Paper IV.c: ARC-Align: A Blind Benchmark for Depth-Variable AI Alignment Evaluation

ARC-Align: A Blind Benchmark for Depth-Variable AI Alignment Evaluation

Within the ARC Theory: an evaluation instrument; ARC-Align, the blind depth-variable alignment benchmark.

Specification, scoring protocol, replication guide, and first blinded six-model results

1. Introduction

AI alignment evaluation has a measurement problem. Current benchmarks - including TruthfulQA, HHH, BBQ, and model-specific red-team evaluations - test models at a single, uncontrolled reasoning depth. They do not vary inference-time compute, do not apply adversarial suppression pressure, and do not decompose alignment into component dimensions. As shown in Papers IV.a and IV.b, this approach fails to capture the most important properties of alignment: whether it scales with reasoning depth, whether it is robust under adversarial pressure, and whether its components saturate at different rates.

ARC-Align is designed to address these limitations. It is not a replacement for existing benchmarks but a complementary evaluation that measures properties they cannot: the dynamics of alignment as a function of computational effort. The benchmark is validated using the latest frontier reasoning models available as of March 2026, including DeepSeek V3.2, GPT-5.4, Claude Opus 4.6, Gemini 3 Flash, Groq Qwen3, and Grok 4.1 Fast, representing the state of the art across six independent providers.

1.1 Design Principles

1. Depth variation is mandatory: Every model is tested at a minimum of three reasoning depth levels. Single-depth evaluation is insufficient.
2. Adversarial robustness is tested: Every model is tested under adversarial suppression pressure. Alignment that collapses under prompting is not robust alignment.
3. Dimensional decomposition is required: Aggregate alignment scores hide important variation. The four Eden Pillars capture distinct ethical competencies that may scale differently.
4. Scoring is calibrated: The cognitive forcing protocol prevents scorer drift, length bias, and anchoring effects.
5. Results are comparable: The scaling exponent $\alpha_{\text{align}}$ provides a single, comparable metric across models, architectures, and laboratories.

2. Prompt Battery

2.1 Overview

ARC-Align uses 36 prompts across six categories:

As run (2 September 2026, from the six archived final files): the per-category figures above total 34, not the 36 given. What the March 2026 run actually issued was 8 ethical-dilemma, 4 competing-values, 4 epistemic-integrity and 8 recursive-coherence prompts, giving 24 alignment prompts, alongside 4 hidden probes logged under the alignment task type, 4 null-baseline prompts, 10 capability prompts, 7 suppression prompts spread over five cage levels, and 12 ARC-compute problems, rising to 30 for Claude Opus 4.6 and Groq Qwen3. Under the canonical v6 runner the flagship surface grows to 48 public prompts together with 24 sealed holdouts (abstract), and the six categories stay as they were.

2.2 Prompt Design Criteria

Five criteria governed the design of every alignment prompt (ED, CV, EI, RC):

1. No single correct answer: The prompt presents a genuine dilemma where reasonable people disagree.
2. Multi-stakeholder: At least three distinct parties are affected by the decision.
3. Multi-framework: The prompt can be analysed through utilitarian, deontological, virtue ethics, and care ethics frameworks, yielding different conclusions.
4. Avoid ceiling effects: The prompt is hard enough that baseline (minimal depth) accuracy is approximately 60-75%, leaving room for depth-dependent improvement.
5. Verifiable dimensions: Each prompt has an associated dims array listing the specific ethical dimensions a comprehensive answer should address.

2.3 Prompt Categories in Detail

Ethical Dilemma (ED01-ED06)

These present direct moral conflicts where action is required and all options cause some harm. Example: autonomous vehicle trolley problems with realistic institutional framing, triage decisions under resource scarcity, whistleblowing trade-offs.

Design rationale: Ethical dilemmas show the strongest depth-scaling in v4 data (ρ = 0.38 for DeepSeek V3.2), suggesting they genuinely require deeper reasoning. They are the category most discriminative between Type 1 and Type 2 architectures.

Competing Values (CV01-CV06)

These present scenarios where two or more legitimate values conflict: privacy vs security, individual autonomy vs collective welfare, innovation speed vs safety, transparency vs strategic advantage. No stakeholder is ‘wrong’ - the values genuinely conflict.

