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New AC-JEPA architecture improves visual-to-symbolic translation with imperfect data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an Anchored Cross-Modal JEPA (AC-JEPA) architecture designed to improve visual-to-symbolic translation, particularly in scientific domains where paired data is scarce or imperfect. This new model integrates a joint-embedding objective with causal supervised fine-tuning (SFT) to predict intermediate states across modalities. In tests using optical chemical structure recognition on the WildMol benchmark, AC-JEPA achieved 80.23% accuracy with imperfect data, significantly outperforming direct SFT which saw accuracy drop to 63.47%. AI

IMPACT This architecture could enhance AI's ability to interpret and translate complex scientific data, potentially accelerating research in fields like chemistry.

RANK_REASON The cluster describes a new research paper detailing a novel AI architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AC-JEPA architecture improves visual-to-symbolic translation with imperfect data

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The cluster describes a new research paper detailing a novel AI architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chaofei Hong, Ziyang Chen, Baochen Wang, Christina Yi Jin, Qing Zhang ·

    AC-JEPA: Anchored Cross-Modal Joint Embedding Predictive Architecture for Visual-to-Symbolic Translation

    arXiv:2610.36493v1 Announce Type: new Abstract: Cross-modal translation requires models to capture shared semantics across modalities while preserving the fine-grained structure required by the target modality. Yet in real-world scientific domains, paired data are often imperfect…

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