Concept · 10 episode(s)

Multimodal Models

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Definition

Multimodal models handle more than one modality — text and images, audio, video, action streams — usually by projecting them into a shared representation space. The frontier question is how cleanly capabilities transfer from one modality to another.

Episodes covering this

  1. 209
    How 2.6 Billion Doodles Exposed the Culture Words Quietly Delete
    Billions of Sketches Reveal Hidden Cultural Variation in Human Concepts
    · ·15 min·Jul 09, 2026
  2. 191
    How One Researcher Beat GPT-5.2 and Gemini 3 by Judging Their Answers, Not Improving Them
    Modality-Driven Search with Holistic Trace Judging for ARC-AGI-2
    Land · Independent Researcher·26 min·Jul 02, 2026
  3. 190
    The Skill Every AI Manager Is Missing: Handing Out Exactly the Right Keys
    ClawArena-Team: Benchmarking Subagent Orchestration and Dynamic Workflows in Language-Model Agents
    Xiong, Ji, Qiu et al. · UNC Chapel Hill·21 min·Jul 02, 2026
  4. 156
    Why More Human Demonstrations Made a Computer-Use Agent Worse
    ProCUA-SFT Technical Report
    Jung, Lu, Cui et al. · NVIDIA / University of Washington·20 min·Jun 18, 2026
  5. 154
    How a 7B Model Out-Investigates a 72B One by Choosing What to Look At
    Native Active Perception as Reasoning for Omni-Modal Understanding
    Xing, Xu, Wang et al. · The Chinese University of Hong Kong·21 min·Jun 18, 2026
  6. 115
    Teaching a Phone Agent to Reason Silently, And Keeping It Honest
    MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models
    Yang, Hu, Hao et al. · Beihang University·24 min·Jun 04, 2026
  7. 111
    How a 4B Web Agent Beat Models 60x Its Size on 500 Demonstrations
    OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents
    Yang, Wu, Chen et al. · UIUC·24 min·Jun 03, 2026
  8. 062
    Treating Hallucinations as Exploits: A Gate-Based Architecture for Agent Safety
    Hallucination as Exploit: Evidence-Carrying Multimodal Agents
    Zhang, Zheng, Yang · Shenzhen University·24 min·May 20, 2026
  9. 047
    When Agent Benchmarks Lie: The Harness Problem in Open-Source AI
    Orchard: An Open-Source Agentic Modeling Framework
    Peng, Yao, Wu et al. · Microsoft Research·28 min·May 15, 2026
  10. 027
    When AI Agents Build the Serving Stack: A Bet on Bespoke Infrastructure
    VibeServe: Can AI Agents Build Bespoke LLM Serving Systems?
    Kamahori, Li, Peter et al. · University of Washington·30 min·May 08, 2026

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