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Reasoning-augmented vision language models (VLMs) generate explicit chains of thought that promise greater capability and transparency but also introduce new failure modes: models may reach correct answers via visually unfaithful intermediate steps, or reason faithfully yet fail on the final prediction. Standard evaluations that only measure final-answer accuracy cannot distinguish these behaviors.

Amazon Bedrock Guardrails now supports protection against undesirable content within code elements including user prompts, comments, variables, function names, and string literals. Amazon Bedrock Guardrails provides configurable safeguards for building generative AI applications at scale. These safety controls work seamlessly whether you’re using foundation models from Amazon Bedrock, or applying them at various intervention points in your application using the ApplyGuardrail API.

Quantifying uncertainty in black-box LLMs is vital for reliable responses and scalable oversight. Existing methods, which gauge a model's uncertainty through evaluating self-consistency in responses to the target query, can be misleading: an LLM may confidently provide an incorrect answer to a target query, yet give a confident and accurate answer to that same target query when answering a knowledge-preserving perturbation of the query.