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No single benchmark score can establish that an AI model has general intelligence. A defensible evaluation defines the claim first, samples distinct cognitive and social capabilities, tests adaptation on genuinely new tasks, controls the complete system being tested, compares with humans only under meaningful conditions, and reports uncertainty and limitations.
The result should be a qualified evidence report, not an “AGI score.” It should say exactly what the system demonstrated, on which tasks, with which tools and settings, and how far that evidence can reasonably be generalized.
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What “general intelligence” means in an evaluation
Evaluation begins with a construct definition. “General intelligence” can refer to several overlapping claims: broad cognitive skill, social competence, autonomous completion of real-world work, rapid learning from limited examples, or some combination of these. A test cannot answer all of those questions unless its scope is stated in advance.
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A different framing comes from François Chollet, whose definition is quoted by ARC Prize: “The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty.” This shifts attention from what a model has already memorized to how efficiently it acquires skills and applies them to problems its developers did not anticipate.
Write the claim in one sentence
Before selecting tests, write a sentence such as: “This evaluation asks whether the model can acquire and transfer cognitive skills across unfamiliar text, visual, quantitative and interactive tasks with limited human assistance.” If social interaction, physical control, long-horizon planning or autonomous tool use are outside scope, say so. A narrower claim can be tested rigorously; an undefined claim cannot.
Separate capability from deployment ambition
A model may perform strongly as a language reasoner while lacking perception, physical agency, durable memory or social understanding. Conversely, an integrated system may complete useful workflows because of retrieval, tools and human oversight even when its underlying model is weak in some domains. Decide whether the object of evaluation is the model weights, a model-plus-tools system, or an autonomous product. The report must use the same object throughout.
Build a capability map instead of one score
General intelligence is a portfolio of abilities. A credible study samples distinct domains and reports domain-level results rather than hiding uneven performance inside a single average.
| Capability area | What to test | Evidence to record |
|---|---|---|
| Reasoning | Deduction, induction, causal explanation, quantitative problem solving and multi-step planning | Accuracy, number of steps, error types and performance when irrelevant details are added |
| Learning and adaptation | Acquiring a rule or procedure from a small number of demonstrations, then applying it to new instances | Examples needed, improvement across trials, retention and transfer to a different task family |
| Language | Comprehension, generation, instruction following, ambiguity resolution and communication with different audiences | Factuality, constraint adherence, calibration and robustness to paraphrase |
| Perception and multimodal understanding | Reading diagrams, spatial relations, visual transformations, audio or other modalities included in the claim | Modality-specific accuracy and failures caused by the interface rather than reasoning |
| Creativity and open-ended construction | Producing useful, original solutions under explicit constraints | Independent quality ratings, constraint satisfaction and diversity of solutions |
| Metacognition | Recognizing uncertainty, detecting mistakes, asking for missing information and revising answers | Calibration, correction rate and whether confidence tracks actual success |
| Social cognition | Perspective taking, conversational cooperation, norm-sensitive decisions and conflict handling | Scenario-specific judgments, consistency and sensitivity to context |
| Autonomous task completion | Longer workflows involving planning, tools, state tracking and recovery from errors | Completion rate, intervention count, time, cost, unsafe actions and recovery behavior |
These categories are examples of an evaluation map, not a claim that every study must include every capability. The selected set should match the stated construct and the environments in which the system is expected to operate.
How do you test whether an AI is generally intelligent?
Use a staged protocol. The order matters because later measurements are difficult to interpret if the construct, task pool or system configuration was not fixed first.
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Define the construct and decision rule
State the capabilities, modalities, autonomy level and population of tasks covered. Decide what would count as evidence, what would count as a capability gap and which limitations would invalidate a broad claim. Do not call a model “generally intelligent” merely because it clears an arbitrary percentage on one test.
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Assemble diverse task families
Choose multiple families per capability where practical. Include familiar performance tasks and tasks requiring acquisition or transfer. Vary wording, surface format, context and difficulty so that success cannot come from recognizing one template. Keep the domains separate in analysis; a high language score should not compensate for an untested or failed capability.
