Multimodal search with holistic trace evaluation for ARC-AGI-2

In the field of artificial intelligence, language models have achieved an impressive ability to generate coherent reasoning chains, but the true frontier lies not only in generating answers, but in knowing when the correct answer is in the minority against an apparently logical majority. This challenge becomes evident in abstract visual reasoning tasks such as ARC-AGI-2, where the selection among multiple hypotheses —and not their mere generation— becomes the core of the problem. An innovative approach, based on multimodal search with holistic trace evaluation, has managed to outperform the most advanced models on the market by more than 18 percentage points, combining text, image, and code channels as independent search operators, and subjecting all traces to a joint judgment within the same broad context. This methodology demonstrates that the diversity of approaches, when evaluated globally rather than through simple voting, is capable of rescuing correct hypotheses that would otherwise be lost. For a company seeking to integrate AI solutions for businesses, this principle is key: it is not enough to implement a model, but rather to build systems that explore multiple paths and judge them with full context. Q2BSTUDIO, as a software development and technology company, applies this philosophy in its custom software projects, where the combination of different modalities —from computer vision to natural language processing— makes it possible to create robust AI agents capable of operating in complex environments. The ability to holistically evaluate reasoning traces, similar to how an expert judge compares all alternatives before deciding, translates directly into the construction of custom applications that not only execute instructions, but understand context and make informed decisions. This approach also aligns with the cybersecurity and monitoring practices that Q2BSTUDIO offers: just as a threat detection system analyzes multiple attack vectors simultaneously, a multimodal reasoner must consider all possible paths before issuing a judgment. The integration of AWS and Azure cloud services makes it possible to scale these search and evaluation processes, deploying multiple agents in parallel without losing the coherence of the holistic judgment. On the other hand, business intelligence and power bi services benefit from this same logic: instead of averaging contradictory indicators, a dashboard that evaluates multiple business hypotheses jointly can reveal patterns that majority voting would hide. The original article documents that attempts at iterative refinement or prescriptive templates systematically reduce the diversity of hypotheses and worsen performance, underscoring the importance of allowing AI agents to explore without excessive restrictions. In practice, Q2BSTUDIO implements these principles in its automation projects, where the holistic evaluation of process traces —from logs to user events— makes it possible to detect anomalies that an isolated analysis would overlook. The company thus offers a comprehensive approach that combines the power of artificial intelligence with the robustness of software engineering, ensuring that each solution, whether a recommendation system or a power bi dashboard, is built on a foundation of diverse and contextual reasoning. For those seeking to go beyond standard models and need software that reasons like an expert, the lesson from ARC-AGI-2 is clear: the key is to search multimodally and judge holistically, exactly as Q2BSTUDIO does in each of its developments.