The Abstraction and Reasoning Corpus (ARC) was designed to measure generalization and skill-acquisition efficiency—not raw pattern-matching ability. Its tasks require structured reasoning, object-centric perception, and compositional transformations. This is exactly what makes ARC valuable as a benchmark for advanced models.

But it also introduces a practical problem for researchers:

ARC provides almost no meaningful signal when a model is still “dumb.”

In the early phases of exploring new architectures or inductive biases, most prototypes are weak. They fail almost all tasks. And because ARC tasks require a relatively high minimum level of intelligence to get anything right, these early models tend to score identically: near-zero.

This is what I call ARC’s low resolution on dumb models.

1. Most ARC tasks require high minimum intelligence

ARC is by design adversarial to brute force, shallow heuristics, or pattern-matching networks. For most tasks:

• You need object-level segmentation
• You need to infer a transformation rule
• You need to apply it consistently to a new input
• You must generalize, not memorize

If a model lacks any of these ingredients, it fails the entire task. And because there’s no partial credit, models below the threshold aren’t differentiated.

Result: multiple models with very different internal abilities all collapse to the same output score—zero.

This flattens the early part of the performance curve.

2. Small improvements produce no measurable change

When developing a new idea—whether an architecture, representation, objective function, or training strategy—the first versions are almost always weak. Researchers iterate quickly to see whether a concept has promise.

On ARC, however:

• Small architectural improvements don’t register on the leaderboard.
• Slightly better representations still score zero.
• Early innovations show no measurable difference unless the model crosses a fairly high competence threshold.

This kills iteration speed.

A creative idea might genuinely help, but ARC won’t show it until after substantial engineering, optimization, and system-level refining.

The benchmark becomes binary for early-stage models:

Either it magically solves a task, or it doesn’t—and mostly, it doesn’t.

3. This makes early exploration expensive

Because small, incremental improvements do not translate into detectable score changes:

• Researchers must invest significant engineering effort before learning whether an idea is promising.
• “Cheap” experiments stop being cheap—almost all require full-stack implementation.
• The exploratory phase becomes risky and slow.

This discourages trying new directions and biases research toward incremental improvements on known working systems rather than bold ideas with unproven potential.

4. Community response: easier or more controlled variants of ARC

The community has recognized this problem for the original ARC-1 and created intermediate datasets:

• ConceptARC – tasks grouped by interpretable concepts
• L-ARC – tasks stratified by difficulty
• Subsets of ARC – easier/cleaner subsets
• re-ARC and synthetic generators – tailor-made tasks with more variety and controllable complexity

These datasets help provide gradient signal during model development and allow researchers to validate inductive biases before throwing models at full ARC.

5. ARC-2 needs similar scaffolding

ARC-2 inherits the same evaluation philosophy as ARC-1, but as of now:

• There are few or no difficulty-scaled variants
• No standardized synthetic pretraining datasets
• No curated “easy tier” that allows weak models to show incremental gains
• No developmental ladder to help models bootstrap reasoning skills

As ARC-2 becomes the new target for generalization research, the lack of intermediate-resolution datasets will become a bottleneck.

We need ARC-2 equivalents of ConceptARC, re-ARC-style generators, difficulty-graded subsets, and developmental curricula.

Conclusion

ARC is an excellent benchmark for measuring high-level reasoning in mature systems.

But for early-stage models, ARC provides almost zero feedback. Its resolution at the low end is too coarse to distinguish between different “dumb” architectures.

To accelerate research—especially for ARC-2—we need supporting datasets that:

• expose easier tasks
• provide continuous difficulty scaling
• offer dense signal during learning
• help model designers test ideas without full engineering commitment

Without these, the path from novel idea → working ARC solver will remain unnecessarily long, expensive, and discouraging.