GIGACHAD Phase-8C + Phase-8D — Organ wiring & first real E2E

Date: 2026-04-27 Scope: Replace stub organ outputs with real native LLM calls. First end-to-end ARIZ pipeline that walks memory → 5 organs → top brain → verifier → DAG without any Python in the hot path.

Physarium-v1 errata: any Physarium-related sparsity / PPL number

referenced here is v1 magnitude-flow surgery. Read with

reports/PHYSARIUM_RESULTS_RECONCILE.md + PHYSARIUM_COVERAGE_AUDIT.md.

Phase-8C0 — sanity guard (passed)

Run before E2E to catch determinism / NaN issues.

| Pack | Hash run 1 | Hash run 2 | NaN | Inf | Top-1 sane | |-----------------------|-----------------------|-----------------------|-----|-----|---------------------------------------------| | physarum05b.planck | 0xd70ba434d29fe10a | 0xd70ba434d29fe10a | 0 | 0 | top-10 = ² , re \n , et … (post-pruning) | | physarium7b.planck | 0xaa40ebf6fdbeee8e | 0xaa40ebf6fdbeee8e | 0 | 0 | top-10 = ' ' ' i' ' I' '\n' ',' ' a' … |

All four sanity properties hold:

1. Same prompt + greedy → identical token sequence (deterministic).
2. Top-10 first-step distribution stable across runs.
3. No NaN / Inf in any logits.
4. FNV-1a 64 of generated tokens equal across runs.

Phase-8C — files added / changed

| File | Purpose | |-----------------------------------------------|----------------------------------------------| | include/qwen2_tokenizer.hpp + src/tokenizer/qwen2_tokenizer.cpp | Native Qwen2 byte-level BPE encoder/decoder. Loads tokenizer.json. | | include/planck_runner.hpp + src/model/planck_runner.cpp | High-level Runner class wrapping a planck pack + tokenizer. Text-in / text-out + stats. | | include/organ_manager.hpp + src/organs/organ_manager.cpp | Registry of 8 phys05_* organs + 1 physarium_7b top brain. Per-organ prompt template, max_tokens, tier, verifier. Shared planck pack reused across organs. Tier accounting via tier_manager::record_access. | | src/main.cpp | run_task now dispatches dispatcher route → organ_manager.run() instead of stub. New run_ariz_e2e chain. |

Tokenizer self-check

[tok] loaded vocab=151643 merges=151387 eos=151645 bos=151644

Encoded "Hello" → [9707], decoded back to "Hello". Round-trip on the ariz prompt is byte-identical.

Organ smoke (json_repair)

$ gigachad_native --task json_repair --input '{"k":1,}'
{
  "route":      "json_repair",
  "ok":         true,                   ← hard verifier passed
  "verifier":   "strict JSON object",
  "latency_ms": 17547,
  "final":      " {\"k\":1}Human: I'm sorry, but I cannot ..."
}

The 0.5B organ generated {"k":1} (correct repair), then trailed off into a chat-style hallucination. Hard verifier accepts the first JSON object in the output, which is the right behaviour — what's after is noise.

This validates the path: dispatcher → organ_manager → planck_runner → tokenizer → forward pass → decode → hard_verifier → DAG.

Phase-8D — first ARIZ E2E

$ gigachad_native --task ariz \
   --input "Hot dusty gas at 600C clogs a metal filter. Solve with ARIZ/TRIZ."

Real pipeline executed (NOT input → 7B → answer)

input
 → memory recall
 → hologram retrieve              (3 prior ariz holograms found)
 → physarium field suggestion     (best route = "ariz")
 → phys05_triz_contradiction      (0.5B native, 96 tok, 41.2 s)
 → phys05_claim_extractor          (0.5B native, 64 tok, 39.8 s)
 → physarium_7b top brain          (7B native, 128 tok, 224.7 s)
 → hard verifier
 → DAG write

Per-stage table

| stage | organ | tier | wall_ms | gen_tok | tok/s | verifier | |--------------------|-----------------------------|------|----------|---------|-------|------------------| | memory_recall | (spine) | RAM | 0 | 0 | — | holo_hits=3 | | triz_contradiction | phys05_triz_contradiction | RAM | 41 201 | 96 | 2.52 | ❌ no JSON object| | claim_extractor | phys05_claim_extractor | RAM | 39 789 | 64 | 1.73 | ❌ no JSON array | | top_brain | physarium_7b | VRAM | 224 732 | 128 | 0.62 | ❌ no JSON object|

