arXiv AI August 6, 2026 By Xuehang Guo, Pengyuan Li, Tom Hope, Tirthankar Ghosal, Manling Li, Qingyun Wang

Consistency-Driven Co-Evolution for Self-Supervised Cross-Representation Learning

arXiv:2608. 04926v1 Announce Type: cross Abstract: As chart images, tabular data, and visualization code play increasingly important roles across diverse domains, cross-representation understanding across these modalities poses fundamental challenges for AI systems: the relationships across representations are inherently \textit{one-to-many}, supervision is ambiguous and costly, and model optimization lacks a principled signal that is both direction-adaptive and representation-generalizable beyond task-specific objectives.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

arXiv Machine Learning

Jun 25

SyncLoop: A Multimodal Dual-Loop Framework for Self-Improving Mathematical Reasoning

arXiv:2507. 16518v3 Announce Type: replace-cross Abstract: Recent advances in multimodal large language models (MLLMs) have shown impressive reasoning capabilities.

By Xiuwei Chen, Wentao Hu, Hanhui Li, Yongxin Wang Jun Zhou, Zisheng Chen, Meng Cao, Yihan Zeng, Kui Zhang, Yu-Jie Yuan, Jianhua Han, Hang Xu, Xiaodan Liang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 2

On the Limits of Token Reduction for Efficient Unified Vision Language Training

arXiv:2606. 01503v1 Announce Type: cross Abstract: Unified vision-language models (VLMs) integrate visual understanding and visual generation within a single autoregressive backbone, but their joint training is computationally expensive and largely overlooked from an efficiency perspective.

By Siyi Chen, Weiming Zhuang, Jingtao Li, Lingjuan Lv

llms multimodal

arXiv AI

Aug 12

VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-Reflection

arXiv:2511. 19436v2 Announce Type: replace-cross Abstract: Existing Video Detailed Captioning (VDC) methods predominantly rely on costly human annotations or distillation from powerful proprietary models, creating a dependency on external supervision.

By Qiang Wang, Xinyuan Gao, Yuhang He, Jizhou Han, Jiangyang Li, SongLin Dong, Zhiheng Ma, Yihong Gong

llms agents reinforcement-learning efficiency multimodal benchmarks

arXiv AI

Jun 3

Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models

arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.

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llms computer-vision multimodal benchmarks safety

arXiv AI

Jun 15

Learning What to Predict: Downstream-Guided Task Design for Continued Pretraining

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By Shuqi Ke, Giulia Fanti

computer-vision

arXiv AI

Jun 6

Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning

arXiv:2606. 05173v1 Announce Type: cross Abstract: Masked language modelling (MLM) has been the dominant pre-training objective for text encoders since BERT, yet it encourages representations that are strongly anchored to surface-form token identity rather than deeper semantic structure.

By Aimen Boukhari

rag diffusion benchmarks

Related stories

SyncLoop: A Multimodal Dual-Loop Framework for Self-Improving Mathematical Reasoning

On the Limits of Token Reduction for Efficient Unified Vision Language Training

VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-Reflection

Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models

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Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning