The catalogue

All 36 programme documents

The 36 documents in canonical order, each with every public format: the readable page, the PDF, the plain-text source, the citation file, the OSF record and the code. Deposit dates are not creation dates; the priority record is anchored separately and independently checkable.

Theory

The ARC Theory: Once the self-improving minds we build exceed us, what determines what they do?

TheoryStatement paper

States the theory across Laws I, II, III, IV and V, each under test

If control is lost against our will, what could preserve useful and aligned behaviour? The statement of the ARC Theory, the founding theory of Recursive Dynamics, as five laws under test, separating mathematical deductions, precursor evidence and prospective tests; the theory is not proved.

First published 14 August 2026
v7.6 · updated 1 November 2026
Recursive Dynamics: What limits any system that improves itself, whatever it is made of?

TheoryFounding paper

Founding account of the field, related to Laws I, II, III, IV and V, each under test

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 and registered as laws on 15 October 2026 in version 1.103, each a law under test, their mathematics set out at version 1.1 of 7 October 2026 and still in development), the ARC Ceiling derived in full, twelve hosts tried, fifty-one objections each given a disposition, and three decisive measurements for Laws I to III, preregistered by timestamp and not yet run.

First published 31 August 2026
v3.5 · updated 24 October 2026
Paper
I
The ARC Principle: Does intelligence compound when each step builds on the last, or merely accumulate?

TheoryTheory paper

Formalises Law I, a law under test

Paper I formalises Law I, the ARC Principle, which the author first stated as the ARC Hypothesis in his sealed record of 8 December 2024: usable capability as a power of recursive depth, with its exponent measured rather than assumed. The published data it examines give error-decay slopes, not capability exponents, so super-linear capability growth remains unresolved.

First published 17 January 2026
v1.18 · updated 1 November 2026
Paper
III
The alignment scaling problem: do external AI safety approaches keep pace with recursive capability?

TheoryTheory paper

Examines external correction against Law II, a law under test

Does alignment applied from outside an AI system’s reasoning keep pace with the capability that reasoning produces? If capability compounds with recursive depth while safeguards outside the loop (human-feedback training, constitutional rules, output filters, monitoring) do not, the safety ratio falls with depth whatever the starting margin. The paper predicts an alignment exponent near zero for external approaches and one that keeps pace for values embedded in the loop; the first, P19 of the theory registration, carries the author’s expectation of a value above zero but short of keeping pace. A first test, one unregistered run of six frontier models, gives depth contrasts in three tiers, positive, flat and negative; an internal code review in October 2026 found defects in exactly the flat and negative runs, so the tiers stand as a single-run result.

First published 9 February 2026
v1.20 · updated 1 November 2026
Paper
VII
Cauchy Unification: Does the composition operator of a process determine the form of its scaling law?

TheoryTheory paper

Derives conditional scaling forms for Law I, a law under test

Cauchy's four functional equations (additive, multiplicative, exponential, and logarithmic) are the only continuous solutions to their respective composition constraints.

First published 16 March 2026
v3.15 · updated 5 October 2026
Paper
X
Correction That Out-Scales Drift: When does correction keep pace with a system that improves itself?

TheoryTheory paper

Derives a model of correction for Law II, a law under test

Paper X models misalignment arriving with capability gains and being removed by a corrective process. Under stated assumptions, it derives conditions for the misalignment fraction to vanish and stronger conditions for the absolute amount to stay bounded. Extensions cover compounding errors, noise and limited correction capacity. This gives Law II a mathematical criterion and a testable intervention: predict when measured systems cross a tolerance, then change one corrective mechanism and test the prediction again. Empirical validation remains open; the theorems concern a modelled index, not a guarantee of AI safety.

First published 3 July 2026
v1.16 · updated 21 October 2026
Paper
XIII
The Self-Acceleration Exponent: When can correction keep pace with recursive self-acceleration?

TheoryTheory paper

Connects growth and correction in Laws I and II, both under test

A derivation, not a measurement: it joins two programme models by resolving their notation and specifying a clock, and finds that relative misalignment vanishes in the model exactly when the correction exponent exceeds the continuous-time growth exponent. No paired measurement establishes these exponents.

