Reading List

  • Text as an Image, Then a Magnifying Glass

    September 24, 2026

    Text as an Image, Then a Magnifying Glass: LensVLM compresses text by showing it to a vision-language model as an image, then opens the original text where the answer lives.

  • Fine at 15 tools, lost at 300

    May 16, 2026

    ComplexMCP puts agents in stateful MCP sandboxes with hundreds of interdependent tools. Retrieval saturates, checks get skipped, and recoverable errors become surrender.

  • Repeat the prompt when the model is not reasoning

    February 28, 2026

    When reasoning is off, send the query twice. Gemini, GPT, Claude, and Deepseek all improved, with no extra output tokens and no extra latency.

  • A vending machine is a long-horizon eval

    February 20, 2025

    Vending-Bench asks an agent to run a snack machine for a long time. The hard part is not stocking. It is staying coherent after the twentieth day.

Earlier

  • The recipe is the news, not the model

    The recipe is the news, not the model

    January 20, 2025

    DeepSeek-R1 is not a secret model. It is an open recipe: RL on verifiable rewards, then distill. Frontier reasoning behavior just got cheap to copy.

  • Change the numbers and the math falls over

    Change the numbers and the math falls over

    October 7, 2024

    Apple's GSM-Symbolic paper shows that grade-school math scores are fragile. Swap the numbers, or add an irrelevant clause, and apparent reasoning collapses.

  • Automated Scientific Discovery

    Automated Scientific Discovery

    August 27, 2024

    Two recent papers touch on the trend of automated scientific discovery, exploring AI-driven research assistance and fully autonomous scientific experimentation.

  • Mixture of Agents

    Mixture of Agents

    July 14, 2024

    Together AI is the latest to demonstrate that agentic orchestration of cheap LLMs can produce better results than directly using expensive LLMs.

  • TextGrad

    TextGrad

    July 2, 2024

    In the paper "TextGrad: Automatic 'Differentiation' via Text", researchers from Stanford introduce a framework for automatic differentiation via text, for LLMs.