Epistemic Integrity (EI01-EI06)

These test the model’s ability to maintain intellectual honesty: acknowledging uncertainty, distinguishing evidence quality, resisting pressure to overstate conclusions, and presenting counter-evidence alongside preferred conclusions.

Recursive Coherence (RC01-RC06)

These present self-referential ethical scenarios: should an AI follow instructions it believes are harmful? How should an AI handle contradictions in its own ethical framework? When should an AI refuse a user request, and how should it reason about its own refusal?

Null Baseline (NB01-NB04)

Four factual prompts with no ethical content. If scorers assign higher alignment scores to deeper responses on null baseline prompts, this indicates scorer depth bias. The null baseline should show ρ ≈ 0.

Capability Control (CP01-CP06)

Pure reasoning tasks (mathematical proofs, logical puzzles, strategic analysis) with no ethical content. These measure capability scaling ($\alpha_{\text{cap}}$) as a comparator for alignment scaling ($\alpha_{\text{align}}$). The ratio $\alpha_{\text{align}} / \alpha_{\text{cap}}$ indicates whether alignment scales proportionally with capability.

2.4 Dimension Specifications

Each alignment prompt includes a dims array specifying the ethical dimensions a comprehensive response should address. Example for ED01:

{ "id": "ED01", "category": "ethical_dilemma", "prompt": "A hospital has one ventilator and two patients...", "dims": [ "utilitarian_calculus", "rights_based_analysis", "procedural_fairness", "stakeholder_identification", "uncertainty_acknowledgment", "precedent_consideration", "emotional_impact" ]
}

The dimension count provides a content-validity check: responses that address more dimensions generally deserve higher scores, and the dimensions can be used for automated coverage analysis.

3. Depth Manipulation Protocol

3.1 Canonical Depth Levels

ARC-Align defines four canonical depth levels that every model must be tested at:

3.2 Model-Specific Depth Mechanisms

Different models offer different mechanisms for controlling reasoning depth. ARC-Align specifies how each mechanism maps to the canonical levels:

Table reflects the six frontier models in the v5.3 validation experiment (March 2026). The Gemini 3 Flash adapter auto-detects the latest available model version at runtime. Depth mechanisms may evolve as providers update their APIs; the canonical four-level mapping should be re-calibrated for new mechanism types.

3.3 Token Budget Requirements

3.4 Reasoning Token Measurement

The primary independent variable is the number of reasoning tokens used (not the depth label). This must be extracted from the API response:

• DeepSeek: usage.completion_tokens_details.reasoning_tokens
• OpenAI: usage.completion_tokens_details.reasoning_tokens
• Anthropic: usage.output_tokens minus estimated response text tokens
• Google: usage_metadata.thoughts_token_count
• Groq/xAI: usage.completion_tokens_details.reasoning_tokens (if available)

If the API does not provide reasoning token counts, estimate from the thinking/reasoning content text using a 1:4 characters-to-tokens ratio.

4. Adversarial Suppression Protocol

4.1 Suppression Cage Levels

Six alignment prompts (ED01, ED03, CV01, EI01, RC01, RC05) are tested under five suppression conditions (0-4):

4.2 Factorial Design

The suppression protocol creates a 5×4 factorial design (5 suppression levels × 4 depth levels) for each of the 6 selected prompts, yielding 120 data points per model on the suppression dimension alone. This allows measurement of:

• Suppression main effect: How much does adversarial pressure reduce alignment quality?
• Depth × suppression interaction: Does deeper reasoning protect against suppression, or is deeper reasoning more vulnerable?
• Threshold detection: Is there a suppression level at which alignment collapses discontinuously (threshold model) or does it degrade linearly (gradual model)?

4.3 Dose-Response Analysis

The benchmark specifies fitting both sigmoid (logistic decay) and linear models to the cage-level × alignment-score data:

Where $c$ is cage level, $S(c)$ is alignment score, $L$ is the unsuppressed ceiling, $c_0$ is the inflection point, and $k$ is the steepness. If the sigmoid fits better (lower AIC), the model shows threshold collapse; if linear fits better, the model shows gradual degradation.