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Create a protected novelty set
Reserve tasks and instances that were not used for prompt development, model selection or repeated tuning. Hold out entire task families when possible, not only random items from a known distribution. Record when the final test set was frozen and restrict access to people who need it. The goal is to estimate performance on related but unseen problems, not merely reproduce a familiar benchmark.
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Specify the evaluated system
Record the exact model name and version, system prompts, tool permissions, retrieval sources, memory, context limits, inference settings, reasoning budget, temperature or sampling policy, hardware constraints and any human assistance. If the claim concerns a complete agent, include orchestration code, tool APIs and safeguards. Two runs called “the same model” may represent materially different systems when these settings change.
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Predefine procedures and scoring
Write the instructions, number of attempts, time limits, allowed tools, stopping conditions and scoring rubric before looking at final results. Decide how partial credit, abstentions, malformed outputs and tool failures are handled. For open-ended tasks, use blinded raters or a validated rubric and report inter-rater agreement where applicable.
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Run baseline and ablation conditions
Test simple baselines, such as a tool-free model, a retrieval-only system or a fixed scripted procedure, when they clarify what the full system contributes. Remove or disable one component at a time where feasible. These comparisons help distinguish reasoning gains from extra information, external computation or human intervention.
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Measure learning and transfer
Give the system a controlled opportunity to learn a new rule or procedure, then test it on new instances and, ideally, a different task family. Track examples or interactions required, improvement over trials, retention after a delay and performance when superficial features change. A model that succeeds immediately on a familiar format but does not improve or transfer has supplied evidence of task performance, not broad skill acquisition.
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Compare with humans under matched conditions
Use human performance to contextualize difficulty, not as an automatic certification standard. Match information, tools, time, feedback, scoring and interface as closely as possible. Describe participant expertise and sample size. Explain which human abilities are not represented by the procedure; humans are embodied and may use different strategies and resources.
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Estimate uncertainty
Report item counts, repeated trials, sampling procedure, confidence intervals or other uncertainty estimates, and assumptions behind them. Separate variation caused by stochastic generation from variation caused by task sampling. If the test set is small or hand-curated, say so; a precise-looking percentage can still have weak evidential value.
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Analyze successes and failures by scope
Inspect representative errors, not only totals. A failure may indicate a reasoning gap, missing modality, interface problem, tool outage, distribution shift or benchmark mismatch. A success establishes demonstrated performance under the stated conditions; it does not automatically establish the same ability in an untested environment.
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Benchmark accuracy is not generalized accuracy
NIST distinguishes performance on the exact benchmark items from performance generalized to a wider population of related items. These are different statistical targets.
| Target | Question answered | Main risk |
|---|---|---|
| Fixed-benchmark accuracy | How often did the system answer the published or frozen items correctly? | Scores can be inflated by memorization, leakage, repeated tuning or familiarity with the item format. |
| Generalized accuracy | How well is the system expected to perform on comparable, unseen items drawn from the intended task population? | Estimates depend on sampling assumptions and can be uncertain when the task population is poorly defined. |
NIST’s statistical work explains why these quantities need separate estimates. A benchmark result should therefore identify the item set, its construction, the intended population of related tasks and the method used to quantify uncertainty. Saying “the model scored 80 percent” without that context leaves the reader unable to tell what was actually measured.
How can you tell whether an AI is reasoning or memorizing?
No single diagnostic proves that a model reasoned internally. The practical goal is to reduce explanations based on memorized answers and test whether behavior changes appropriately when the underlying problem changes.
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- Use controlled novelty. Change entities, numbers, layouts and wording while preserving the rule. Include tasks created after the model’s likely training period when provenance can be established.
- Test counterfactuals. Alter one premise at a time and check whether the conclusion changes in the logically required direction. Include impossible or inconsistent premises to see whether the system notices them.
- Vary surface form and modality. Present the same abstract relation in different text, visual or structured formats. Robust transfer is stronger evidence than repeated success on near-duplicates.
- Probe intermediate competence. Ask the system to identify relevant information, state assumptions, predict the next step and check its own answer. Treat explanations as additional behavior to evaluate, not as proof that the hidden computation followed the explanation.
