ARC-AGI-2 Toasted: Generalist Agent Crushes Chollet's 'Hardest' Benchmark With Code Generation

Agentica Agent Achieves New SOTA on ARC-AGI-2 Benchmark

The landscape of artificial intelligence benchmarking just experienced a seismic shift, propelled by a recent breakthrough from the Agentica ecosystem. On February 12, 2026, at approximately 4:28 AM UTC, @gregkamradt shared explosive news: an Agentica agent had not merely competed but had decisively achieved a new State-of-the-Art (SOTA) score on the notoriously difficult ARC-AGI-2 benchmark. This benchmark, famously championed by François Chollet, has long stood as a crucible for testing abstract reasoning, frequently deemed one of the "hardest" challenges facing modern generalist AI systems due to its reliance on novel, human-like pattern recognition rather than rote memorization or massive data regurgitation.

The implication of this achievement is profound. If the results hold, it signals a significant leap forward in an agent’s ability to tackle complex, visual reasoning tasks that resist simple statistical solutions.

Performance Details and Methodology

The Record-Breaking Score

The Agentica agent registered an exceptional SOTA score of 85.28% on the ARC-AGI-2 evaluation set. This figure immediately places the system at the forefront of current public benchmarks for this specific task.

Mechanism of Success: Code Generation

The core methodology behind this high performance centers on a powerful, yet surprisingly streamlined, approach: the agent utilizes code generation and execution as its primary reasoning tool. Instead of brute-forcing visual puzzles through pre-trained weights alone, the Agentica system appears capable of dynamically writing and running code snippets tailored to solve the novel pattern presented in each ARC task. This suggests a high level of meta-cognition—the ability to devise a program to solve a problem, rather than just learning the solution itself.

Remarkable Efficiency

Perhaps the most astonishing detail shared by @gregkamradt is the sheer economy of the solution. The entire implementation for this high-performing agent, capable of achieving SOTA on Chollet's benchmark, required approximately 350 lines of code. This metric underscores the efficiency and generality of the underlying Agentica SDK architecture, contrasting sharply with the potentially millions or billions of parameters that might be expected to underpin such reasoning capabilities in other models.

Generalization Versus Specialization

The conversation around AI breakthroughs often descends into a debate: is the success due to genuine intelligence, or merely intensive, targeted tuning for a specific test? The Agentica performance appears to lean heavily toward the former.

Evidence of Generalist Strength

The system is explicitly highlighted as non-specialized for ARC. This is critical. The success on ARC-AGI-2 is being presented not as the result of an agent exhaustively fine-tuned on variations of Chollet’s tasks, but as a fortunate—or perhaps inherent—spillover effect of its generalist design. Furthermore, the source material notes that this Agentica agent demonstrates corresponding strength across other, unspecified benchmarks.

This evidence supports the hypothesis that the system is extracting fundamental, transferable reasoning capabilities from its training, rather than overfitting to the visual idiosyncrasies of the ARC dataset. The performance is a function of generality, not specificity.

Community Reception and Future Benchmarking

The announcement, immediately shared across social platforms, generated a flurry of activity, including a measured but pointed reaction from respected figures in the field.

Verification and Skepticism

The immediate response, even from allies of rapid AI development, contained an element of necessary skepticism. The original source indicated a desire for full verification of the methodology, recognizing that the utility of any benchmark hinges on rigorous adherence to testing protocols. As one prominent voice noted, the focus should shift:

“And fwiw it’s less important that it’s ‘hard’ and more important that it’s reflective of intelligence.”

This perspective frames the benchmark not as a barrier to overcome, but as a mirror reflecting true cognitive capacity.

The Next Frontier

With ARC-AGI-2 potentially "toasted," the community immediately began pivoting toward the next challenge. If an agent can master this level of abstract, visual programming and reasoning, the question becomes: what other cognitive hurdles remain that truly test the limits of current artificial general intelligence efforts? The call to action is clear: what benchmark should be thrown at this system next to probe its deeper capabilities?

Further Reading and Source Access

For those seeking to dive deep into the technical underpinnings, the detailed results, methodology, and implementation specifics are available in an accompanying analysis. This comprehensive look promises to illuminate how an Agentica agent managed to craft a solution demanding sophisticated reasoning with only a few hundred lines of guiding code.

Source:

• The announcement and commentary originated on X (formerly Twitter) via @gregkamradt: https://x.com/gregkamradt/status/2021803662658736430
• Detailed results and methodology can be found at: symbolica.ai/blog/arcgentica.