P0 live-state audit: 54 rows claimed ACTIVE, none had a branch, 10 had no evidence at all

.agents/NOW.md

@@ -38,8 +38,7 @@ both gate models, reproduced 2–3x on an idle box. See [gates.md](gates.md) and
 (0.26.0.dev0).
 
 Method rules hardened (AGENTS.md): the STRUCTURAL lens (same kernel, different
-throughput ⇒ audit the context; scan the REFERENCE's own rationale; per-shape
-MEASUREMENT arbitrates; distrust aggregate bytes/time and CROSS-TOOL comparisons).
+throughput ⇒ audit the context; per-shape MEASUREMENT arbitrates).
 
 ## Next actions
 
@@ -52,19 +51,20 @@ MEASUREMENT arbitrates; distrust aggregate bytes/time and CROSS-TOOL comparisons
    f32-out caller) once the Laguna fix proves the mechanism.
 4. **Restore `local-ai-worker`** on dgx when the GPU campaign ends
    (`docker update --restart=always` + `docker start`).
-5. **Protocol substrate — partly done.** Claim triage DONE; `docs/STATUS.md`
-   under a shrink-only ratchet; roadmap compacted; `AGENTS.md` tiered. REMAINING:
-   anchor backfill (98 rows `SPIKE`/`ACTIVE`, need code/test anchors; 6 model rows
-   need a DECISION, architecture unregistered); record-era rollover BLOCKED on
-   `check-agent-record.py` binding `DONE` rows to `parity-ledger.md` LINE anchors
-   (re-anchor by ROW ID first; `state.md`/`benchmark-record.md` can roll now).
+5. **Protocol substrate — partly done.** Claim triage + live-state audit DONE
+   (10 unevidenced rows → `READY`, 11 claims retired, 9 amended); `STATUS.md`
+   ratcheted; roadmap compacted; `AGENTS.md` tiered. REMAINING: anchor backfill
+   (6 model rows need a DECISION); record-era rollover BLOCKED on `DONE` rows
+   bound to `parity-ledger.md` LINE anchors (re-anchor by ROW ID).
+   ★ The gate SELF-BLINDS on those same 10 (audit §➁a); its fix owes an 8-row
+   adjudication. workflow.md now states the `ACTIVE` precondition.
 
 **Operator/helper protocol**
 ([spec](specs/operator-helper-protocol.md)): roles DECLARED then MATERIALIZED
 into a lock or worktree+PR; operator merges PRs first and does features only via
 sub-agents; helpers use worktrees on `row/<ROW-ID>` and open a DRAFT PR at the
 START, which IS the claim. **W0-W5 LANDED**; role discipline ENFORCING,
-`--require-role` still opt-in. Queue: 4 rows. Backfill: 79 rows, 30 anchored; blocker is claim FAMILIES.
+`--require-role` still opt-in. Queue: 10 rows — 6 are audit-vacated, with LANDED gate anchors; READ before picking. Backfill: 79 rows, 30 anchored; blocker is claim FAMILIES.
 **Upstream inventory** ([spec](specs/upstream-derived-inventory-2026-08-05.md),
 drift-gated, arch parity BOTH ways): SM060/061/070 below vLLM's floor =
 OUT-OF-SCOPE; COMP-*/DISTRIBUTED-* are REAL unported work; **all 362 archs now have rows**; llama.cpp's 11 extra devices are IN SCOPE, spike-gated

