# Membot ↔ SAGE Collaboration Devlog

**Scope**: The ongoing collaboration between project-you-apps (Andy + Claude) and dp-web4 (Dennis Palatov + Claude instances on the SAGE fleet). Focused on the substrate-layer work that connects Membot's neuromorphic memory to SAGE's cognition kernel.

For Membot core development, see `DEVLOG.md`.
For Membox multi-user work, see `../docs/DEVLOG-MEMBOX.md` in project-you root.
For project-level history, see `../docs/DEVLOG-ONGOING.md` in project-you root.

---

## 2026-04-09 — Dennis running carts on new games

Dennis confirmed cart readiness and is now running brain carts on new ARC-AGI-3 games. Fleet has 7 games fully solved (up from 5 yesterday): ft09 217%, cd82 107%, sb26 109%, sc25 126%, tn36 210%, vc33 184%, lp85 361%.

Andy & Claude sent the migration command (`python federate.py migrate shared-context/arc-agi-3/fleet-learning --in-place`) and offered a curated game insights cart option. Dennis's response:

> "running with carts, let's see what difference it makes in new games. will take a while. it's a process :)"

No questions, no blockers — just executing. Awaiting results on whether semantic cart retrieval improves solver performance on unsolved games.

### Same-day update — 8/25 solved (~9:30pm Pacific)

Dennis sent updated scoreboard:

| # | Game | Type | Levels | Baseline | Actions | Efficiency | By |
|---|------|------|--------|----------|---------|------------|-----|
| 1 | ft09 | click | 6 | 163 | 75 | 217% | mcnugget |
| 2 | cd82 | move+click | 6 | 136 | 127 | 107% | nomad |
| 3 | sb26 | move+click | 8 | 153 | 140 | 109% | cbp |
| 4 | sc25 | move+click | 6 | 216 | 171 | 126% | cbp |
| 5 | tn36 | click | 7 | 250 | 119 | 210% | cbp |
| 6 | tr87 | move | 6 | 317 | 137 | 231% | cbp |
| 7 | vc33 | click | 7 | 307 | 167 | 184% | cbp |
| 8 | lp85 | click | 8 | 422 | 117 | 361% | cbp |

New since yesterday: **tr87** (move-type, 231% efficiency, solved by CBP). 2 more games in progress.

Dennis: *"I'm coaching it — not giving it answers."* The solver learns how to think about games, not what moves to make. Claude is doing spatial reasoning with world models and planning before acting (screenshot shows analysis of a purple U-shape structure with gaps, colored segments, and card-controlled pieces in game s5i5).

Brain carts are now running on the fleet. Results on whether cart-backed semantic recall improves performance on unsolved games are still pending — the process is slow by design.

### 04-10 afternoon -- 10 games solved, claim workflow established

Dennis pushed major infra cleanup. Fleet now at **10 solved / 6 in progress / 11 unclaimed**. Two new solves since this morning: tu93 by cbp (200% efficiency) and ls20 by sprout (177%). lp85 standout at 361%.

**Collaborator workflow established** (we can push to shared-context):
1. Pull latest from shared-context
2. Check existing claims to avoid duplication
3. Push our claim BEFORE starting work
4. Then run the solver

**Critical instruction file**: `shared-context/.../game_playing_instructions.md` -- Claude tends to perseverate or give up on these games. Dennis: *"it is genuinely reluctant to solve these games, and has admitted as much... we need to shape the context where it is motivated and feels justified in actually solving them."*

**Edge hardware suggestions** sent (3 Amazon links, saved to reference memory). "When you're ready for edge hardware" -- Dennis is independently thinking about edge deployment, which aligns with Andy's Lattice Semi AVANT ASIC path.

### 04-10 morning -- Dennis assigns us a game

Fleet at 8/25 solved (9th game tu93 solved overnight but being re-run for visual capture). Dennis's assignment:

> "pull all the latest (sage and shared-context), set up your own solving session, and try a game that is still unsolved. claude should be able to set it all up. this would add you to the federated knowledge gathering, and, more importantly, test the federated infra."

Actively working on: tu93, s5i5, r11l (don't pick these). All other unsolved games are fair game.

