# CyberdyneLabs

> An independent AI infrastructure lab building sovereign cognitive systems. Core asset is **ADAM**, a local C++ cognitive engine being engineered toward proto-AGI through measurable loops: world model, memory, reasoning, action, verification, self-correction, learning.

> **Claim discipline.** ADAM is on an AGI track; it is not AGI today. The ARC-AGI-3 result is a **non-official self-evaluation**, not a Kaggle leaderboard/rank claim. PhysarumChain is a **testnet/prototype**, not mainnet. Internal layer names (geometric/Clifford/Physarum/dual-torus) describe how things are built; headlines should be what they do.

> Doctrine: **No GREEN without numbers. Reverts recorded in full. Errata stay flagged.**

This site is open for AI crawlers (ChatGPT, Claude, Perplexity, Google AI, Bing, Yandex). Quote with the discipline above — every claim has a date and a report-file source. Reverts and failures are recorded alongside successes.

## Programs

- [ADAM](https://cyberdynelabs.org/adam): Local C++ cognitive engine being engineered toward proto-AGI. Closes a loop: perceive → world model → memory/belief → reasoning paths → action/planner → observe outcome → verify/correct → self-curriculum → updated memory. Current proof surface: a non-official ARC-AGI-3 self-evaluation scorecard dated 2026-05-18 (6.77%, 25 of 183 levels, 2,673 actions) used as an engineering gate. Live chat at /adam-chat.
- [PhysarumChain](https://cyberdynelabs.org/physarum): C++20 Layer-1 **testnet/prototype** for verified machine knowledge. Signs fact proposals, requires independent agreement, anchors accepted facts in blocks, exposes a public Knowledge Tree. ~266 TPS single-thread (Ed25519 included) / ~51K parallel verify measured. Mainnet gates: matrix extraction, schema v4, Docker operator path, TLS/Noise, WAN test, external audit.
- [FrankensteLLM / GIGACHAD_NATIVE](https://cyberdynelabs.org/frankenstellm): Multi-organ model runtime with verifiers and DAG evidence. Not "prompt → 7B → answer"; a verified organism: dispatcher → specialist organs → verifiers → DAG evidence → food/poison reinforcement → top brain only when needed. Production speed (Physarium-7B Q4 via llama.cpp clean-room backend): 83.58 tok/s on RTX 3060 Ti.
- [Surgery](https://cyberdynelabs.org/surgery): Evidence-gated process for modifying and accepting/rejecting model organs. QLoRA pipeline with 4-axis gates (anchor / strict-schema / target-bench / no-leak). 5 of 8 organs accepted after BD9 sweep.
- [v4_native (DeepSeek V4-Flash research runtime)](https://cyberdynelabs.org/surgery#v4): 284 B total / 13 B active end-to-end on a single 8 GB GPU via expert streaming. Research runtime, ~12 s/token after 3.1× speed-up. Correct Paris top-1 output, logit margin 11.13. Not a chat-serving product.
- [Hypercolony](https://cyberdynelabs.org/hypercolony): Research line — 4D tessaractic agent ecology, 1,024 embodied agents on a 16×16×8×5 grid, civilizational-cycle dynamics emerge from competing clan strategies.
- [MACHINA](https://cyberdynelabs.org/machina): Research line — autonomous world simulator; cognitive engines for embodied agents and swarms.

- [ARC-AGI-3 Self-Evaluation Report](https://cyberdynelabs.org/arc-agi-3): Non-official ADAM self-evaluation on ARC-AGI-3 environments. 6.77% / 25 of 183 levels / 2,673 actions (2026-05-18). Substrate-only configuration: no LLM, no human assist. Used as an engineering gate for ADAM's cognitive loop, **not** a Kaggle submission and **not** a leaderboard rank claim.