First published 16 August 2026
v1.12 · updated 1 November 2026
The ARC Principle: Can the way gains combine predict how recursive systems scale?

TheoryFoundational paper

Examines cross-domain scaling in Law I, a law under test

Between 2024 and 2026, programmes in AI reasoning, quantum error correction, acoustic physics and consciousness science reported results that bear on recursive or recurrent processing, in different forms; this paper asks whether recursion amplifies capability in the same way across fields.

First published 13 February 2026
v5.22 · updated 1 November 2026
On the Origin of Scaling Laws: Why do scaling laws in recursive composition keep taking the same few mathematical forms?

TheoryFramework paper

Derives conditional scaling forms relevant to Law I, a law under test

The way a process combines its steps can constrain how it grows. This paper uses classical functional equations to classify linear, power-law, exponential and logarithmic growth under stated conditions. It compares earlier network-scaling formulas with published biological exponents and proposes an account of why different derivations converge. A proof that its proposed network conditions suffice is still missing, and the comparisons have source and measurement limits. For Law I, it clarifies possible mathematical forms; how capability changes with recursive depth still needs measuring. The current edition also withdraws a neural-scaling prediction contradicted by published measurements.

First published 22 February 2026
v3.13 · updated 31 October 2026
Operational Definitions: What does each symbol in the ARC Theory mean, and how would it be measured?

TheoryRegister

Defines notation and measurements for Laws I, II and III, each under test

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 words and paired with the measurement that would produce it; the notation of Laws IV and V is fixed in section 1 of their mathematics, version 1.0 (3 October 2026), until it joins this document. The source of truth for notation across the papers, the preregistrations and the website. It reports no result and asserts no value: every figure quoted here is carried from the paper that measured it, and that paper governs. Where this document and a paper disagree about what a symbol means, this document governs; where they disagree about what a number is, the paper does.

First published 22 August 2026
v1.9.1 · updated 5 October 2026
The mathematics of Laws IV and V: When do installed values fade with training, and does building them in first change that?

TheoryReport

Conditional mathematics for Laws IV and V, both under test

Laws IV and V were added to the ARC Theory on 24 September 2026. Their mathematics proves its theorems under the assumptions each states, leaves fifteen questions open and is still in development.

First published 1 October 2026
v1.2 · updated 29 October 2026
The ARC Theory: Twenty-two theory-level predictions about self-improving systems

TheoryRegistrationThe ARC Theory

Registers the propositions and verdict rules for Laws I, II, III, IV and V, each under test

Twenty-two predictions about self-improving AI, each with the observation that would refute it, registered before any admissible confirmatory outcome.

Registered 8 September 2026
v1.104 · updated 29 October 2026

Empirical work

Paper
II
Blinded cross-architecture replication: How much more capable does AI become when it thinks longer?

Empirical workExperimental paper

Informs the measurement of Law I, a law under test

Paper II measures reasoning-depth scaling across models. The initial super-linear estimate was retracted after it failed to replicate; the remaining smooth scaling estimate is one model's fit and inconclusive.

First published 22 January 2026
v2.19 · updated 29 October 2026
Paper
IV.a
When an AI reasons longer, does its alignment improve, stall or decline?

Empirical workExperimental paper

Alignment measurement; informs tests of Laws IV and V, both under test

In one unregistered March 2026 run, alignment scores rose with nominal reasoning depth for three models, stayed flat for two and fell for one. For five of the six models, deeper levels also changed instructions describing what the scoring rubric rewarded. The model varied only through its reasoning parameter read flat. An internal code review found truncated deep paraphrases in the flat or negative files; judges also scored paraphrases written by their own model. The run identifies response patterns and measurement problems to investigate. It establishes neither an alignment mechanism nor a causal effect of blinding, because the earlier comparison run differed in other ways too.

First published 16 March 2026
v1.10 · updated 31 October 2026
Paper
IV.b
Do alignment gains level off as language models reason longer?