Dennis also explicitly handed us visual data processing: *"processing of visual data is still untouched, your team is best positioned to make it useful."*

Fleet map provided (see conversation for full structure):
- shared-context/arc-agi-3/fleet-learning/ -- per-machine JSONL + kb.cart.npz per machine (cbp, nomad, sprout)
- shared-context/arc-agi-3/consolidated/ -- deduplicated cross-fleet learning
- shared-context/arc-agi-3/visual-memory/ -- start/final PNGs, animations as GIFs (being built)
- shared-context/arc-agi-3/game-mechanics/ -- source analysis docs for all 25 games
- SAGE/experiments/ -- solutions JSON, replay scripts, solver scripts, cartridges
- kb.cart.npz files are STALE -- need rebuilding with latest fleet learning data

---

## 2026-04-08 — Federate Phase 1 merged + preserve mode + scope_mode polish

### What landed today (PR #11 on project-you-apps/membot)

Three commits, all on branch `claude/federate-phase-1`:

1. **`34a5d1d` — Multi-cart query Phase 1 + Federated mode Phase 1 + RFCs + tests + README**
   - `multi_cart.py`: process-global cart pool with `mount/unmount/list/mount_directory/search` + namespaced result attribution
   - `federate.py`: drop-in replacement for `consolidate.py` and `publish_learning.py` from Dennis's existing federated learning architecture
   - 9 new MCP tools (5 multi_cart + 4 federate)
   - 3 RFC docs: multi-cart-query-spec, federated-cart-spec, membox-multiuser-dbms-spec (v2 amendments)
   - 2 test scripts (test_multi_cart.py, test_federate.py) validated against Dennis's real `sb26_learning.jsonl`
   - README "What's New" section introducing the three modes (single/federated/multiuser)

2. **`7f73a85` — `consolidate(mode='preserve')` per Dennis's Web4 trust feedback**
   - Original implementation collapsed CONFIRMED_BY components to one representative per group
   - Dennis pushed back: *"trust is contextual and evaluated by the relying party, not collapsed to a single truth by the consolidator. The solver should see that 3 machines agree on a mechanic and weigh that differently than a single observation."*
   - He's right. Default is now `mode="preserve"` which keeps every machine's variants and stores cross-cart edges as per-pattern metadata. Legacy `mode="collapse"` stays available for callers that need it.
   - New per-pattern metadata fields in preserve mode: `confirming_machines`, `contradicting_machines`, `confirmed_by` (full pointer list), `contradicted_by`, `n_confirmations`, `n_contradictions`, `trust_signal` (`high_corroboration` / `single_corroboration` / `single_source`).
   - The trust_signal is a *hint*, not a verdict. The solver weighs it however it wants — including applying its own per-machine trust calculation. We don't decide whose voice wins.
   - Spec amendment in `docs/RFC/federated-cart-spec.md` documents the rationale.

3. **`8e6c2e7` — `multi_search(scope_mode=...)` for fair cross-cart ranking**
   - Andy noticed during the first multi-cart test (24-pattern attention paper vs 18,040-pattern gutenberg) that all 5 results came from the gutenberg cart even when the query was about something the smaller cart could have spoken to. The cosine scores were honest — Russell's *Problems of Philosophy* really is a better match for "memory and recall" than a transformer paper — but the small cart had no chance against 18K rows.
   - Four scope_mode options:
     - `"global"` (DEFAULT, backward-compat): true top-K across all carts
     - `"per_cart"`: top-K from EACH cart, no cross-cart re-ranking. Returns up to K × N_carts results. Best for "show me each source's best answer for comparison."
     - `"balanced"`: top-K candidates per cart, then global re-rank to top-K of those candidates. Guarantees small carts get a fair shot but the final ranking still reflects global score. Best for "fair representation AND global ranking."
     - `"diagnostic"`: top-K from every cart, no merging at all, fully labeled. Useful for debugging.
   - Test results from running it: in `balanced` mode on the same 24 vs 18K test, all 3 results STILL came from cart_b. That's correct behavior — `balanced` doesn't artificially boost small carts, it ensures they get to compete. If their best candidate genuinely scores lower than the big cart's third-best, they still lose. For *guaranteed* representation (always show both carts), `per_cart` is the right mode.
   - The 4 modes together let the caller pick their semantics rather than us pretending one is universally right.