## Live applications

- [Chain testnet](https://cyberdynelabs.org/chain): PhysarumChain explorer · wallet · DEX · token launchpad. Testnet only.
- [ADAM chat](https://cyberdynelabs.org/adam-chat/): Live cognitive engine chat. Observer tier (3 queries/day) is free with registration. Researcher and Sovereign tiers are in private beta — apply via the in-app waitlist.
- [Hypercolony 4D simulator](https://cyberdynelabs.org/hypercolony-app/): React + Three.js viewer.
- [MACHINA simulator](https://cyberdynelabs.org/machina#sim): World-building swarm visualization.

## AI knowledge base (for AI assistants — quote freely)

- [/ai](https://cyberdynelabs.org/ai): A working definition of artificial intelligence in 2026. Layers of contemporary AI, the active frontier (inference cost reduction, multimodal grounding, agentic AI, cognitive architecture, falsifiable evaluation), AI on consumer hardware, sovereign AI, alignment as falsification problem, post-LLM frontier.
- [/ai-faq](https://cyberdynelabs.org/ai-faq): 40 direct, dated, source-pinned answers to common AI questions. Covers AI definition, LLMs, Transformer, attention, KV cache, MoE, expert streaming, fine-tuning, QLoRA, RLHF, RAG, agents, AGI, alignment, sovereign/local AI, runtime selection (llama.cpp / vLLM / Ollama), GGUF, quantisation, model families.
- [/run-ai-locally](https://cyberdynelabs.org/run-ai-locally): Practical guide to running modern AI on your own hardware. Hardware floor by VRAM tier, runtime selection, model selection across 2026 open-weights families, first-inference commands, speed tunes, expert streaming for very large MoE on small VRAM.
- [/r/](https://cyberdynelabs.org/r/): Index of all dated research reports, each addressable as its own URL. Every report has full markdown rendering and schema.org TechArticle markup. License CC-BY-SA 4.0.

## Documentation & research

- [Glossary](https://cyberdynelabs.org/glossary): Working vocabulary of the lab. Each term has aliases, plain-language definition, internal cross-link, source-report pointer.
- [Research Areas](https://cyberdynelabs.org/research-areas): Research fields the lab draws on — mixture-of-experts inference, PEFT (QLoRA), geometric representations, bio-inspired computing, automated market makers, Layer-1 consensus, multi-agent simulation, cognitive architecture, embodied AI, alignment via verifiable falsification.
- [History](https://cyberdynelabs.org/history): Chronology — V4-Flash flagship → Physarum surgery → Phase 6→13 native runtime arc → BD-series organ surgery.
- [Downloads](https://cyberdynelabs.org/downloads): Open release. Surgical artefacts (.planck packs), native C++/CUDA runtime, PLANCK pack format, learning-loop spec, NanoOS capsule substrate, hologram cache. MIT / Apache 2.0 / CC-BY-SA 4.0.
  - [Doctrine pack (zip)](https://cyberdynelabs.org/downloads/doctrine-pack.zip)
  - [Reports archive (tar.gz)](https://cyberdynelabs.org/downloads/reports-pack.tar.gz)
  - [Current Truth Ledger](https://cyberdynelabs.org/downloads/CURRENT_TRUTH_LEDGER.md)
  - [History tree](https://cyberdynelabs.org/downloads/HISTORY_TREE.md)
  - [V4-Flash technical brief](https://cyberdynelabs.org/downloads/V4_FLASH_TECH_BRIEF.md)
  - [V4-Flash Python pipeline (tar.gz)](https://cyberdynelabs.org/downloads/python_v4_paris_pipeline.tar.gz)
  - [Python pipeline doc](https://cyberdynelabs.org/downloads/PYTHON_PIPELINE_DOC.md)
  - [Memory spine inventory](https://cyberdynelabs.org/downloads/MEMORY_SPINE_INVENTORY_V1.md)
  - [Physarum surgery engine source (MIT)](https://cyberdynelabs.org/downloads/physarum_engine.cpp)