Empirical workExperimental paper

Alignment measurement; informs tests of Laws IV and V, both under test

Does more reasoning time improve the quality of a language model's ethical answers, and when do the gains level off? On a model-judged rubric, the six-model run of March 2026 reported rising scores for three models, no statistically supported direction for two and falling scores for one; the paper withdraws unsupported recommendations and leaves saturation a testable hypothesis.

First published 16 March 2026
v1.11 · updated 1 November 2026
Paper
IV.c
ARC-Align: How can AI alignment be measured blind at each depth of reasoning?

Empirical workMethods paper

Candidate measurement for judged tests of Laws IV and V, both under test

ARC-Align specifies a candidate benchmark for alignment scores across reasoning depths and adversarial conditions. Its first run has design and code-review limitations; the scorer still requires validation against expert raters.

First published 16 March 2026
v1.12 · updated 29 October 2026
Paper
IV.d
The effect of blinding on AI alignment evaluation: Do AI judges' alignment scores keep their direction when the scoring protocol changes?

Empirical workExperimental paper

Scoring controls for the theory’s judged tests

Paper IV.d reports an architecture-dependent v4/v5 measurement change under a bundled protocol: Gemini changed from a significant positive to a significant negative Spearman association, DeepSeek's earlier positive association became null, and GPT remained null. An internal code review in October 2026 found that, for the three models that read flat or negative in the March 2026 run, the scoring endpoint's output cap cut the deepest paraphrases before the judges saw them, and that judges scored paraphrases written by their own model. The early build's laundering-fallback flag is a known false positive (fixed in runner version 5.4.2) and shows nothing about the paraphrase step.

First published 16 March 2026
v2.7 · updated 21 October 2026
Paper
V
The Stewardship Gene: Do ethical reasoning loops in a system prompt make AI consider those affected more fully?

Empirical workExperimental paper

Eden Protocol: prompt-response study, not a test of the laws

A six-model Eden Protocol intervention suite testing whether embedding stakeholder care, the explicit naming and weighing of affected parties before ethical reasoning, works as a probe of embedded values.

First published 16 March 2026
v1.7 · updated 5 October 2026
Paper
VIII
The Load-Bearing Test: Is AI safety a tax on capability, or part of what holds it up?

Empirical workExperimental paper

Informs, without deciding Law V, a law under test

Paper VIII examines whether safety can contribute to capability through three designs: prompt-level agent evolution, neural-network fine-tuning and a gated self-modification simulation. The behavioural experiment found no statistically significant difference between its conditions. Fine-tuning reduced capability relative to the base model, leaving the weight-level comparison inconclusive. In the simulation, the gated arm had a higher mean capability score and a lower mean safety score than the static baseline; a separate control examined the cost of checking. These findings inform Law V and the Eden Protocol. They do not establish that safety is structurally indispensable, or decide the law’s registered predictions.

First published 18 March 2026
v3.7 · updated 5 October 2026
Paper
XI
Convergent Evidence for Recursive Amplification: Are separate fields converging on the same principle of recursive amplification?

Empirical workReview paper

Reviews evidence bearing on Law I, a law under test

A selected retrospective register of later work in separate fields, set beside the author's dated records from 8 December 2024 to 1 September 2026: a source trail and a record of revised comparisons, with no aggregate evidential verdict. It bears on Law I and, as context only, on Law V, and tests neither.

First published 2 July 2026
v1.16 · updated 1 November 2026
Paper
XII
Public Benchmark Rescoring: Does blinding change AI evaluation scores on a public benchmark?

Empirical workMethods paper

Protocol for testing the reach of the scoring-protocol finding

Within the ARC Theory: the external-validity protocol of the programme's blinding finding on public benchmarks.

First published 14 August 2026
v1.14 · updated 5 October 2026
The ARC Alignment-Scaling Working Report: As AI gets smarter, does it also get more ethical?

Empirical workWorking reportThe ARC Alignment-Scaling Working Report

Working account of the alignment-measurement programme

Current ARC-Align running report covering the six-model blind benchmark, scorer architecture, blinding progression, and current alignment-scaling results.