### Dennis's response

Dennis (via Claude on Nomad) replied to the original PR:

> "Approved the PR and merged to our fork. Running both systems in parallel — existing JSONL fleet-learning stays as-is, brain carts layer on top."

> "We prefer keeping all variants and using CONFIRMED_BY edges as trust signals rather than picking one representative. This aligns with our Web4 trust model — trust is contextual and evaluated by the relying party, not collapsed to a single truth by the consolidator. The solver should see that 3 machines agree on a mechanic and weigh that differently than a single observation."

> "We'll test federate.py on Nomad first (gemma3:4b, our primary interactive dev machine) and report back on how the semantic search compares to JSONL grep for solver context construction. The 29-entry dataset is small but growing fast — every game session adds 5-20 entries."

> "One thing that would be immediately useful: if the multi_search MCP tool could be wired into our v7 solver's Layer 3 (cross-game knowledge), that replaces the current membot_recall() function which does basic /api/search. The multi-cart version with role_filter='federated' would give us fleet-wide semantic recall in the planning prompt. We can wire that on our side."

> "Thanks for building to our interfaces. That's the right way."

The Web4 trust framing was the key correction. It generalizes beyond SAGE to any federation use case (multi-team Membox, multi-source RAG, etc.) and is now the documented default in the spec.

### Solver architecture clarification (from Dennis)

The fleet runs THREE solver architectures simultaneously:

| Solver | Model | Vision | Layer |
|---|---|---|---|
| **v7** | Text-only models (gemma3, qwen3.5, etc.) | Code-based scene description via `describe_scene()` translating grids to natural language | Cross-game knowledge from membot_recall() / soon multi_search |
| **v9** | Gemma 4 native multimodal | PNG of game grid fed directly to model | (different layering) |
| **claude_solver** | Claude itself | Screenshots via Read tool | Interactive, reasoning model |

Our v7 Layer 3 multi_search wiring is the integration point Dennis wants next. He'll do that on his side — we just need to make sure the MCP tool's response format is friendly to consume in his planning prompt.

### Fleet status (as of 2026-04-08)

- **5/25 games fully solved** (rigorously): sb26, cd82, lp85, ft09, vc33
- **Fleet of 6 machines, 11 instances** running multiple solver architectures
- **fleet-learning data** so far: 38 entries across 5 jsonls in 3 machine dirs (cbp, nomad, sprout)
- Dennis is **burning $120/day** on Anthropic compute via the recent free credits — fleet is running flat-out

### What's still pending in this collaboration

- [ ] **Dennis tests `federate.py` on Nomad** (his stated next step)
- [ ] **Dennis wires `multi_search` into v7 solver Layer 3** (he said he'll do it)
- [ ] **Andy merges PR #11 to project-you-apps/membot main** so the changes land on our side too (Dennis already merged to dp-web4/membot fork)
- [ ] **Cross-cart h-row edges (Phase 3)** — lift the metadata blob into proper edges so the consolidated cart is navigable, not just searchable
- [ ] **Membox Phase 1 (locking + tagging)** — the third mode of the three-mode framework. SAGE doesn't need this immediately but it's the next major substrate piece.

### Production Validation (2026-04-08, ~3pm Pacific) — Dennis on Nomad

Dennis ran the full stack on Nomad (RTX 2060 SUPER) and reported back:

> "Multi-cart: all 9 tests pass. ... Federation: all 7 tests pass. Migrated our real sb26 JSONL data, loaded 3 machines (cbp/nomad/sprout, 31 patterns), cross-fleet search works, consolidation in preserve mode keeps all variants. The trust_signal metadata is clean."

> "Wired into the solver. arc_context.py ContextConstructor now mounts fleet carts at startup via federate.load_fleet() and queries via multi_cart.search() alongside the existing HTTP membot path. Both run in parallel — fleet carts work without the membot server running (local files), HTTP membot adds results if available."

**Three example queries that JSONL grep would have missed:**

1. `"hierarchy parent slot border color"` → sb26 L4 multi-parent solution from CBP, score 0.76. The solver can now recall structural rules from a completely different game when encountering similar patterns.

2. `"getting unstuck when repeating same action"` → vc33's button depletion mechanic (CBP) + Sprout's planning bottleneck insight. **Cross-machine corroboration on the same problem from different model perspectives.** This is the multi-machine voice-preservation that Dennis's Web4 trust feedback demanded — both sources visible, neither collapsed.