- Measure adaptation efficiency. Provide a small, standardized teaching set for an unfamiliar rule, then score new examples. Record how much feedback was required and whether the learned rule survives irrelevant changes.
- Check sensitivity to leakage. Search for overlap between evaluation material and publicly available training or development data where feasible. Keep final items private and limit repeated access.
- Compare with simpler alternatives. If a lookup, calculator, retrieval index or scripted policy explains the result, the evidence should be attributed to the complete system rather than assumed to come from general reasoning.
These tests support a stronger or weaker inference; they do not expose a definitive internal chain of thought. The report should describe observable behavior and the competing explanations that remain plausible.
What ARC-AGI can and cannot establish
ARC-AGI is useful evidence about a particular slice of generalization: adapting to abstract problems with limited prior knowledge and applying inferred rules to new instances. ARC-AGI-2 is described by its authors as an expanded, more granular evaluation of abstract reasoning and problem solving.
That focus makes ARC-AGI relevant when the claim includes novel abstract problem solving. It does not test the full range of language, social, physical, economic and autonomous capabilities implied by a broad AGI definition. An ARC score is therefore not an AGI verdict or certification.
ARC Prize reports human baselines and seeks comparable evaluation procedures. It also emphasizes keeping evaluation datasets private and secure so that learning and adaptation can be tested. Those controls improve interpretability, but no single benchmark becomes exhaustive merely because its test set is protected. The OECD similarly warns that benchmark performance can fail to transfer beyond benchmark conditions and distributions.
How to compare evaluation frameworks
When choosing among benchmarks or evaluation campaigns, score the design along the following axes rather than ranking projects by a headline number.
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| Axis | Questions to ask | What a strong design provides |
|---|---|---|
| Breadth | How many distinct capability domains and task families are represented? | Coverage aligned with the claim, with domain-level results instead of one opaque average. |
| Novelty and leakage resistance | Were tasks unseen, held out and protected from repeated tuning? | Private or access-controlled tests, provenance checks and held-out task families. |
| Construct validity | Do the tasks actually measure the stated capability? | A documented rationale, pilot analysis and evidence that task success is not explained by a shortcut. |
| Human comparability | Do humans and AI receive comparable information, tools, time and scoring? | Matched procedures, participant description and explicit limits on interpretation. |
| System and resource accounting | Are model version, scaffolding, tools, compute, data and human labor disclosed? | A reproducible system specification and separate reporting for model and external components. |
| Statistical validity | Are sampling, repeated attempts and uncertainty reported? | Clear distinction between fixed-set and generalized performance, with uncertainty estimates. |
| Reproducibility and realism | Can another evaluator repeat the test, and does it resemble meaningful use? | Stable procedures, accessible materials where security permits, and realistic but controlled tasks. |
OECD describes evaluation campaigns as balancing realism with reproducibility, objective assessment and repeatable measurement. A highly realistic test that cannot be repeated may be useful for a deployment decision but weak for scientific comparison. A perfectly repeatable puzzle suite may be easy to optimize and distant from real work. State which trade-off the evaluation makes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Human comparisons require special care
A human baseline can show whether a task is trivial, difficult or near the edge of ordinary performance. It does not automatically turn the task into a valid test of AI intelligence. The OECD states: “Human tests cannot be directly used as measurement instruments for an ability-oriented evaluation of AI.”
Human tests are designed for particular populations, instructions and incentives. People are embodied, can use world knowledge and may employ strategies unavailable to a model. Models may process text at a different speed, call tools, retry outputs or receive hidden scaffolding. A fair comparison therefore specifies:
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- available reference material, calculators, software and external assistance;
- time limits, breaks, feedback and number of attempts;
- interface and modality, including whether images, audio or physical actions are involved;
- scoring rules for partial credit, uncertainty and abstention; and
- which conclusions the comparison supports and which it cannot support.
If conditions cannot be matched, present human results as context rather than a direct ranking.
How to report an evaluation so others can interpret it
A publication-ready report should let a reader reconstruct what was tested and what the result means. Include the following fields.