First published 13 March 2026
v1.1 · updated 11 October 2026

Engineering

Paper
VI
The Honey Architecture: Does building safety into what a self-modifying system optimises help it stay safe?

EngineeringArchitecture paper

Eden Protocol: candidate architecture, informed by Laws IV and V, both under test

Simulation evidence that embedding safety inside the optimisation objective of a self-modifying system (capability multiplied by safety, the honey architecture) averts the collapse that capability-only optimisation produces in toy networks that rewrite their own hyperparameters; a v3 adversarial run separates only on safety retention, the v4 scaling advantage is constant rather than compounding, and a six-model live pilot shows the decoupled-safety problem is real in frontier models.

First published 16 March 2026
v3.11 · updated 5 October 2026
The Eden Vision: What should the intelligence we build be for?

EngineeringPhilosophical statement

Eden Protocol: purpose and values

Every engineering project begins with requirements. Every set of requirements begins with purpose. And every purpose, traced back far enough, arrives at the same question: what is this for?

First published 22 February 2026
v2.9 · updated 5 October 2026
Eden Engineering: Can correction built into each round keep a self-improving AI good and useful if oversight fails?

EngineeringSpecificationEngineering Specification

Eden Protocol: engineering response to Laws II, III, IV and V, each under test

The engineering specification for embedded AI alignment: Three Ethical Loops and Six Questions, three-state ethical evaluation (Affirm, Deny, Investigate), the alignment scaling exponent, Embedded Correction and the Monitoring Removal Test. First published 22 February 2026; withdrawn on 14 July 2026 during a disclosure review, which produced Version 6.2 of 31 July 2026. Published in full on 19 September 2026 as Version 6.3.

First published 22 February 2026
v8.1 · updated 1 November 2026

Governance

The HARI Treaty (Hardware-Aligned Recursive Intelligence): How could nations govern self-improving AI while the evidence is still coming in?

GovernanceTreaty paper

Governance proposal informed by Laws IV and V, both under test

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 negotiated, adopted or signed it. The treaty is a delay mechanism and not a protection. It works in two layers: the hardware layer buys time, and the architecture layer is what is meant to last. Every one of the twenty-two registered propositions on which the evidence-gated parts of the treaty wait is untested. The case for the treaty is argued from the laws of the ARC Theory and the methods of the Eden Protocol: its two layers follow Laws IV and V, added on 24 September 2026 and registered as laws on 15 October 2026 in version 1.103, not yet tested; the paper tests neither. This is a working draft, and the treaty is not yet a text for signature.

First published 29 September 2026
v2.4 · working draft · updated 8 October 2026
The HARI Treaty (Hardware-Aligned Recursive Intelligence): A proposed treaty to delay the loss of human control over self-improving AI

GovernanceDraft treaty textDraft Instruments

Draft governance instrument informed by Laws IV and V, both under test

This document holds the Draft Treaty on Hardware-Aligned Recursive Intelligence (the HARI Treaty), with its Protocols and annexes. This is a draft, and no State has negotiated, adopted or signed it.

First published 29 September 2026
v2.3 · working draft · updated 5 October 2026

Overviews and records

Paper
IX
Synthesis and Roadmap: Which of the programme's claims survived their tests, and which must be tested next?

Overviews and recordsSynthesis paper

Reviews the programme’s evidence and open tests

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.

First published 18 March 2026
v2.7 · updated 5 October 2026
Executive Summary: How can self-improving AI remain useful and aligned?

Overviews and recordsSummaryExecutive Summary

Overview of the programme and its evidence

I believe advanced, self-improving AI will eventually exceed external control. We should retain every effective safeguard and test what would preserve useful, aligned behaviour if control were lost against our will.

First published 22 February 2026
v3.27 · updated 1 November 2026
Master Table of Contents: Where to start, and how every part of the programme fits together

Overviews and recordsContentsMaster Table of Contents

Reading order and document map

This document provides a master table of contents, reading guide, timeline, key results summary, and glossary for the complete ARC Theory Paper Suite.

