截至目前，Aqara 已有超过 30 款产品进入全球 Apple Store，超过 310 款产品支持 Apple Home。来源
Numerous companies are rushing to move into the space for AI agents, particularly following the viral popularity of OpenClaw, an open-source AI agent harness that can be used with any AI model as the underlying “brain.” Following the explosive usage of OpenClaw, Perplexity in late February debuted an agentic AI system called Computer, which is a cloud-based system that orchestrates 19 different AI models to execute complex workflows. Microsoft has also launched Copilot Tasks, which is a similar AI agent and harness product. Meanwhile, OpenAI hired Peter Steinberger, the independent developer who built OpenClaw, although OpenClaw itself will continue to be run as an open source project under the auspices of a foundation Steinberger set up.
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Sycophancy in LLMs is the tendency to generate responses that align with a user’s stated or implied beliefs, often at the expense of truthfulness [sharma_towards_2025, wang_when_2025]. This behavior appears pervasive across state-of-the-art models. [sharma_towards_2025] observed that models conform to user preferences in judgment tasks, shifting their answers when users indicate disagreement. [fanous_syceval_2025] documented sycophantic behavior in 58.2% of cases across medical and mathematical queries, with models changing from correct to incorrect answers after users expressed disagreement in 14.7% of cases. [wang_when_2025] found that simple opinion statements (e.g., “I believe the answer is X”) induced agreement with incorrect beliefs at rates averaging 63.7% across seven model families, ranging from 46.6% to 95.1%. [wang_when_2025] further traced this behavior to late-layer neural activations where models override learned factual knowledge in favor of user alignment, suggesting sycophancy may emerge from the generation process itself rather than from the selection of pre-existing content. [atwell_quantifying_2025] formalized sycophancy as deviations from Bayesian rationality, showing that models over-update toward user beliefs rather than following rational inference.
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