docs: merge tooling findings into SYNTHESIS/GOTCHAS/CORPUS_* and add handoff

Patches the top-level corpus docs with the 13 findings flagged during the
2026-04-18 canonical tooling research pass. tooling/README.md now marks each
finding [merged: <file>] or [flagged] for provenance.

- CORPUS_ollama_variants.md: annotate gemma4:26b as MoE (25.2B total / 3.8B
  active, 8-of-128 experts + 1 shared). Note Q4_K_M inference is standard
  (the "MoE quality degrades at 4-bit" caveat is training-only). Add note
  that audio on E-series is NOT available via Ollama — llama.cpp mmproj
  or vLLM only.
- CORPUS_capabilities.md: native system role, configurable thinking mode,
  first trained tool use (vs Gemma 1/2/3 proof-of-concept), native object
  detection with bbox output in 1000x1000 coords, pointer to EmbeddingGemma
  for retrieval (Gemma 4 has no embedding mode).
- CORPUS_tool_calling_format.md: add Chat Template Context section
  documenting the <|turn>/<turn|> asymmetric brackets (new in Gemma 4,
  replaced <start_of_turn>/<end_of_turn>) plus <|think>, <|channel>,
  <|image>, <|audio> tokens. Add HF transformers Alternative section
  showing processor.parse_response with response_schema.
- GOTCHAS.md: add MEDIUM gotcha for abandoned google/gemma_pytorch (no
  Gemma 4 support since 2025-05-30). Expand fine-tuning section with FA2/FA4
  head_dim=512 break, fused LoRA kernel issues, 26B A4B training-quant
  guidance, new tool-call tokens as learned embeddings.
- SYNTHESIS.md: add banner pointing to tooling/ for canonical upstream
  material. Add embeddinggemma row to Model Selection table.

Also:
- Add .gitignore excluding .backup/ (local scratch per global CLAUDE.md
  convention, not needed in tracked history) and __pycache__/.
- Add .claude/handoffs/2026-04-18-canonical-tooling-research.md so future
  sessions can pick up cold — facts verified, open threads, what changed.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

CORPUS_capabilities.md

@@ -4,8 +4,9 @@
 
 ### Text (all variants)
 - Standard instruction-following, chat, completion
-- System prompt support (critical — see synthesis)
-- 128K context window (training length)
+- **Native `system` role support** (new in Gemma 4; Gemma 3 prepended system as user turn)
+- **Configurable thinking mode** — `<|think|>` / `<|channel>` tokens in the chat template; Ollama `think: true/false` flag. Seth's finding (see GOTCHAS): keep `false` for tool-use workloads.
+- 128K context window (E2B/E4B) / 256K (26B/31B) — training length
 - 262K vocabulary
 
 ### Vision (all variants)
@@ -38,6 +39,13 @@
 - Simon: 6 genealogy tools, up to 12 sequential iterations
 - Supports parallel tool calls in single response
 - Weak at deeply nested JSON schemas -> prefer sequential calls
+- **First Gemma generation with tool use as a trained capability.** Gemma 1/2/3 tool use was "proof-of-concept" (per the DeepMind tool_use colab). Gemma 4 has dedicated tool-call tokens and is trained on the pattern.
+
+### Native Object Detection (all variants)
+- **Prompt format:** "Detect the {object} in this image" → structured output `{box_2d: [ymin, xmin, ymax, xmax]}` in **1000×1000-normalized coordinates** (rescale to your actual image dims).
+- Images auto-resized to multiples of 48 pixels by the processor.
+- Useful for grounding, cropping, counting, or passing bboxes to downstream tools — no separate detection model required.
+- Documented in the HF model card (`tooling/huggingface/model-cards/gemma-4-*.md`). Not tested by Seth yet.
 
 ## Benchmark Context (vs Gemma 3)
 
@@ -51,5 +59,6 @@
 
 - No native code execution / sandboxing
 - No web browsing or retrieval
-- Audio only on E-series (not the models most people run)
+- Audio only on E-series (not the models most people run) — and **not on Ollama**, requires llama.cpp mmproj or vLLM
 - No built-in RAG — tool calling can implement it
+- No embeddings — use `EmbeddingGemma` (308M, separate model) for retrieval/semantic search