.agents/backend-matrix.md

@@ -270,7 +270,7 @@ the rest are `SPIKE`.
 | ID | Item | Upstream | Our code | Tests/evidence | Spike/spec | State | Owner |
 |---|---|---|---|---|---|---|---|
 | `BACKEND-DISTRIBUTED-COMM` | The unifying `vt::Communicator` / process-group abstraction — rank/world_size + AllReduce(sum/max/min/prod)/AllGather/Send/Recv, stream-ordered (each takes a `Queue&`). **W1 LANDED**: abstraction (`include/vt/communicator.h`) + a CPU in-process multi-rank transport (`src/vt/communicator.cpp`, N ranks = N host threads over one barrier+staging+mailbox) proven by `tests/vt/test_communicator.cpp` (2/4-rank AllReduce-sum + AllGather exact on every rank, Send/Recv rendezvous, RED-verified; 8 cases/50 assertions). `world_size==1` ⇒ every collective a byte-identical no-op (asserted). **W2 LANDED**: collectives now ROUTE through `OpProvider`/`OpId` (`kAllReduce`/`kAllGather`/`kSend`/`kRecv`, keyed on the queue's DeviceType) — the CPU in-process reduce registered on kCPU (`test_communicator` still 50/50 through the OpId path), the NCCL provider on kCUDA. W2+ residuals: RDMA/TCP (Spark), MLX-ring (kMETAL) transports | vLLM `device_communicators/base_device_communicator.py:147` (DeviceCommunicatorBase interface, the port template) + `distributed/parallel_state.py:358` (GroupCoordinator dispatch; world_size==1 bypass :638) | LANDED: `include/vt/communicator.h` + `src/vt/communicator.cpp` (sibling of `vt::Queue` `include/vt/device.h:50`); OpId routing via `include/vt/op_provider.h:108` (`OpId::kAllReduce/…`); stream-order hooks reused `include/vt/backend.h:87-104` | CPU exact-gate (`test_communicator`, 50/50, via OpId path) | [scale-out spike](specs/scale-out-distributed.md) | `ACTIVE` | `CLAIM-SCALE-OUT-W2` |
-| `BACKEND-DISTRIBUTED-TP` | Tensor parallel (intra-node multi-GPU) — sharded Column/Row/QKV linears, vocab-parallel embed + LM head, attention-head split, MoE expert-parallel; all-reduce after o_proj/MLP-down and the EP combine. **W2 LANDED (CPU-gated)**: the `TensorParallel`/`TpShard`/`TpAllReduceSum` wiring (`include/vllm/model_executor/models/tensor_parallel.h`) threaded into the Qwen3-dense forward (o_proj all-reduce `dense_attn_block.h`, MLP-down `qwen3.cpp`) + the MergedColumn shard at the loader chokepoint (`dense_weight_loaders.h`), proven by `tests/vt/test_tp_forward.cpp` — sharded-matmul + RowParallel all-reduce **== the unsharded tp=1 forward** over the W1 CPU communicator (RED-verified: dropping the all-reduce fails 24 assertions). `tp_size==1`/nullptr ⇒ every helper a byte-identical no-op (asserted). RESIDUAL (HW-gated, no ≥2-GPU box): QKV head-aware KV replication, vocab/LM-head + MoE-EP sharding, and a real TP-2 GPU forward | vLLM `layers/linear.py:418` (Column, out-dim shard) / `:1612` (Row, all-reduce :1766) / `:1021` (QKV heads :1074) + `vocab_parallel_embedding.py:198` + `fused_moe/expert_map_manager.py:22` | LANDED `include/vllm/model_executor/models/tensor_parallel.h`; seams `dense_attn_block.h` (o_proj all-reduce) + `qwen3.cpp` (MLP-down); weight chokepoint `dense_weight_loaders.h:131` (column shard) | CPU multi-rank TP gate (`test_tp_forward`, 60/60, RED-verified) | [scale-out spike](specs/scale-out-distributed.md) | `ACTIVE` | `CLAIM-SCALE-OUT-W2` |