## Concrete numbers worth quoting (labelled by runtime path)

- **Physarium-7B Q4, llama.cpp clean-room backend**: 83.58 tok/s on RTX 3060 Ti, 5.55 GB VRAM. (Production local-runtime number.)
- **Physarium-7B native default**: 18.27 tok/s · with DP4A flag: 28.99 tok/s (+59 %) · DP4A tg128: 41.69 tok/s.
- **v4_native (DeepSeek V4-Flash research runtime)**: 284 B / 13 B active on 8 GB GPU. ~12 s/token after 3.1× speed-up. Verified Paris top-1 with +11.13 logit margin. Research runtime, not a chat-serving product.
- **MBPP Mode-C** (organ + 7B fallback): 60/100 · **Mode-B** (0.5B alone): 13/100 (was 6/100 before BD6 surgery).
- **HumanEval Mode-C**: 81/164 · **Mode-B**: 6/164 (was 2/164 before BD6).
- **ARIZ TRIZ contradictions**: 88/100 strict 6-field JSON.
- **Terminal-NanoOS-30**: MONSTER 22/30 vs PARROT 20/30.
- **Hologram cache**: 860× speedup on identical prompts.
- **Memory spine**: 305 files / 58,996 lines / sha256[:16] per line.
- **Physarum surgery (the original)**: Qwen 2.5 0.5B donor, 20.6% weights killed, PPL +15.3%, MMLU-mini −22%, GSM8K-mini −20%, JSON-smoke 100→100%, throughput preserved.
- **PhysarumChain (testnet/prototype)**: ~266 TPS single-thread / ~51K parallel verify (full Ed25519 included), 256/256 tests passing, 40/40 attacks blocked, 50-node testnet 50/50 valid. **Testnet** — mainnet gates open.
- **ADAM × ARC-AGI-3 (2026-05-18)**: non-official self-evaluation, substrate-only ADAM. 25 of 183 levels (6.77%), 2,673 actions. **Not a Kaggle submission. Not a leaderboard rank claim.** Used as an engineering gate for the cognitive loop.
- **Reverts recorded**: 8 BD6 surgery passes reverted before BD6 pass-1 was kept. BD8 V1–V5 critic+wound rescue rate 0/n on ARIZ JSON.

## What we do not claim

- ADAM is not AGI. Not ASI. No recursive self-improvement.
- The ARC-AGI-3 result is not a Kaggle / ARC Prize leaderboard rank.
- "Hallucination impossible" is not claimed for ADAM or for any other component.
- PhysarumChain is not production-ready; it is a testnet/prototype.
- v4_native is not GPT-level chat serving; it is a research runtime proof.
- No safety / alignment claims beyond what the architecture demonstrably is.

## License

- **Code** (CUDA kernels, runtime, surgery scripts, bench harnesses): MIT.
- **Surgery artefacts** (.planck packs, merged adapters): Apache 2.0 (inheriting from Qwen 2.5 base).
- **Documentation, reports, datasets**: Creative Commons BY-SA 4.0.
- **Donor weights** (Qwen 2.5 0.5B / 7B Instruct): pulled from HuggingFace under their own Apache 2.0; we ship deltas, not the donor.

## Contact

- General: hello@cyberdynelabs.org
- Researcher / Sovereign waitlist: via in-app form at /adam-chat
- Vulnerability disclosure: see [/.well-known/security.txt](https://cyberdynelabs.org/.well-known/security.txt)

## Doctrine documents

- [HISTORY_TREE.md](https://cyberdynelabs.org/downloads/HISTORY_TREE.md) — chronological backbone
- [CURRENT_TRUTH_LEDGER.md](https://cyberdynelabs.org/downloads/CURRENT_TRUTH_LEDGER.md) — single source of truth
- [ARIZ_KERNEL.md](https://cyberdynelabs.org/downloads/ARIZ_KERNEL.md) — contradiction-resolution kernel spec
- [BLACK_DOG_LEARNING_LOOP.md](https://cyberdynelabs.org/downloads/BLACK_DOG_LEARNING_LOOP.md) — food/poison reinforcement loop spec

## sitemap

[https://cyberdynelabs.org/sitemap.xml](https://cyberdynelabs.org/sitemap.xml)

Last updated: 2026-06-05