Papers

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Problem

Developing effective skills for AI agents – those specific instructions or knowledge bases that guide them in performing tasks – is currently a difficult and inconsistent process. Existing methods involve manually crafting skills, generating them once (“one-shot”), or allowing skills to evolve through unpredictable self-revision. These approaches lack the rigor of deep learning optimization and often fail to produce consistently improved skills over time.

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Problem

Large Language Models (LLMs) are known to harbor biases, but these biases are tricky to pin down due to the random nature of how they generate text. Traditional methods for checking LLM fairness often just look at a single output or use automated metrics that don’t reveal the full picture—they miss biases lurking in less common generation pathways.

Method

The paper introduces “TreeTracer,” a visual analytics tool designed to tackle this issue. Here’s how it works: