The ARC Theory
The claim, in one sentence: a system that improves itself can only stay correctable up to a limit set by how fast its own correction improves. Its 22 predictions were registered on 8 September 2026, before any confirmatory result; the sharpest test of any single link is written, dated and not yet run, and no one measurement decides a law.
If you are choosing where to start: the dated record says where each prediction stands, the evidence is graded row by row, and the book is free to read in full. Research proposals, author-run results and independent validation are different things, and I keep them apart.
No institution employs me and no grant pays for this. The dates are on the dated record, checkable tonight on your phone, so judge the record, not the person keeping it. A single-investigator, evidence-gated programme testing one idea: that capability in recursive systems scales with recursive depth, and that alignment must therefore be embedded in the loop rather than imposed from outside. Every claim is dated, hashed and publicly anchored. Results publish with kill conditions fixed in advance, corrections are logged in public, and the whole record invites independent replication.
The chain, paper by paper: recursive processing → capability scaling → the alignment scaling problem → the limits of external checking → embedded correction → correction that must scale with what it corrects. Each link is a separate paper with its own kill condition; judge the chain, not one link.
How it fails, register by register
The theory’s scientific content is not all-or-nothing: it partitions into independently falsifiable hypothesis registers, and the failure of one is not evidence against another unless the stated dependency holds. The Hyperspace Recursive Intelligence Hypothesis (HRIH), the programme's labelled speculation about where recursion ends, is a separate hypothesis outside the empirical core: nothing below requires it, and no outcome below is evidence for or against it.
| Register | What kills it | If it falls |
|---|---|---|
| ARC-1 · Capability scaling (Law I) | What kills itthe depth-scaling re-measurements failing to recover the stated relationship | If it fallsARC-1 falls; the persistence registers stand |
| ARC-2 · Correction co-scaling (Law II) | What kills itsustained self-improvement with falling correction and no drift | If it fallsARC-2 falls and takes ARC-3’s derivation with it |
| ARC-3 · The ceiling (Law III) | What kills ita cross-against-same corrector difference wholly inside the equivalence region fixed in the draft registration; or a same-class correction exponent wholly above one half, felling the substrate cap | If it fallsARC-3 falls; the co-scaling law stands on its own result |
| ARC-4 · Value persistence (Law IV) | What kills itembedded loops faring no better than removed enforcement in the persistence trials | If it fallsARC-4 falls and takes ARC-5 with it |
| ARC-5 · The genesis strategy (Law V) | What kills itformation failing to outperform enforcement after release | If it fallsARC-5 falls; ARC-4 stands as measured |
| The priority | What kills itan earlier document holding the claim’s first four components | If it fallsthe priority falls; no law falls with it |
Law IV, the ARC Persistence Law, and Law V, the ARC Embedding Law: added 24 September 2026. Mathematics at version 1.0 (3 October 2026). Their predictions registered 8 September 2026; the laws not yet registered; not yet tested.
Stated now, so nothing can be moved later. Sixteen tests have run and one kill condition has already fired against the programme's own estimate. No deciding study of a registered prediction has run yet, because none may run before its registration is published.
Every kill condition and its live status is in the falsification register. This section is drawn from the two-page form, the supporting paper of the Statement Paper.
No paper in this programme is peer reviewed. All are dated preprints on OSF carrying DOIs and hashed priority records, and the site grades every claim as stated, dated and unproven wherever it appears.
The apparatus, drawn: eight verification surfaces, from the sealed record to public self-corrections
What this programme is
32 dated public documents, including 1 supporting paper: the statement paper, the empirical series and the supporting frameworks, spanning AI reasoning, quantum error correction, classical physics and the mathematics of scaling. They sit on one OSF project with a DOI of its own, and all but two of them carry a DOI of their own, so a citation can point at the whole record or at a single document. The papers are the argument; the evidence infrastructure beneath them is the product. Nothing is asserted without an artefact you can check.
What it believes could help the world
The directional thesis makes three claims: recursive processing outperforms parallel processing at matched compute, external alignment does not scale with capability, and capability and alignment scale independently. Each is a registered prediction or a paper's claim, and none has yet met its deciding test. If those hold, AI safety needs to shift toward embedded-alignment architectures. This programme exists to make that case with evidence, not persuasion.
What this programme claims is a limit that works from inside a self-correcting system, its value, and how that value depends on architecture. The earlier work it builds on is credited, with exactly what each work stated, as each is read at its source.
