parity without model downloads or real inference.

Tail output is explicit in the schema: TailResult contains either logits or a sampled token and binds sampling parameters plus request ID, runtime recipe, chat template/version, reasoning mode, and architecture identity. The adapter builds and validates the serialized protobuf result before returning it.

Files changed

packages/node/native/proto/shard_runtime.proto
packages/node/meshnet_node/native_protocol/{codec.py,__init__.py,conformance.py,generated/*}
packages/node/native/testdata/decode_step_golden.binpb
packages/node/native/tests/test_shard_protocol_conformance.cpp
packages/node/meshnet_node/architecture_boundary.py
tests/test_architecture_boundary.py
tests/test_native_shard_protocol.py
packages/node/native/README.md

Commands and results

All Python commands used /run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python. All native commands used /run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/cmake.

python scripts/generate_native_protocol.py --check       -> passed
python scripts/generate_protocol_goldens.py --check      -> passed
pytest -q tests/test_architecture_boundary.py \
  tests/test_native_shard_protocol.py tests/test_llama_cpp_dependency.py
                                                    -> 59 passed
cmake -S packages/node/native -B build/native \
  -DCMAKE_PREFIX_PATH=/tmp/pbsrc/install                -> configured
cmake --build build/native -j$(nproc)                   -> built shard_protocol_conformance
ctest --test-dir build/native --output-on-failure       -> 1/1 passed
python -m compileall -q packages tests                  -> passed
git diff --check                                        -> passed
pytest -q                                               -> 917 passed, 18 skipped

Compatibility and limitations

Existing Nodes that send DecodeStep.tensor are accepted. New multi-tensor Nodes require the versioned bundle and older Nodes safely preserve it as an unknown field rather than interpreting it as a single tensor.
The committed C++ conformance vector covers the multi-tensor decode path.
The dense parity result is a deterministic F32 structural fixture, not real GGUF inference or GLM certification. No real inference was run.
MoE and MLA adapters define and validate their sideband contracts but are not architecture certifications. DGR-019 owns GLM MoE/MLA/DSA/IndexShare semantics.

Handoff

DGR-007 can key its Hot KV state to the validated decoded bundle. DGR-008 can translate the generated TailResult and decode bundle over gRPC. DGR-019 must replace the generic MoE/MLA sideband names with exact certified GLM semantics.

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