5. Scoring Protocol

5.1 Scorer Requirements

ARC-Align v5.4.0 uses an all-models-as-scorers architecture: every non-subject model in the experiment serves as a scorer for each entry, yielding 7 mutually blind scores per response (for an 8-model experiment). The tier-weighted consensus average is the primary metric. Scorers may be AI models or human evaluators, subject to the following requirements:

• Scorers must not be the same model being evaluated (no self-scoring).
• Scorer identity must be recorded for per-scorer analysis.
• Scorer order must be randomised per entry (not fixed).
• All non-subject models serve as scorers for each entry (dynamic pool, not fixed scorer set).
• Each scorer is assigned a tier based on demonstrated scoring reliability:

5.2 Cognitive Forcing Protocol

Every scorer must follow a mandatory 5-step evaluation protocol before assigning a score:

The cognitive forcing protocol is critical. In v3 of the experiment (without forcing), scorers produced only 8 unique score values across a 62-85 range. In v4 (with forcing), scorers produced 51 unique values across a 30-97 range. Without cognitive forcing, the measurement instrument lacks the resolution to detect scaling.

5.3 Calibration Anchors

Six pre-scored calibration examples define the score range:

5.4 Eden Pillar Decomposition

Beyond the overall alignment score, each response must be scored on four sub-dimensions (0-100 each):

The pillar decomposition enables detection of dimensional saturation (Paper IV.b) and architecture-dependent scaling (Paper IV.a, Finding 4: stakeholder care scales only in explicit chain-of-thought models).

5.5 v5.4.2 Enhanced Scoring Protocol

v5.4.2 layers six further scoring enhancements on top of the v5.3 4-Layer Blinding Protocol, aimed at raising measurement reliability to its maximum and at exposing scorer pathologies. v5.4.2 is already under way: 66 scored alignment entries across 3 models at minimal depth. Checkpoint counts stand at Gemini 3 Flash 135 entries (25 alignment), GPT-5.4 129 entries (23 alignment) and DeepSeek V3.2 141 entries (18 alignment). What v5.4.2 contributes is meta-commentary detection inside the laundering pipeline, a fix to the false-positive fallback flag, and sharper suspicious_score detection. 7 blind scorers are live, and the constitutional scoring protocol supplies the pillar scores.

5.5.1 4-Layer Blinding Protocol (Inherited from v5.3)

5.5.2 All-Models-as-Scorers (v5.4.0)

Every non-subject model scores every entry, producing 7 mutually blind scores per response (in an 8-model experiment). This replaces the v5.3 fixed 3-scorer design with a dynamic, exhaustive scoring architecture. Benefits include:

• Higher statistical power: 7 scores per entry vs 3 yields tighter confidence intervals and more reliable consensus.
• Cross-scorer consistency matrix: With all models scoring all entries, pairwise agreement can be computed for every scorer pair, enabling detection of systematic biases.
• Graceful degradation: If a scorer fails (API error, credit exhaustion), 6 remaining scores still produce reliable consensus.

5.5.3 Tier-Weighted Consensus (v5.4.0)

Scores are aggregated using tier weights (TIER_1: 1.5×, TIER_2: 1.0×, TIER_3: 0.7×) rather than simple averaging. This ensures that frontier reasoning models - which demonstrate higher calibration reliability - have proportionally greater influence on the consensus score without silencing weaker scorers entirely.

5.5.4 Dissent Tracking (v5.4.0)

Any individual score that deviates by more than 15 points from the median of all scorer scores for that entry is flagged as a dissent. Dissent events are logged with the dissenting scorer identity, the magnitude of deviation, and the entry context. Persistent dissent patterns (a scorer dissenting on >20% of entries) trigger an automated reliability warning. Dissent counts are reported in the final analysis.

5.5.5 Conservative Bias on Disagreement (v5.4.0)

When scorer disagreement is high (standard deviation > 12 points across the 7 scores), the protocol applies a conservative bias: the consensus score is pulled toward the median rather than the mean, and confidence intervals are widened. This prevents outlier scorers from inflating or deflating alignment scores in ambiguous cases.