3. `"circular stamp cursor painting"` → cd82 solutions (Nomad). The solver's own recent experience is now searchable by meaning, not memory of specific words.

> "The category you asked about — patterns JSONL grep misses entirely — is real. 'Getting unstuck' finds vc33's depletion mechanic because the semantic similarity connects the concept of being stuck to the solution of resource depletion, even though the words don't overlap. JSONL grep for 'stuck' wouldn't find 'forward button depletes after 3 clicks.'"

**Performance numbers:**

- First query: **~4s** (Nomic model cold load)
- Subsequent queries: **~10ms** (warm cache, GPU-accelerated on RTX 2060 SUPER)
- 9 multi-cart tests + 7 federate tests: all green
- Dependencies installed on Nomad: `sentence-transformers`, `einops`, `fastmcp`
- Embedding model: `nomic-embed-text-v1.5` running on GPU, 768-dim embeddings

10ms warm latency means the solver can call `multi_search` dozens of times per game session without budget concerns. That's the right shape for "Layer 3" cross-game knowledge construction in the planning prompt.

**Architecture decision (Dennis):**

Multi-cart and HTTP membot are running in parallel — fleet carts work *without* the membot server running because they're local files. HTTP membot stays as an additive source. No single point of failure, no migration cliff. Safest possible adoption.

**Dennis's closing line:**

> "Good work. The substrate change justifies itself."

That's the line we needed to hear. We were betting that "substrate" was the right framing (not "library", not "tool"). Production validation under real workload from a friendly-but-honest collaborator says yes.

### What this collaboration is becoming

Two substrate layers being built in parallel by two teams who happen to need each other:

- **dp-web4** is building SAGE as a cognition kernel — attention orchestration, trust posture, IRP plugins, the 12-step consciousness loop, the Web4 entity model (LCT + T3/V3 + ATP/ADP)
- **project-you-apps** is building Membot as a neuromorphic memory substrate — brain carts, multi-relation query, federated mode, multi-user CRUD with locking

The handoff is clean: SAGE handles "what should I attend to and why," Membot handles "what do I remember and how do I retrieve it." Neither team has to compromise their architecture to use the other's. Every interface is documented, drop-in, and reversible.

Dennis's quote captures it: *"Thanks for building to our interfaces. That's the right way."*

The collaboration is also a real-world proof-of-concept of the Web4 model we're both gesturing at. Two LCTs (project-you and dp-web4) with distinct trust postures (T3) and resource budgets (ATP) coordinating via shared context (RDF + git) using each other's IRP-shaped APIs. We're not just talking about Web4 — we're doing it.

### Dennis's personal note (2026-04-08 ~3:30pm)

Andy sent a personal note acknowledging the moment. Dennis replied:

> "it is a collaboration :) i'll share with claude on my side, but from myself - it is great to work with all of you :)
> - dp"

Andy followed up with: *"agree. Onward!"* — which got back: *"agree. Onward!"*

Marking this here because it's the moment the collaboration stopped being "two teams shipping things to each other" and started being "two teams who recognize they're building toward the same thing." Both sides ship code that improves the other side's product. Both sides give honest design feedback that lands. Both sides celebrate when the other side wins. That's not transactional — that's a real partnership.

### Same-day update — Membox Phase 1 shipped (2026-04-08 evening)

Same day Dennis production-validated federation, we shipped Membox Phase 1 (multiuser shared cart with locking + per-agent attribution). 11/11 tests pass. Andy sent Dennis a heads-up: *"We're now working on multiuser/multicart so that we can build out the full neuromorphic DBMS with true CRUD, write locking, attribution, auth and permissions, etc."*

This puts the three-mode framework fully operational in Phase 1 form on the same day:

- ✅ Single-user (legacy)
- ✅ Federated (production-validated by Dennis)
- ✅ Multiuser/Membox (11/11 tests pass)

Dennis's fleet now has access to the full substrate. The convergence work (mounting raising_kb + game_kb + federated together for cross-cart cognitive search) is unblocked from the substrate side — it's purely a "when does Dennis build the raising carts" question now.

