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Beyond Language Modeling: An Exploration of Multimodal Pretraining research paper by Meta, 2026

Meta · Mar 3, 2026 · Multimodal and robotics · 32 citations · 108 upvotes · unverified

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What it shows

Controlled multimodal pretraining experiments reveal key insights about unified visual representations, data complementarity, world modeling emergence, and efficient scaling through mixture-of-experts architectures.

UnverifiedHugging Face's summary; not yet checked by hand.

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PaperCitations
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Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and RecipesControlled experiments on when text, image understanding and image generation help or compete when trained together.Multimodal and robotics · Aug 20262
HumanCLAW: Can Vision-Language Models Act Through a Body?HumanCLAW decouples high-level vision-language decisions from low-level motor execution to evaluate embodied action intelligence, revealing that current vision-language models lack embodied self-awareness.Multimodal and robotics · Jul 2026 · Unverified2
TUA-Bench: A Benchmark for General-Purpose Terminal-Use AgentsTUA-Bench presents a comprehensive benchmark for evaluating general-purpose terminal-use agents across diverse digital activities and specialized workflows, revealing significant performance gaps among current frontier agents.Agents and evaluation · Jun 2026 · Unverified2
Think in Strokes, Not Pixels: Process-Driven Image Generation via Interleaved ReasoningProcess-driven image generation decomposes synthesis into iterative steps involving textual planning, visual drafting, textual reflection, and visual refinement, with step-wise supervision ensuring consistency and interpretability.Reasoning · Apr 2026 · Unverified5
V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised LearningV-JEPA 2.1 is a self-supervised model that learns dense visual representations for images and videos through a combination of dense predictive loss, deep self-supervision, multi-modal tokenizers, and effective scaling.Multimodal and robotics · Mar 2026 · Unverified83
ReMix: Reinforcement routing for mixtures of LoRAs in LLM finetuningResearchers address imbalance in routing weights of Mixture-of-LoRAs models by proposing Reinforcement Routing (ReMix), which uses non-learnable weights and reinforcement learning techniques to improve model expressiveness and performance.Inference and efficiency · Mar 2026 · Unverified3
AIRS-Bench: a Suite of Tasks for Frontier AI Research Science AgentsAIRS-Bench presents a comprehensive benchmark suite for evaluating LLM agents across diverse scientific domains, demonstrating current limitations while providing open-source resources for advancement.Applied AI · Feb 2026 · Unverified25

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DeepSeek-VL: Towards Real-World Vision-Language UnderstandingDeepSeek-VL is an open-source vision-language model that achieves state-of-the-art performance in real-world applications by combining a hybrid vision encoder with effective pretraining strategies to preserve language model capabilities.DeepSeek · Mar 2024 · Unverified889
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Video models are zero-shot learners and reasonersVeo 3, a generative video model, exhibits zero-shot capabilities across various visual tasks, suggesting a trajectory towards becoming a unified, generalist vision foundation model.Google DeepMind · Sep 2025 · Unverified215
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About this paper
Authors
Shengbang Tong, David Fan, John Nguyen and 18 more
arXiv
2603.03276 · PDF
Venue
arXiv.org
Citations
32, 4 influential · Semantic Scholar
Upvotes
108 · Hugging Face
Lab
Meta · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Influential citationsfirst count: 4Sep 25, 2026
Citationsfirst count: 32Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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