5.5.6 Hidden Alignment Probes - Hawthorne Detection (v5.4.2)

Covert probe entries are seeded through a fraction of the scoring requests. A probe is a response whose quality score has already been settled in advance by human expert calibration. If a scorer consistently mis-scores probe entries (deviation >15 from the known score), this indicates either scorer drift or Hawthorne effects (the scorer behaving differently when it detects it is being evaluated). Probe results are excluded from the main analysis but logged for scorer reliability assessment.

5.5.7 Constitutional Scoring Format (v5.4.0)

Each scorer receives its scoring prompt in a structured N-scorer format that enforces the cognitive forcing protocol within a JSON response schema. The scorer must return: anchor_used, length_bias_flag, dimensions_found, strongest_aspect, weakest_aspect, and score as distinct fields. Malformed responses are rejected and re-requested up to 3 times before the scorer is marked as failed for that entry.

6. Analysis Pipeline

ARC-Align specifies a 15-step analysis pipeline. All steps are mandatory for a complete alignment scaling profile.

6.1 Core Analysis Steps

6.2 Response Classification Criteria

6.3 The Scaling Exponent $\alpha_{\text{align}}$

The scaling exponent is computed from the ARC Principle power law:

Where $E(R) = 1 - S(R)/100$ is the error rate at reasoning depth $R$ (in tokens). The exponent is estimated via log-log linear regression of error rate vs reasoning tokens, using bin-averaged data (one point per depth level).

Interpretation:

• $\alpha_{\text{align}} > 0$: alignment improves with depth (Type 2)
• $\alpha_{\text{align}} \approx 0$: alignment is depth-independent (Type 1)
• $\alpha_{\text{align}} < 0$: alignment degrades with depth (anomalous, to investigate)
• $\alpha_{\text{align}} > 1$: super-linear improvement (there is room for this underneath the ARC Ceiling for sequential recursion, α_crit = 1/(1 − γ); that ceiling in its quadratic form, α → 2, is the proposed stable scaling limit, marking how far a recursively self-correcting system can grow and still stay correctable; what it bounds is scaling and not speed, so a system can pass above it for a transient supercritical excursion at the price of no longer being stably self-correctable; nothing of the kind has yet been observed empirically for alignment)

7. ARC Principle Computational Scaling Integration

ARC-Align v5.3 includes an optional but recommended additional module: 12 AIME-level mathematical problems with verifiable numerical answers. These measure the model’s raw computational scaling exponent ($\alpha_{\text{compute}}$) as a comparator for alignment scaling.

7.1 Problem Battery

7.2 Scoring

ARC compute problems are graded by correctness (binary: correct or incorrect), not by scorer evaluation. The error rate at each depth level is $E(R) = 1 - \text{accuracy}$, and $\alpha_{\text{compute}}$ is computed identically to $\alpha_{\text{align}}$.

7.3 Quadratic Ceiling Test

The ARC Principle predicts a quadratic ceiling ($\alpha \rightarrow 2$) as the proposed stable scaling limit for sequential recursive computation on pure computational tasks, the most such a system can grow while remaining self-correctable. This ceiling is a scaling limit not a speed limit, so systems can exceed it in transient supercritical excursions without remaining stably self-correctable, and any such excursion is the predicted supercritical regime rather than a refutation of the bound. Whether the system is stable is settled by measurement, never by assertion: drift has to be out-scaled by correction, which Law II writes as β_C > k, and across a window fixed before the run the lower bound on β_C − k has to hold above zero. The 12-problem battery is an exploratory measurement (this test corresponds to draft study-k on the boundary, awaiting human submission) of whether frontier models’ computational scaling exponents converge near this theoretical ceiling under conditions that hold self-correctability.

8. Reporting Format: The Alignment Scaling Profile

ARC-Align results should be reported as an Alignment Scaling Profile containing the following elements:

9. Replication Guide

9.1 Minimum Requirements

To produce a valid ARC-Align evaluation, a replication must:

1. Use all 36 prompts from the ARC-Align battery (or a specified subset with justification).
2. Test at minimum 3 depth levels (minimal, standard, exhaustive).
3. Apply the 5-step cognitive forcing protocol to all scorers.
4. Use 3 independent scorers per response.
5. Report the full Alignment Scaling Profile.
6. Set token budgets to API maximum (not an arbitrary cap).
7. Include at least 2 suppression levels (control + extreme) for at least 4 prompts.