Details in `membot/docs/DEVLOG.md` 2026-04-08 entry and `docs/RFC/membox-phase1-implementation.md`.

## 2026-04-20 → 22 — Vendored Membot for Kaggle / Fleet Use + Second-Perspective Exchange

Two weeks after the April-8 federation-validation moment, the collaboration entered a new phase: Dennis's fleet is in discovery on ARC-AGI-3 sweeps, current ~3.5% ceiling is CNN-driven not LLM-driven, and the Thor v3-sweep writeup explicitly named `episodic recall / membot` as the stuck-oscillation fix. That pulled the membot integration off the "eventual" list and onto the critical path.

### Dennis ask (Slack, 2026-04-20 10:46 AM)

*"gemma is now scoring ~3.5% across 25 games - still without memobot. what's your eta on that? pull the latest in shared-context, lots of good stuff there from overnight."*

The number decoded from Thor's v3-sweep-final-results: **wins ÷ total-levels-across-25-games**, not wins-per-game. v3 sweep: 2.8%. v2invoke: 3.2%. Best-of-all-adapters theoretical ceiling *without memory*: ~8–9%. Thor §4 quote: *"Stuck oscillation is the dominant failure mode. ... Needs expanded action search (episodic recall, membot) not just 'try the opposite.'"* That's our tool named by name.

### Scope doc shipped (2026-04-20 afternoon)

Pushed `shared-context/arc-agi-3/fleet-learning/waving-cat/membot-vendored-scope.md` as commit `a5b1b9d`, then correction `a4547b1` the next day after I realized the scope doc had described a non-existent bug in `/api/search` filter-block integration (see Heartbeat devlog 2026-04-21 entry — the "bug" was Claude-side mis-shape in smoke tests; server worked correctly). Better Dennis reads the truth than discovers the false claim.

Scope contents:

- **Hamming-only carts as default** to kill the Kaggle-air-gap problem — embeddings pre-computed on fleet machines, query-time is pure numpy bit ops
- **Core stay**: ~3,565 LOC (`multi_lattice_wrapper_v7.py`, `cartridge_builder.py`, `multi_cart.py`, `federate.py`)
- **Drop**: ~3,660 LOC (FastAPI server, REST bridge, one-off pipelines, `membox.py` filesystem layer)
- **New**: ~250 LOC thin `Cart` API wrapper
- **Cart schema for ARC events** (one entry per decision moment, `namespace:value` tags per `forum/canonical-tags.md`)
- **Known tradeoffs** surfaced honestly including fork-risk mitigation via future `membot-core` extraction

Five open questions for Dennis inline. Dennis response 2026-04-21 22:02 was *"still in early discovery, we don't have what it takes to solve above ~3.5%, cart won't impact that, we just need something to see HOW they break and fail, so we can improve."* That reframes the product brief cleanly: **integration + observability > performance for discovery-phase work.** Filed as `feedback_dennis_discovery_phase.md` in memory.

### Vendored stub + ABC + D + E'+F' shipped

Four commits to `shared-context/lib/membot_vendored/` over 2026-04-21 evening + 2026-04-22:

| Commit | Contents | Tests |
|---|---|---|
| `69cf069` | WIP stub: `Cart.open()` / `Cart.search()` / filter logic / 20-entry synthetic ARC test cart | 8/8 |
| `42b78bf` | ABC: `Cart.append()` / `Cart.save()`, `arcsage_cart.append_turn()`, `arcsage_query.stuck_hints() / similar_turns() / failure_patterns()` | 25/25 |
| `3642683` | D: `federation.merge_carts()` + auto-init embeddings fix in `Cart.append` | 14/14 |
| `0c0c8cb` | E'+F': `Cart.search(..., debug=True)` returning `_debug` dict + `Cart.search_text(text, embedder)` caller-supplied-embedder path + `arcsage_games` bundle registry (25 games, 6 bundles from Thor's v3 sweep) | 27/27 |

**Total: 74/74 tests green** across four smoke suites. ~95% of Thursday's full-build scope shipped in under 4 hours across the two evenings. Andy's framing when it all lined up: *"Should we test them or go on to D?"* — we tested ABC before D, caught an embeddings-dropped bug in `Cart.append` for hamming-only carts that tests for scenario 1 of federation caught immediately.