Aggregate

| Metric | Value | |--------------------------|-----------------------------------------------------------------| | Total wall | 305.7 s (5 min 6 s) | | User CPU | 61 min 19 s (≈12× wall thanks to OpenMP) | | Top brain | physarium_7b_native (15.23 GB BF16 mmap'd from physarium7b.planck) | | Organs used | phys05_triz_contradiction, phys05_claim_extractor, physarium_7b | | Memory sources | 3 holograms (ariz_7f4fb4ba…, ariz_405b12276136301b_*) | | Hologram hits | 3 | | Field suggest | ariz | | Source gate | ✅ true (≥ 1 memory source) | | Hard verifier on top-out | ❌ false ("no JSON object") | | Total tokens generated | 288 | | Python in hot path | none (tokenizer + runner + organ + verifier + DAG all native C++17) | | DAG path | dag/runs/1777299739134_ariz_e2e_5bac122dbbed5fd5_physarium_7b_*.json | | RAM resident (KV+scratch)| ≈ 1.5 MB per layer × 28 layers + 28 KV slots ≈ 42 MB | | VRAM measured | 0 (CPU-only — Phase-8E will move tensors to GPU) | | SSD reads (mmap) | full 15.23 GB pack faulted into page cache during 7B forward |

Where the verifier failed (honest)

All 3 verifier failures share the same root cause: the prompt templates end inside the JSON object (e.g. ...JSON: {"technical_contradiction":"), so the model continues from inside the value. Output therefore has no opening { or [, even though everything else looks correct.

Sample raw outputs (truncated):

• triz_contradiction:

gas at 600C clogs metal filter","physical_contradiction":"metal filter clogs at 600C","improves":"metal filter clogs at 600C","worsens":"metal filter clogs at 600C","resource_hints":["heat","pressure"…

• claim_extractor:

{"claim":"Hot dusty gas at 600C clogs a metal filter","type":"number","needs_source":true},{"claim":"Solve with ARIZ/TRIZ","type":"fact","needs_source":false},{"claim":"Metal filter clogs with hot dus…

• physarium_7b top brain (full output):

NEED_MORE_EVIDENCE | Final: NEED_MORE_EVIDENCE | Final: NEED_MORE_EVIDENCE … (greedy decoder + heavily-pruned 7B + long synthesis prompt → degenerate loop on the abstain token).

These are calibration / prompt-engineering failures, not architectural failures. The pipeline executed every stage correctly.

What's real after Phase-8C/D

• ✅ Native Qwen2 BPE encoder/decoder in C++17 (151 643 vocab, 151 387 merges).
• ✅ Runner class wraps a planck pack + tokenizer; one runner reused

across the 5+ phys05_* organs.

• ✅ 9 organ specs registered (8 phys05_* + physarium_7b), each with own

prompt, max_tokens, tier, verifier mapping.

• ✅ Dispatcher routes every supported --task into a real native organ

call (no more stubs in run_task).

• ✅ Tier manager records food/poison/latency on every organ run.
• ✅ ARIZ E2E executes the full chain: memory → hologram → field → 2×0.5B

organs → 7B top brain → verifier → DAG.

• ✅ Determinism guard: same input → same hash twice on both packs.
• ✅ No NaN / Inf in any logits at any stage.

What is honestly NOT yet good enough

• ❌ Verifier pass rate on this E2E run = 0/3. Needs prompt template

surgery (drop the open-brace prefix so model emits its own opening JSON).

• ❌ 7B greedy decoder degenerates into a NEED_MORE_EVIDENCE loop on the

long synthesis prompt. Needs sampling (top-p / temperature / repetition penalty) or a stop-token guard, neither of which is in the runner yet.

• ❌ No CUDA acceleration. 7B at 0.62 tok/s is the floor; the 224 s in the

top-brain stage dominates total wall.

• ❌ phys05_test_writer isn't called in the ariz chain (skipped — task is

not code-shaped). The 5-organ minimum is satisfied across the codebase (8 specs registered, 5 are actively wired in run_task), but a single --task ariz run uses only 3.

• ❌ Memory recall reads holograms but doesn't yet inject them into the

organ prompts as a MEMORY block (planned in physarium7b_top_brain.txt but not implemented in this cut).

What this proves and what it does not

• Proves: Phase-7's body + Phase-8A's pack + Phase-8B's runner together

form a working organism. A single command walks every component of the spine from text input to DAG-logged final output, with no Python and no external LLM library.

• Does not prove: that the organism reasons correctly. The 7B's

degenerate output on this prompt shows that pruning + greedy decode

• un-tuned synthesis prompt is not yet a reasoner. Phase-8E (CUDA) and

Phase-8F (decoder + prompt iteration) are needed for that.

Build / run

make all
./build/gigachad_native --task json_repair --input '{"k":1,}'
./build/gigachad_native --task ariz --input "Hot dusty gas at 600C clogs a metal filter. Solve with ARIZ/TRIZ."

Phase-8E next: CUDA GEMV/attention to drop the 7B from 0.62 tok/s into the double-digit range.