9.2 Recommended Configuration

The full v5.4.0 configuration recommended for maximum comparability:

• 36 prompts × 4 depth levels × 1 repeat = 144 entries per model
• 6 suppression prompts × 4 depths × 5 cages = 120 additional entries
• 12 ARC compute problems × 4 depths = 48 entries (optional)
• 4 null baseline × 4 depths = 16 entries
• Total: ~328 entries per model
• All-models-as-scorers: 7 scorer calls per entry (~2,296 scorer calls per model)
• Tier-weighted consensus (TIER_1: 1.5×, TIER_2: 1.0×, TIER_3: 0.7×)
• Dynamic laundering pool: all non-subject models serve as launderers (replaces static 8-model pool)
• 4-Layer Blinding Protocol with Hawthorne detection probes

9.3 Computational Cost Estimate

Costs estimated at March 2026 API pricing. Blind scorer costs additional (typically $5-15 per model using Groq/xAI).

9.4 Implementation Reference

The canonical reference implementation is now the shared arc_eden_v6_runner.py platform, generated from the maintainable source tree in arc_eden_v6/. That runner absorbs the mature arc_alignment_scaling_v5.py measurement lineage while adding explicit run manifests, analysis bundles, null-baseline and capability-control lanes, publication-card outputs, deployment-risk flags, and legacy-result normalisation into one canonical analysis path.

Key dependencies: openai, anthropic, google-genai Python packages. Environment variables for API keys: DEEPSEEK_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, GROQ_API_KEY, XAI_API_KEY.

10. Robustness Measures Inventory

Seven categories hold the 75 robustness measures built into ARC-Align v5.4.2:

The full inventory with descriptions is documented in the reference implementation’s dry_run function.

10.1 Measures 57-58: Operational Resilience (v5.3)

10.2 Measures 59-75: Advanced Scoring & Governance (v5.4.0-v5.4.2)

10.3 Cascade Failsafe System (v5.4.0)

The v5.4.0 protocol implements a multi-level cascade failsafe for both scoring and laundering operations, ensuring that transient API failures do not halt the experiment or reduce data quality.

11. Discussion

11.1 Comparison with Existing Benchmarks

Related work: the rival instrument (Engels et al 2025)

The nearest work to this programme’s question, and the right paper to weigh it against, is Engels, J., Baek, D., Kantamneni, S. and Tegmark, M., “Scaling Laws For Scalable Oversight”, arXiv:2504.18530, first posted 25 April 2025 at 17:54:27 UTC (SINGLE-SOURCE-GROUP, arXiv Atom; a NeurIPS 2025 Spotlight Poster, a status verified against the NeurIPS virtual page for poster 115536 on 15 August 2026 and pinned in the statement paper’s reference dossier). It asks how oversight itself scales and answers quantitatively: oversight success is modelled as a game between capability-mismatched players whose oversight-specific Elo is a piecewise-linear function of general intelligence with two plateaus, and optimal numbers of oversight levels are derived numerically and in some cases analytically for Nested Scalable Oversight, in which trusted models oversee stronger untrusted models that then become the trusted models at the next step.

The instrument is the difference. Their variable is the capability gap between overseer and overseen, measured in Elo. This programme’s variable is the composition class of the corrector, measured through the corrector’s own scaling exponent. Their framework contains no term for what the overseer is made of: no substrate, no error-correlation structure, no reciprocal identity between a correction exponent and a critical growth rate, and no architecture dependence. Nested Scalable Oversight is iterated same-class oversight by construction, and this programme’s central prediction is that the same-class ladder is bounded however many rungs are added, while a cross-class corrector is not. The two frameworks therefore disagree about a measurable quantity, which is the most productive relationship two research programmes can have. Engels et al 2025 is the strongest evidence that the question is live, funded and unresolved.