+| `BACKEND-DISTRIBUTED-TP` | Tensor parallel (intra-node multi-GPU) — sharded Column/Row/QKV linears, vocab-parallel embed + LM head, attention-head split, MoE expert-parallel; all-reduce after o_proj/MLP-down and the EP combine. **W2 LANDED (CPU-gated)**: the `TensorParallel`/`TpShard`/`TpAllReduceSum` wiring (`include/vllm/model_executor/models/tensor_parallel.h`) threaded into the Qwen3-dense forward (o_proj all-reduce `dense_attn_block.h`, MLP-down `qwen3.cpp`) + the MergedColumn shard at the loader chokepoint (`dense_weight_loaders.h`), proven by `tests/vt/test_tp_forward.cpp` — sharded-matmul + RowParallel all-reduce **== the unsharded tp=1 forward** over the W1 CPU communicator (RED-verified: dropping the all-reduce fails 24 assertions). `tp_size==1`/nullptr ⇒ every helper a byte-identical no-op (asserted). RESIDUAL (HW-gated, no ≥2-GPU box): QKV head-aware KV replication, vocab/LM-head + MoE-EP sharding, and a real TP-2 GPU forward | vLLM `layers/linear.py:418` (Column, out-dim shard) / `:1612` (Row, all-reduce :1766) / `:1021` (QKV heads :1074) + `vocab_parallel_embedding.py:198` + `fused_moe/expert_map_manager.py:22` | LANDED `include/vllm/model_executor/models/tensor_parallel.h`; seams `dense_attn_block.h` (o_proj all-reduce) + `qwen3.cpp` (MLP-down); weight chokepoint `dense_weight_loaders.h:131` (column shard) | CPU multi-rank TP gate (`test_tp_forward`, 60/60, RED-verified) | [scale-out spike](specs/scale-out-distributed.md) | `READY` | - |
 | `BACKEND-DISTRIBUTED-PP` | Pipeline parallel — PP stage split (`PPMissingLayer` analogue) + inter-stage `IntermediateTensors` send/recv over the comm layer + multi-worker executor fan-out | vLLM `models/utils.py:785` (PPMissingLayer) / `:798` (make_layers) + `distributed/utils.py:127` (get_pp_indices) + `parallel_state.py:957` (send_tensor_dict) | fan-out seam `src/vllm/v1/executor/executor.cpp:7-34` (direct single-worker call today) | - | [scale-out spike](specs/scale-out-distributed.md) | `SPIKE` | `CLAIM-SCALE-OUT-SPIKE` |
 | `BACKEND-DISTRIBUTED-DP` | Data parallel — N independent engine replicas over the SAME weights + a DP coordinator (global "request wave" so all DP ranks step together) + a per-step token-count all-reduce; DP×EP is the large-scale DeepSeek serving topology (DP-replicated attention + EP-sharded experts). NOT part of `world_size` (DP is outside: `world_size_across_dp = world_size × DP`) | vLLM `v1/engine/coordinator.py:23` (`DPCoordinator`, wave :33-56) + `v1/worker/dp_utils.py:164` (`coordinate_batch_across_dp`; per-step `num_tokens_across_dp` all-reduce :53) + group `distributed/parallel_state.py:1866` + flags `config/parallel.py:129-145` | reuses W1 `Communicator::AllReduce` for the token-count sync; NEW engine-replica executor + coordinator; **depends on the multi-worker executor (`executor.cpp:7-34`)** | - | [parallelism-modes spike](specs/parallelism-modes.md) | `SPIKE` | `CLAIM-PARALLELISM-MODES-SPIKE` |
 | `BACKEND-DISTRIBUTED-EP` | Expert parallel — each rank owns a DISJOINT subset of WHOLE experts (vs TP's every-rank-all-experts intermediate-dim shard); all-to-all dispatch (tokens→expert-owner) + all-to-all combine. EP group = DP×PCP×TP ranks (MoE-only), a re-grouping NOT a new `world_size` knob; EPLB is a separate same-ranks group. DeepEP HT/LL/V2 all-to-all backends present upstream | vLLM `fused_moe/expert_map_manager.py:22` (`determine_expert_map`, local_num_experts :69) + `use_ep` `fused_moe/config.py:1204` + EP group `distributed/parallel_state.py:1892` + all-to-all backends `fused_moe/all2all_utils.py:44` (`prepare_finalize/deepep_{ht,ll,v2}.py`); flags `--enable-expert-parallel` `config/parallel.py:165` + `--all2all-backend` `:188` | NEW `OpId::kAllToAll` (dispatch/combine) on the same `vt::Communicator` + the expert-subset loader (`qwen3_moe_weights.cpp:35-38` per-expert loop); `vt::MoeCombine` (`include/vt/ops.h:104`) becomes the combine | - | [parallelism-modes spike](specs/parallelism-modes.md) | `SPIKE` | `CLAIM-PARALLELISM-MODES-SPIKE` |