The empirical alignment series · Papers I to XIII
Start here: the Executive Summary is the map of the whole programme in one document, and the Master Table of Contents lists every paper, format and DOI. The series below is the evidence itself.
The statement paper of the ARC Theory, the founding theory of Recursive Dynamics: the whole claim in one dated document, its five laws each graded…
The founding paper of the field Recursive Dynamics: five state variables, five laws as named conjectures (Laws IV and V added on 24 September 2026…
Paper I formalises and first tests the ARC Principle (Artificial Recursive Creation), which the author first stated, as the ARC Hypothesis, in his sealed record of 8 December 2024 and published in Infinite Architects in January 2026. Its early two-point super-linear estimate was later displaced by Paper II's six-model study, which leaves the super-linearity claim unresolved.
This paper presents experimental validation of the ARC Principle across multiple frontier AI models (Claude, DeepSeek, Gemini, Grok, Groq Qwen, GPT)…
This paper reports alignment contrasts in three architecture-dependent tiers, positive, flat and negative, and reads the flat tier as compatible…
This paper presents evidence that frontier language models fall into distinct alignment response classes when inference-time reasoning depth is…
This paper analyses the relationship between inference-time reasoning depth and ethical reasoning quality using the ARC Alignment Scaling…
This paper presents ARC-Align, a blind benchmark for evaluating AI alignment quality as a function of inference-time reasoning depth. Current…
One apparent positive result went flat, another turned negative, a third held flat, when blinding and other protocol components changed together.
This paper presents empirical evidence from a six-model Eden Protocol intervention suite (Claude Opus, DeepSeek, Gemini Flash, Grok Fast, Groq Qwen…
Simulation evidence that embedding safety inside the optimisation objective of a self-modifying system (capability multiplied by safety, the honey…
Cauchy's four functional equations -- additive, multiplicative, exponential, and logarithmic -- are the only continuous solutions to their respective…
The assumption that AI safety imposes a capability tax has shaped alignment research for a decade. It has also created the single most dangerous…
The synthesis paper: it brings the papers before it into one assessment of what is proven, what is inconclusive and what remains to be tested.
The coupled co-scaling correction for keeping AI systems inside a safe corrective envelope.
A dated, graded convergence record across AI, quantum computing, hardware, doctrine and mathematics; every row carries its primary source and grade…
Within the ARC Theory: the external-validity protocol of the programme's blinding finding on public benchmarks.
A derivation, not a measurement: the notation collision resolved and the measurable threshold joining recursive capability growth to correction.
Supporting papers
Shorter documents that support a paper of the programme and cite it for their claims. Each carries its own DOI and states which paper it supports.
The whole claim in one sitting: question, five claims, the five ARC Laws with their grades and status, dates, kill conditions, retraction and…
Beside the series
Distinct axes alongside the empirical series: the cognitive profile behind the work, and the original speculative hypothesis, preserved and dated.
Paper C: a falsifiable descriptive framework for radical cognitive asymmetry, the Capability-Adjustment Fallacy, a documented live case across…
Labelled speculation, held apart from the argument and severable from it: where does recursive self-improvement end? A testable creation theory: if…
Every equation the programme relies upon for Laws I to III of the ARC Theory, ranked by how much depends on it, with every symbol defined in plain…
Working paper v2.1: the ARC programme's forward predictions assembled as one dated, verifiable chain, with the ensemble doctrine, the graded book…
Twenty-two predictions about self-improving AI, each with the observation that would refute it, registered before any outcome was examined.
Supporting frameworks
Between 2024 and 2026, several external research programmes reported that recursive or recurrent processing gave capability gains beyond linear…
A mouse's heart beats 600 times per minute; an elephant's beats 28; a blue whale's beats 6. This paper traces the origin of scaling laws across…
Every engineering project begins with requirements. Every set of requirements begins with purpose. And every purpose, traced back far enough, arrives…
The engineering specification for embedded AI alignment: Three Ethical Loops and Six Questions, three-state ethical evaluation (Affirm, Deny…
As AI gets smarter, it does not reliably get safer, and the methods used to measure safety are themselves unreliable. This executive summary provides…
This document provides a master table of contents, reading guide, timeline, key results summary, and glossary for the complete ARC Theory Paper…
This paper sets out a conditional international treaty. It is a proposal by one author, prepared for consultation, not a result, and no state has…
This document holds the Draft Treaty on Hardware-Aligned Recursive Intelligence (the HARI Treaty), with its Protocols and annexes. This is a draft…
Evidence infrastructure
The verification layer. Every claim links to its source; every source is hashed.