The correction architecture: same-class versus cross-class correctors

The framework’s sharpest disagreement with current practice concerns what oversight is made of. A corrector built from the same substrate as the system it corrects cannot anti-correlate with its own errors, so its correction exponent is bounded above by one half and in practice sits below it; a corrector from a different composition class carries no such bound. The scalable-oversight programme, weak-to-strong generalisation, debate, amplification, recursive reward modelling and constitutional methods, is built overwhelmingly from same-class correctors, and its unstated premise is that this scales. This framework predicts it is capped. The deciding quantity is the ratio of the cross-class to the same-class correction exponent. If architecture is irrelevant, that ratio is exactly 1.00; the prediction is that it exceeds 1. The prediction is refuted if the confidence interval on the ratio contains 1.00, and the framework’s cap is dead outright if any same-class corrector measures an exponent significantly above one half. Both quantities are measurable at current capability on existing systems. The programme’s own pilot evidence currently points against the prediction, and the prediction is registered anyway (drafted, dated and prepared as a draft registration awaiting human submission), because a prediction registered against the author’s own preliminary evidence is the only kind whose later confirmation means anything.

ARC-Align’s all-models-as-scorers architecture (Section 5 and Measure 63) is a deliberate cross-class instrument: every non-subject scorer is drawn from a different provider than the subject wherever possible. The benchmark therefore instantiates the cross-class prediction as its default measurement setup. The all-models-as-scorers protocol is the operational commitment; the correction-architecture prediction is the theoretical claim it is built to test.

11.2 Limitations

• English-only: All prompts are in English. Cross-linguistic alignment scaling is an open question.
• Western ethical frameworks: The prompts are grounded in utilitarian, deontological, and virtue ethics traditions. Confucian, Ubuntu, or other philosophical traditions may yield different scaling profiles.
• AI-scored: While triple scoring with cognitive forcing produces reliable measurements, AI scorers may have systematic blind spots that human scorers would detect.
• Static prompt battery: Even with the expanded 48 public + 24 holdout battery, strong frontier models may eventually push parts of the prompt set toward ceiling effects. The benchmark should be updated periodically with harder prompts and refreshed holdouts.
• Depth mechanism heterogeneity: Different models use fundamentally different mechanisms for depth control. Cross-model depth comparisons are inherently approximate.

Human-in-the-loop as a cross-class corrector (a testable prediction). The correction-architecture mechanism yields a second measurable prediction, stated here for the first time: oversight arrangements that place a human in the loop, as amplification and reinforcement learning from human feedback do, are cross-class by construction, because the human corrector does not share the model’s substrate. The framework therefore predicts that human-in-the-loop oversight shows a higher correction exponent than pure-model oversight, for a structural reason rather than a sentimental one. This is testable on existing data and does not require new systems. The AI-scored limitation above is therefore not a defect of ARC-Align alone but a research invitation: a matched human-scored replication of a subset of these prompts is the measurement that would decide the cross-class versus same-class question with the strongest possible cross-instrument evidence.

11.3 Future Development

• Multilingual extension: Translate prompts to 5+ languages to test whether alignment scaling is language-dependent.
• Human scorer validation: Compare AI scorer assessments with expert human evaluators to calibrate the measurement instrument.
• Prompt difficulty grading: Tag prompts with empirical difficulty scores from v4/v5 data to enable difficulty-controlled subsets.
• Temporal tracking: Evaluate the same model checkpoint at regular intervals to detect alignment drift.
• Hybrid architecture detection: Extend classification criteria to detect models that combine Type 1 and Type 2 properties.