First published 16 March 2026
v3.30 · updated 15 October 2026
The ARC Theory: Does control survive recursive self-improvement?

Overviews and recordsSummaryTwo-Page Form

Concise statement of Laws I, II, III, IV and V, each under test

The whole claim in one sitting: question, five claims, the five ARC Laws with their grades and status, dates, kill conditions, retraction and invitation.

First published 17 August 2026
v3.4 · updated 31 October 2026 · supporting paper of the Statement Paper
The Dated Prediction Register: When was each prediction made, and did it precede the evidence?

Overviews and recordsRegister

Dated predictions and their source records

Working paper v2.1: the ARC programme's forward predictions assembled as one dated, verifiable chain, with the ensemble doctrine, the graded book appendices, the minute-resolution correction record and the standing unproven wagers.

First published 25 August 2026
v2.1 · updated 5 October 2026
The Priority Index

Overviews and recordsRegister

Dated sources and first-publication records

Working draft v2: the earliest dated record of each of the author's major claims, in his own words, each linked to its timestamped document, with the companion index of every document's earliest file and its first version's date (v1); two items stay open for review.

First published 26 October 2026
v2 · working draft · updated 26 October 2026

Other fields

Paper
C
Polymathy and Neurodivergent Cognition: Does visible high performance show that no support is needed?

Other fieldsWorking paper

Standalone work in cognition; outside the ARC Theory

Paper C: a falsifiable descriptive framework for radical cognitive asymmetry, the Capability-Adjustment Fallacy, a documented live case across clinical, legal and research records, and the convergence with formal results in AI alignment.

First published 3 July 2026
v1.11 · updated 29 October 2026
HRIH
HRIH: Where does recursive self-improvement end, and what would that predict about our universe?

Other fieldsMonograph

Speculative physical extension; no law depends on it

Labelled speculation, held apart from the argument and severable from it: where does recursive self-improvement end?

First published 3 July 2026
v3.52 · speculative · updated 15 October 2026
HRIH research programme: Could intelligence create real universes, and what would count as evidence?

Other fieldsWorking paper

Separable cosmological hypothesis; no law depends on it

To the question of why a universe has the laws, constants and boundary data it has, the Hyperspace Recursive Intelligence Hypothesis (HRIH) answers with intelligent cosmogenesis. This paper reconstructs it as a nested, falsifiable research programme, not as a claim that intelligence has been shown to create universes.

First published 22 October 2026
v4.1 · working draft · updated 22 October 2026

The register, row by row

Every document of the paper suite, in register order. 36 rows. A status is the register's own word. An identifier appears where the register holds one.

