\textsc{DiARC}: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models
arXiv:2606.26530v1 Announce Type: cross Abstract: The Abstraction and Reasoning Corpus (ARC;~\citealp{chollet2019measure}) contains tasks that require summarizing patterns from limited grid samples and predicting output grids. Recently, many large language model based approaches have attempted to transform it into a text-based reasoning task. However, methods based on open-source models have generally yielded unsatisfactory results, while those relying on closed-source models are too costly. Current efforts mainly focus on data augmentation, constructing ARC-like data for more comprehensive su
The paper published at arXiv highlights current limitations in open-source LLMs' reasoning abilities for complex tasks like ARC, indicating a push for more effective, less costly solutions.
Improving LLM reasoning and pattern summarization from limited samples is crucial for advancing AI's capability beyond basic tasks, particularly for autonomous systems and agentic AI.
This research suggests a more efficient way to enhance logical reasoning in LLMs, potentially democratizing advanced AI capabilities currently costly or proprietary.
- · Open-source AI developers
- · Companies using open-source LLMs
- · AI agents developers
- · Proprietary LLM providers if open-source catches up
- · Researchers relying solely on closed-source model superiority
Improved performance of open-source LLMs on complex reasoning tasks.
Accelerated development and deployment of more sophisticated AI agents and autonomous systems.
Enhanced competition in the LLM market, potentially driving down costs or improving the capabilities of proprietary models.
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Read at arXiv cs.AI