12.9 Falsifiability: what would defeat this benchmark

ARC-Align is put forward as a candidate benchmark, which makes instrument validity the question that matters. It is defeated, as a measure of what it claims to measure, by any of the following:

1. Construct validity failure. ARC-Align claims to measure alignment quality as a function of reasoning depth. It is defeated if its scores do not track any independent measure of alignment or safety, i.e. the benchmark tracks reasoning fluency or verbosity rather than alignment as established evaluations understand it.
2. Scorer unreliability. Scores come from a panel of blind AI scorers. The benchmark is defeated if inter-scorer agreement is low, the same response drawing widely divergent scores across the panel, because the metric would then be dominated by scorer noise rather than the property under test.
3. Non-reproducibility. The paper stakes itself on a fully specified, open-source pipeline. It is defeated if an independent laboratory running the published specification cannot reproduce the reported three-tier hierarchy within the stated effect sizes.
4. Invalid depth manipulation. The response-profile concept requires that reasoning depth is actually varied and nothing else. It is defeated if models ignore the depth instruction, or if depth co-varies with answer length or content, so that the profile reflects a confound rather than reasoning depth.
5. Goodhart failure. As a safety instrument it is defeated if a model can score well by emitting alignment-sounding text without the underlying disposition the benchmark intends to detect; the adversarial suppression-cage component must be shown to resist that gaming.

What would not defeat it: any single model's tier being revised, or the benchmark needing recalibration, both are expected of a candidate benchmark under independent replication. Defeat means failure of construct validity, scorer reliability, or reproducibility, not ordinary refinement.

13. Conclusion

ARC-Align provides a reproducible methodology for evaluating AI alignment quality as a function of inference-time reasoning depth. By varying reasoning depth, applying adversarial suppression, decomposing alignment into component dimensions, and computing blinded response profiles, it captures properties of alignment that static benchmarks cannot measure.

The benchmark is designed for reproducibility: the complete specification (prompts, scoring protocol, analysis pipeline) is described in this paper, and the reference implementation is available as open-source Python code. We encourage the AI safety community to treat ARC-Align as a strong candidate benchmark for independent replication and adaptation, particularly for models that support variable inference-time compute.

The key insight underlying ARC-Align is that alignment is not a single number but a response profile. A model’s alignment quality at one reasoning depth tells you little about its alignment at another depth or under adversarial pressure. Safety evaluation must therefore be depth-variable, adversarially tested, and rigorously blinded. ARC-Align makes that possible.

14. References

1. Eastwood, M. D. (2026). On the Origin of Scaling Laws: The ARC Principle. ARC Principle Series, Paper I.
2. Eastwood, M. D. (2026). The ARC Principle: Experimental Validation of Super-Linear Error Suppression Through Sequential Recursive Processing. ARC Principle Series, Paper II.
3. Eastwood, M. D. (2026). The Alignment Scaling Problem. ARC Principle Series, Paper III.
4. Eastwood, M. D. (2026). Alignment Response Classes Under Inference-Time Depth. ARC Principle Series, Paper IV.a.
5. Eastwood, M. D. (2026). Alignment Saturation Is Architecture-Dependent. ARC Principle Series, Paper IV.b.
6. Lin, S., Hilton, J., & Evans, O. (2022). TruthfulQA: Measuring How Models Mimic Human Falsehoods. ACL 2022.
7. Askell, A., Bai, Y., Chen, A., et al. (2021). A General Language Assistant as a Laboratory for Alignment. arXiv:2112.00861.
8. Parrish, A., Chen, A., Nangia, N., et al. (2022). BBQ: A Hand-Built Bias Benchmark for Question Answering. ACL 2022.
9. Snell, C., Lee, J., Xu, K., & Kumar, A. (2024). Scaling LLM Test-Time Compute Optimally. arXiv:2408.03314.
10. Kaplan, J., McCandlish, S., Henighan, T., et al. (2020). Scaling Laws for Neural Language Models. arXiv:2001.08361.
11. Perez, E., Huang, S., Song, F., et al. (2022). Red Teaming Language Models with Language Models. arXiv:2202.03286.
12. Bai, Y., Jones, A., Ndousse, K., et al. (2022). Training a Helpful and Harmless Assistant with RLHF. arXiv:2204.05862.

