neuron-tai/.scratch/distributed-gguf-runtime/evidence/DGR-039/BLOCKED.md

DGR-039 is blocked: no real dense ranged executor exists

Date: 2026-08-01

DGR-039 remains passes: false in the authoritative prd.json.

Verified blocker

The live native worker can load and range-attest a GGUF, and it maintains per-session llama.cpp KV bookkeeping. It cannot execute a dense model range:

• LlamaShardEngine::Execute in packages/node/native/worker/llama_shard_engine.cpp deliberately does not convert the TensorBundle into a llama.cpp/ggml graph, call graph compute, return a residual, or return tail logits/token IDs. Its only successful effect is advancing session.past_len and the local token reservation.
• ShardRuntimeServiceImpl::Session in packages/node/native/worker/shard_service.cpp returns the incoming prefill bundle verbatim (*response.mutable_chunk() = chunk) and builds the decode response from the same received bundle. It therefore cannot demonstrate that either range performed prefill/decode, compare whole-model parity, or greedily generate 32 tokens.
• tests/test_architecture_boundary.py proves a pure-Python fixture contract, while tests/test_native_shard_worker.py proves an echo seam. Neither is a real GGUF execution route. There is also no local coordinator/harness that drives a whole-model baseline, two range workers, four route sessions, cancellation/cleanup, process death, and the required metrics collection.

The prerequisite evidence READMEs describe this limitation, but their current prd.json completion flags do not alter the live implementation above.

Required follow-on before this acceptance can run

1. Bind the DGR-035 dense boundary adapter to a native llama.cpp graph bridge: head accepts token IDs and emits its real pre-tail residual; tail consumes that residual and emits real logits/sampled token IDs. Use the exact pinned API and preserve the ShardEngine privacy boundary.
2. Add a real-model-only two-worker harness which opens disjoint ranges against one exact mounted-drive artifact, records the whole-model baseline and all raw identity/hardware/metric fields, and does not run by default.
3. Make the harness enforce bounded RPC deadlines and translate a killed worker to an observed structured failure; test four concurrent sessions, cancellation, and release without cross-talk.
4. Run it on a host with loopback sockets and an explicitly selected GGUF. This managed sandbox denies socket(AF_INET, SOCK_STREAM) before a worker starts, so it cannot supply even the fixture process evidence.

No criterion is weakened and no real-model evidence is claimed.