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New model learns visual-symbolic rules for ARC benchmark

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Loop-OWM, an object-centric world-modeling architecture designed to learn rules for the Abstraction and Reasoning Corpus (ARC). This new model learns visual-symbolic rules as transitions between structured states, incorporating color-prototype slots and a looped transition model. Loop-OWM demonstrated superior performance on both ARC-1 and ARC-2 benchmarks compared to existing methods with similar or fewer parameters. AI

IMPACT Introduces a novel approach to learning visual-symbolic rules, potentially improving AI's ability to understand and generalize from visual patterns.

RANK_REASON The cluster contains an academic paper detailing a new model architecture for a specific AI benchmark.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Gege Gao, Bernhard Sch\"olkopf, Andreas Geiger ·

    Slots, Transitions, Loops: Learning Composable World Models for ARC

    arXiv:2606.12316v1 Announce Type: new Abstract: ARC tests in-context rule induction: given a few input-output demonstrations, a model must infer the hidden rule and apply it to a new query. While many approaches express ARC rules through language, code, or symbolic programs, ARC …

  2. arXiv cs.CV TIER_1 English(EN) · Andreas Geiger ·

    Slots, Transitions, Loops: Learning Composable World Models for ARC

    ARC tests in-context rule induction: given a few input-output demonstrations, a model must infer the hidden rule and apply it to a new query. While many approaches express ARC rules through language, code, or symbolic programs, ARC itself is visual-symbolic: rules appear as grid …

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