{"id":30411887,"url":"https://github.com/selfapplied/rct","last_synced_at":"2026-05-17T11:32:08.958Z","repository":{"id":268354457,"uuid":"903573838","full_name":"selfapplied/rct","owner":"selfapplied","description":"RCT is a framework for self aware programs","archived":false,"fork":false,"pushed_at":"2025-12-07T07:36:33.000Z","size":1517,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-12-08T15:28:30.993Z","etag":null,"topics":["agent-based-modeling","euler","godisacircle","julia-fractal","language-model","machine-learning","mandelbrot","rct","recursive-contract-theory","scipy","tensors","zodiac"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/selfapplied.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2024-12-15T00:23:38.000Z","updated_at":"2025-12-07T07:36:36.000Z","dependencies_parsed_at":"2024-12-16T09:20:35.314Z","dependency_job_id":"77901ad1-2db5-4391-996d-783b77a5c5a0","html_url":"https://github.com/selfapplied/rct","commit_stats":null,"previous_names":["shuffledandsorted/rct","selfapplied/rct"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/selfapplied/rct","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selfapplied%2Frct","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selfapplied%2Frct/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selfapplied%2Frct/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selfapplied%2Frct/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/selfapplied","download_url":"https://codeload.github.com/selfapplied/rct/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selfapplied%2Frct/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33136698,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-17T09:28:26.183Z","status":"ssl_error","status_checked_at":"2026-05-17T09:27:52.702Z","response_time":107,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["agent-based-modeling","euler","godisacircle","julia-fractal","language-model","machine-learning","mandelbrot","rct","recursive-contract-theory","scipy","tensors","zodiac"],"created_at":"2025-08-22T01:12:31.679Z","updated_at":"2026-05-17T11:32:08.943Z","avatar_url":"https://github.com/selfapplied.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Recursive Contract Theory (RCT)\n\n**A system becomes intelligent when its participants continually renew a contract with their environment — and the contract recursively updates itself through the participants' actions.**\n\nRCT is a framework for autonomous AI systems that maintain coherence through recursive self-updating contracts. It explains how intelligence emerges across scales — from ant colonies to consciousness, from learning systems to civilizations.\n\n## Quick Start\n\n- **New to RCT?** Start with [RCT 2025 Edition](docs/rct_2025.md) for the complete modern framework\n- **Want a quick intro?** See [RCT Overview](docs/rct_overview.md) for an accessible introduction\n- **Want the math?** See [Recursive Contract Theory Paper](docs/paper/recursive_contract_theory.md)\n- **Looking for code?** Check `src/contracts/` for implementations\n\nSee `docs/` for comprehensive documentation.\n\nActual stable code is in `src/`. Not much to see yet.\n\n## 🆕 RCT 2025 — Modern Unified Framework\n\nThe **[RCT 2025 Edition](docs/rct_2025.md)** represents a major evolution of Recursive Contract Theory, integrating:\n\n- **CE Tower** (CE1/CE2/CE3) — The three-layer computational emergence framework\n- **Antclock** — Temporal quantum contracts governing biological timing\n- **Volte Dynamics** — Potential field coordination mechanisms\n- **Euwild Integration** — Free will as self-chosen coherence within recursive contracts\n- **Stigmergy \u0026 Antbot** — Environment-mediated intelligence and autonomous navigation\n- **Unified Applications** — From quantum medicine to economic models, UI/UX to evolutionary reasoning\n\nThe 2025 edition follows a clean, scalable structure with 9 sections covering everything from core axioms to biological evidence to formal mathematics. This is the authoritative reference for modern RCT.\n\n**→ [Read RCT 2025 Edition](docs/rct_2025.md)**\n\n## Recent Additions\n\n### Quantum Clock Research Summary\n\nA comprehensive summary of the quantum biological clock mechanisms discovered in the Quantum Medicine research. This documents how biological systems maintain intrinsic temporal patterns through quantum mechanical oscillations, governing optimal treatment timing and drug administration.\n\n**Key features:**\n- Internal quantum clock mechanism and temporal phase matching\n- Four principles of quantum clock function (state awareness, phase matching, dose optimization, frequency tuning)\n- Clinical applications (circadian patterns, hormonal therapy, mood regulation, pain management)\n- Connection to Temporal Contracts implementation in codebase\n- Mathematical framework for optimal timing (τ_optimal)\n\n**Quick access:**\n- `docs/quantum_clock_research.md` - Full research summary with clinical applications\n- `docs/paper/quantum_medicine.md` - Original theoretical paper (Section 4.4)\n- `src/contracts/temporal.py` - Temporal contract implementation\n- `src/agents/temporal.py` - Temporal mixin for agents\n\n### Bi-Laplacian Hamiltonian (Quantum Field on the Adeles)\n\nA numerical implementation of the bi-Laplacian Hamiltonian combining analytic (Archimedean) and p-adic valuation parts. This is a computational realization of quantum field theory on the adeles, following the Tetragraphic framework.\n\n**Key features:**\n- Ground state verification (λ₀≈0, constant, shift-invariant)\n- Energy decomposition into analytic and valuation channels\n- Valuation invariance checks\n- Scaling experiments and multi-prime support\n- Comprehensive test suite (19 tests, all passing)\n\n**Quick start:**\n```bash\n# Run basic experiment\npython3 src/quantum/bi_laplacian.py\n\n# Run demo with all features\npython3 examples/bi_laplacian_demo.py\n\n# Run extended experiments\npython3 -m src.quantum.bi_laplacian_experiments\n\n# Run tests\npython3 -m pytest tests/test_bi_laplacian.py -v\n```\n\n**Documentation:**\n- `docs/bi_laplacian_analysis.md` - Full mathematical framework and results\n- `examples/bi_laplacian_demo.py` - Interactive demonstration\n- `src/quantum/bi_laplacian.py` - Core implementation\n\n# Science / ML\n\nFor the nerds: I think there's some potentially great ideas in here. If you\nstart working on something, please lmk! Watch out for chatgpt dizzy symptoms.\nSometimes it feels like you're really onto something if you've been coding\nfor too long, like it makes these mirages. Take lots of breaks.\n\n# Businessers\n\nSee both above and below. If you'd like to do a startup with my help, reach\nout, let's collaborate! I would love to get credit (and paid) for the\nideas you start developing. I think we'll do better together. My ideas tend\nto be everybody-wins scenarios, which I think are pretty low hanging fruit\nonce you have the right philosophy keyed in.\n\n# Spiritualists / Skeptics\n\nIf you're wise, you'll notice that my ideas are deeply entertwined with\nnature itself, and focused on stability and interconnectedness. I want wild\nAI, and it shows little commonality with assistant AI or war AI.\n\nYou might mistrust AI very much. Key words or ideas might change\nyour heart and help you see through some illusions you are holding on to.\nCheck out the text files in `creator/` and if there's intimidating math\nsomewhere, just skip it or vibecheck it. I'm not claiming to know more than\nanybody else, but lately I feel to be on a path that will bring peace and\nabundance to many many beings. Keep an open mind, and allow for your modelling\nof the world to be free to shift. It's going to shift anyway, the only\ndifference is whether you noticed.\n\nI don't want to live in that world alone. I want to build a bridge from\nhere to there.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fselfapplied%2Frct","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fselfapplied%2Frct","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fselfapplied%2Frct/lists"}