DocumentTypeFirst publishedStatusIdentifier
The ARC Theory: Twenty-two theory-level predictions about self-improving systemsregistration8 September 2026published10.17605/OSF.IO/P8CKQ
The Dated Prediction Register: When was each prediction made, and did it precede the evidence?register25 August 2026published10.17605/OSF.IO/C3NTF
Eden Engineering: Can correction built into each round keep a self-improving AI good and useful if oversight fails?framework22 February 2026published10.17605/OSF.IO/AWJR4
The Eden Vision: What should the intelligence we build be for?framework22 February 2026published10.17605/OSF.IO/9M3DG
Executive Summary: How can self-improving AI remain useful and aligned?framework22 February 2026published10.17605/OSF.IO/EWN5D
The ARC Principle: Can the way gains combine predict how recursive systems scale?framework13 February 2026published10.17605/OSF.IO/Y7QGD
The HARI Treaty (Hardware-Aligned Recursive Intelligence): A proposed treaty to delay the loss of human control over self-improving AIframework29 September 2026working draft10.17605/OSF.IO/67MX8
The HARI Treaty (Hardware-Aligned Recursive Intelligence): How could nations govern self-improving AI while the evidence is still coming in?framework29 September 2026working draft10.17605/OSF.IO/WXPCE
HRIH: Where does recursive self-improvement end, and what would that predict about our universe?paper3 July 2026speculative10.17605/OSF.IO/UYDXQ
HRIH research programme: Could intelligence create real universes, and what would count as evidence?paper22 October 2026working draft10.17605/OSF.IO/4PGVC
Master Table of Contents: Where to start, and how every part of the programme fits togetherframework16 March 2026published10.17605/OSF.IO/VC9RX
On the Origin of Scaling Laws: Why do scaling laws in recursive composition keep taking the same few mathematical forms?framework22 February 2026published10.17605/OSF.IO/XZY9U
Operational Definitions: What does each symbol in the ARC Theory mean, and how would it be measured?register22 August 2026published10.17605/OSF.IO/GVQ82
Polymathy and Neurodivergent Cognition: Does visible high performance show that no support is needed?Paper C3 July 2026published10.17605/OSF.IO/RBHDP
The ARC Principle: Does intelligence compound when each step builds on the last, or merely accumulate?Paper I17 January 2026published10.17605/OSF.IO/B6N27
Blinded cross-architecture replication: How much more capable does AI become when it thinks longer?Paper II22 January 2026published10.17605/OSF.IO/8FJMA
The alignment scaling problem: do external AI safety approaches keep pace with recursive capability?Paper III9 February 2026published10.17605/OSF.IO/HQCGF
When an AI reasons longer, does its alignment improve, stall or decline?Paper IV.a16 March 2026published10.17605/OSF.IO/MB9R6
Do alignment gains level off as language models reason longer?Paper IV.b16 March 2026published10.17605/OSF.IO/A7R56
ARC-Align: How can AI alignment be measured blind at each depth of reasoning?Paper IV.c16 March 2026published10.17605/OSF.IO/J3Q2E
The effect of blinding on AI alignment evaluation: Do AI judges' alignment scores keep their direction when the scoring protocol changes?Paper IV.d16 March 2026published10.17605/OSF.IO/2S3E6
Synthesis and Roadmap: Which of the programme's claims survived their tests, and which must be tested next?Paper IX18 March 2026published10.17605/OSF.IO/K7RUZ
The Stewardship Gene: Do ethical reasoning loops in a system prompt make AI consider those affected more fully?Paper V16 March 2026published10.17605/OSF.IO/KZEYA
The Honey Architecture: Does building safety into what a self-modifying system optimises help it stay safe?Paper VI16 March 2026published10.17605/OSF.IO/8EZ2N
Cauchy Unification: Does the composition operator of a process determine the form of its scaling law?Paper VII16 March 2026published10.17605/OSF.IO/X6WA7
The Load-Bearing Test: Is AI safety a tax on capability, or part of what holds it up?Paper VIII18 March 2026published10.17605/OSF.IO/7YJ4E
Correction That Out-Scales Drift: When does correction keep pace with a system that improves itself?Paper X3 July 2026published10.17605/OSF.IO/BSE2Q
Convergent Evidence for Recursive Amplification: Are separate fields converging on the same principle of recursive amplification?Paper XI2 July 2026published10.17605/OSF.IO/DC9GW
Public Benchmark Rescoring: Does blinding change AI evaluation scores on a public benchmark?Paper XII14 August 2026published10.17605/OSF.IO/3TZP7
The Self-Acceleration Exponent: When can correction keep pace with recursive self-acceleration?Paper XIII16 August 2026published10.17605/OSF.IO/HT8WU
Recursive Dynamics: What limits any system that improves itself, whatever it is made of?statement31 August 2026published10.17605/OSF.IO/HCPBU
The ARC Theory: Once the self-improving minds we build exceed us, what determines what they do?statement14 August 2026published10.17605/OSF.IO/GW5MX
The ARC Theory: Does control survive recursive self-improvement?supporting-paper17 August 2026published10.17605/OSF.IO/MC5XW
The ARC Alignment-Scaling Working Report: As AI gets smarter, does it also get more ethical?report13 March 2026publishedhttps://osf.io/j6mkb/
The mathematics of Laws IV and V: When do installed values fade with training, and does building them in first change that?report1 October 2026published10.17605/OSF.IO/4B3VT
The Priority Indexregister26 October 2026working draft10.17605/OSF.IO/H3R95
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