Anthropic has released Bloom, an open source agentic framework for generating behavioral evaluations of frontier AI models. Bloom takes a researcher-specified behavior and quantifies its frequency and severity across automatically generated scenarios. This allows testing of AI-based systems by evaluating their behavior.
Bloom’s evaluations correlate strongly with hand-labeled judgments. Anthropic finds they reliably separate baseline models from intentionally misaligned ones. As examples of this, Anthropic released benchmark results for four alignment relevant behaviors on 16 models.
Bloom is a scaffolded evaluation system which accepts as input an evaluation configuration (the “seed”), which specifies a target behavior (for example sycophancy, political bias or self-presevation), exemplary transcripts, and the types of interactions the user is interested in, and generates an evaluation suite of interactions with the target model that attempt to uncover the chosen behavior. As reflected in the name of the tool, the evaluation suite will grow differently depending on how it is seeded, which differs from other evaluations that may use a fixed elicitation technique and prompting pattern. Thus, for reproducibility, Bloom evaluations should always be cited together with their full seed configuration.
Link to Anthropic release post: https://www.anthropic.com/research/bloom
Link to Bloom GitHub repository: https://github.com/safety-research/bloom
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