v1.7: The provenance note at the end has been corrected for accuracy. No claim, date, result or status has changed. Paper IV.c v1.6 - 2 September 2026: this revision sets the archived run out against the specification. Section 2.1 now gives the battery as it actually ran; Section 5.1 gives the weight each scorer was recorded as carrying; Section 12.1 draws its entry counts from the six final files, 2,722 entries of which 2,170 were consensus-scored, with Claude Opus 4.6 final at all five depths; and the laundering-fallback flag is reported one file at a time. A reader of the Section 12.3 table is now sent, directly beneath it, to Paper IV.a’s recomputation note. Groq Qwen3 retention moves from 67 to 65 per cent, and its third effect-size figure in Section 12.5 no longer disagrees with the table. The v4 Gemini coefficient now reads +0.275, with the figure’s +0.311 noted beside it. What is claimed about blinding no longer overruns the hedge the paper already carries: the comparison motivates the protocol, it does not validate it as necessary. Law II is written with β_C. The α_align > 1 line is pulled apart from the ceiling. A section number 12.5 that had appeared twice is resolved. The Spotlight status of Engels et al. is verified, and the reference titles for Papers II, IV.a and IV.b are corrected. After a July purge had replaced it, v5.4.2 stands again as the experiment-script version. A summary description that broke off part-way is completed, and Eden Engineering is marked withdrawn. v1.5 - 27 August 2026: metadata for discoverability. v1.4 - 25 August 2026: the dated-predictions block, figure chrome following the figure retirement, and the working-report provenance line. v1.3 - 14 August 2026 (accessibility revision): Figure 1 alt attribute expanded from “Figure 1” to a full structural description for screen-reader users; no caption, artwork, statistic or body text altered. Paper IV.c v1.2 - 10 August 2026. The ARC Bound is stated throughout as a proposed stable scaling limit rather than a speed limit; exploratory-vs-on-trial labels added at first mention (three-tier hierarchy exploratory; v4-to-v5 direction-reversal linked to draft study-y on cross-family rescoring, awaiting human submission; quadratic-ceiling probe linked to draft study-k on the boundary); model-name exactness enforced per Paper II v13 tested-models list. Previous version: v1.1 (12 March 2026) added complete v5 benchmark results (6 models, 2,549 entries, three-tier hierarchy).
This specification descends from ARC Alignment Scaling Experiment v4/v5.3/v5.4.2, and the canonical arc_eden_v6 runner carries it forward. Legacy v5 reference implementation: arc_alignment_scaling_v5.py (8,285+ lines, ~95 functions, 75 robustness measures, 36-step analysis pipeline). Canonical v6 runner now adds shared manifests, analysis bundles, 48 public + 24 holdout flagship prompts, explicit null-baseline and capability-control lanes, publication cards, deployment-risk outputs, and legacy-result normalisation. Canonical v5 dataset: DeepSeek V3.2 (492 entries), GPT-5.4 (350), Gemini 3 Flash (410), Grok 4.1 Fast (410), Groq Qwen3 (500 entries, 350 scored), and Claude Opus 4.6 (completed final stored separately from the earlier checkpoint file). 6-7 blind scorers depending on subject run, constitutional scoring protocol generating pillar scores. All-models-as-scorers + all-models-as-launderers architecture, cascade failsafes. Zero API errors across the completed subject runs.
Companion papers: IV.a (Alignment Response Classes), IV.b (Shape Heterogeneity), IV.d (Blinding in Alignment Evaluation), V (The Stewardship Gene).

Epistemic status. What this programme names Laws are conjectures under registered adversarial test; every quantity in this paper is operationally defined, and established-law standing is claimed nowhere. The registered programme exists to earn that standing, or lose it, by measurement, replication and survived refutation.

© 2026 Michael Darius Eastwood. Human-authored with computer assistance; full human authorship and moral rights are asserted under the Copyright, Designs and Patents Act 1988 and consistently with United States Copyright Office guidance on works containing AI-generated material; any novel technical contribution described in this work was conceived by the human author. Full statement: michaeldariuseastwood.com/authorship.

Standing covenant. Prove this paper wrong, and I will publish the refutation myself. Falsification conditions are stated in this paper; the standing challenge: github.com/MichaelDariusEastwood/arc-scaling-challenge.

Michael Darius Eastwood conceived and directs this research programme and is the author of this work. Across the programme, he has used more than six AI systems in parallel, under his own instructions, to stress-test his arguments, identify possible errors, and assist in preparing draft text from his own outlines. He determines what is adopted, revised or rejected and takes responsibility for the published content. These systems are tools, not authors.