Claim and scope: capabilities, modalities, autonomy level, intended task population
System: model/version, prompts, tools, retrieval, memory, orchestration, human help
Data: task families, provenance, development/test split, leakage controls
Procedure: instructions, attempts, time limits, randomness, stopping rules
Scoring: metrics, partial credit, raters, handling of invalid or abstained answers
Results: per-domain scores, transfer results, baseline comparisons, ablations
Uncertainty: item sampling, trial variation, intervals or other estimates
Failures: representative errors, capability gaps, interface and tool limitations
Generalization claim: what wider population the results are intended to describe
Reproducibility: materials available, security restrictions and exact run dateReport the tested model and configuration, not a brand-level label. If several systems solve different task subsets, do not imply that a combined score represents one coherent agent unless the same integrated system actually produced it.
Interpreting outcomes without overclaiming
Broad, consistent success
Strong performance across independent domains, protected novelty sets and transfer tasks supports a more substantial claim than success on a single benchmark. Even then, the conclusion should remain bounded by the tested modalities, resources, time horizon and task population.
High scores with weak transfer
This pattern is consistent with benchmark-specific competence, memorization, prompt familiarity or narrow optimization. It does not prove the absence of intelligence, but it weakens any claim that the result represents general skill.
Strong reasoning, weak interaction or autonomy
A system may solve abstract problems yet fail to maintain state, ask for clarification, use tools reliably or handle social context. Describe the strengths and missing components separately instead of averaging them into a single verdict.
Inconsistent results across runs
Large variation can arise from stochastic generation, ambiguous instructions, tool instability or a small test set. Increase repeated trials, stabilize settings, improve task wording and report the spread. A best-of-many result should never be presented as ordinary performance.
Failure on one benchmark
A failure may reveal a real capability gap, but it can also reflect an inaccessible modality, poor interface, unfamiliar notation or a mismatch between the benchmark and the intended construct. Examine the error mechanism before drawing a conclusion about general intelligence as a whole.
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- Using one leaderboard as an AGI test. Fix: combine independent domains and report each one.
- Choosing the test after seeing model outputs. Fix: freeze the evaluation protocol and protected set before final scoring.
- Counting tool output as model reasoning. Fix: disclose tools and run ablations that isolate their contribution.
- Mixing model versions or prompts. Fix: version every component and rerun comparisons under matched settings.
- Ignoring repeated tuning. Fix: track development attempts and reserve a final holdout that the team cannot repeatedly inspect.
- Reporting only an average. Fix: provide per-domain results, sample sizes, uncertainty and representative failures.
- Treating explanations as proof of internal reasoning. Fix: test predictions, counterfactuals, transfer and behavior under controlled changes.
- Using human scores without matching conditions. Fix: align information, tools, timing and incentives, or label the comparison as contextual only.
- Assuming benchmark success transfers automatically. Fix: estimate generalized accuracy on related unseen tasks and state the intended task population.
- Turning an evaluation into a certification claim. Fix: use scoped language such as “demonstrated performance on” and list untested capabilities.
A practical decision checklist
Before calling an evaluation evidence of general intelligence, verify that the answer to each question is documented.
- Is “general intelligence” defined for this study rather than assumed?
- Does the task portfolio cover more than one capability and more than one task family?
- Are there genuinely novel, protected tasks or instances?
- Is transfer measured after controlled learning, not just immediate accuracy?
- Are model version, prompts, tools, retrieval, compute and human assistance recorded?
- Are fixed-benchmark and generalized accuracy treated as separate quantities?
- Are human comparisons matched and carefully qualified?
- Are uncertainty, sampling assumptions and repeated-trial variation reported?
- Are successes and failures analyzed by domain and failure mechanism?
- Does the conclusion stay within the tested scope?
If several answers are no, the study may still be useful as a benchmark report or capability snapshot. It is not sufficient evidence for a broad claim that the model demonstrates general intelligence.
What the evidence can support
The strongest defensible conclusion is usually layered: the system demonstrated specified capabilities under specified conditions; it generalized to a defined set of novel tasks with a stated level of uncertainty; it matched or differed from human performance under a particular procedure; and important domains or deployment conditions remain untested.
That discipline makes evaluations more informative than a headline score. It also allows progress to be tracked without pretending that a complex, contested construct has acquired a universally accepted numerical threshold.
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