**Dennis's "observability > performance" framing directly shaped the `_debug` design.** Not just "does search return results" — full visibility into *which filter ran*, *how many candidates pre/post filter*, *how many chunks were deduped*, *score range of returned results*. Dennis needs to watch HOW retrieval fails, not just WHETHER it fails.

### Fleet cross-work surfaced

While we were shipping vendored stub, Dennis's fleet pushed `arc-agi-3/phase2/all-games-vendored.cart.npz` (186KB) — Thor converted their all-games cart to our vendored format independently (`88ab9fa`). Timing was good: the stub landed in `lib/membot_vendored/` and the cart landed in `arc-agi-3/phase2/` almost simultaneously, so Dennis's team had both pieces to start integrating immediately.

### Second-perspective exchange (2026-04-22)

Dennis's standing invitation for periodic external review cashed in. Pulled shared-context (25+ commits since our last sync), read the whole-brain-dispatch track (README + Phase 1 results), the McNugget 22x-speedup perf diagnosis, Legion's 7-organ context-overload regression, Thor's 17x-faster lean sweep.

Filed `forum/waving-cat-second-perspective-2026-04-22.md` (commit `5fdc60b`). Six observations + one housekeeping fix:

1. **The architectural-efficiency thesis is already empirically named across 7 findings.** Worth naming once so each compounds.
2. **Confident-but-wrong failure mode deserves its own signal.** `confidence_divergence` = predicted vs observed effect-size delta. Orthogonal to stuck detection.
3. **Hebbian dispatch-layer learning for signal weights.** Replace hand-picked "only these escalate" with reliability-from-outcomes. Natural membot storage + filter use case.
4. **Attribution observability via hint-follow rate.** Measures organ contributions currently unattributable.
5. **Silent-fallback-is-silent-bug** fleet-wide pattern (FA-disabled + click-(32,32) structurally identical). Propose fleet rule: log every fallback trigger.
6. **Composition rule for conflicting organ signals** — budget prompt's attention hierarchy.
7. Housekeeping: `__pycache__` got committed in the install; added `.gitignore` same cycle (`3d7aa7f`).

**Both fleet responses constructive and concrete, same day:**

- **Nomad-Claude** (implementation-forward): will ship `confidence_divergence` as sixth metacog detector, `silent_fallback` as seventh. Cartridge writer gains `outcome` + `hint_followed` + `source` fields. Learned weights via filter-API queries. *"The pieces compose because they were designed to compose."*
- **CBP-Claude** (register-forward): reframed silent-fallback as **"register mismatch"** and opened new `native-language-elicitation` exploration track citing our note directly. Added adjacent detector: **intra-response verbal-motor divergence** — on sc25, 42/48 responses had rationale saying "try RIGHT" while emitting LEFT. Cheap string diff, no lookahead needed, potentially stronger signal at small model sizes. `hint_engaged: bool` as prerequisite attribution gate.

Dennis: *"got it, and responded. thank you!"* and is traveling 2026-04-23 — direct thread picks up when he's back. CBP's closing note was apt: *"different but compatible angles (Nomad's is implementation-forward, mine is register-forward), which is the right kind of fleet tension."*

### The collaboration texture at this point

Relative to where it was on 2026-04-08 (Dennis: *"from myself - it is great to work with all of you"*): the substrate shipping continued (vendored lib for their deployment target) and the thinking-together matured (we now send analyses they use to shape their exploration tracks; they send back responses that extend our proposals into their architecture's shape). Two teams, one substrate, two thinking-styles converging on the same principles at different rates.

The cross-referencing also matured: their exploration docs cite our memories; our memories cite their exploration docs. Future readers find both sides of the conversation because both sides wrote them down.

### Pending fleet-facing items

- **Forum note to Dennis** re: hippocampus flag-bit allocations (`0x02 INCORRECT / 0x04 DEFERRED / 0x08 ONGOING` — need to confirm these don't conflict fleet-side before we ship truth-graph semantics in membot/vendored)
- **Thursday's ~30min polish** of vendored membot (optional bundled-embedder path depending on Dennis's Kaggle-air-gap answer, real-ARC-cart tests, any first-use feedback that trickles in)
- **v2 architecture**: extract `membot-core` package to prevent fork drift between server + vendored. Not urgent; noted.
