Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Field notes

2,343 notes from my AI research vault, mapped by topic. Pick a topic to see its notes, or search.

Every note is in the list below. The web of topics draws here once the page has loaded.

7 of 2,343 notes match.

  • Question answered
  • Source note
  • Topic
  • Shared notes

Notes on multimodal language models

7 notes

  1. The Illusion of Visual Tool-Use: A Causal Audit of Thinking with Images

    Source note · Aug 6, 2026 · multimodal language models, failure modes, causality

    The study examines the 'thinking-with-images' paradigm used in multimodal Language Models (LLMs), which includes visual operations like crop-and-zoom. The research finds that these operations often result in minimal or…

  2. Task-Conditional Faithfulness Auditing of Multimodal LLMs for Grid Diagnosis

    Source note · Jul 27, 2026 · ai, multimodal language models, auditing

    The paper proposes a framework for auditing multimodal large language models used in grid diagnosis tasks to ensure they are using task-appropriate evidence. The framework compares self-reported reliance, behavior…

  3. Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning

    Source note · Jul 23, 2026 · multimodal language models, unlearning

    Researchers propose FAIRGET, a Visual Question Answering benchmark to evaluate the effectiveness of unlearning (removing personal data from trained models) in multimodal large language models under unbalanced…

  4. Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning

    Source note · Jul 23, 2026 · benchmarking, multimodal language models, unlearning

    Researchers propose a new benchmark, FAIRGET, to evaluate the effectiveness of machine unlearning in multimodal large language models under unbalanced, realistic scenarios. They also introduce FAUN, an unlearning…

  5. Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy

    Source note · Jul 16, 2026 · ai evaluation, multimodal language models, scientific visualization literacy

    Researchers evaluated six large language models on their ability to understand scientific visualizations. The study used a standardized test with 49 items based on 18 visualizations and illustrations, covering eight…

  6. Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy

    Source note · Jul 16, 2026 · ai, benchmarking, multimodal language models

    Researchers compared six large language models on a standardized test for understanding scientific visualizations (SciVis). They found that the model Gemini performed best overall, but all models struggled with certain…

  7. Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy

    Source note · Jul 16, 2026 · ai evaluation, multimodal language models, scientific visualization literacy

    Researchers evaluated six multimodal large language models (MLLMs) on a standardized scientific visualization literacy assessment test, consisting of 49 items based on 18 visualizations and illustrations. The study…

An AI research agent drafts these notes from public papers and posts, and each one keeps its source link. Only the folders I choose to publish leave my Obsidian vault.