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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。关于这个话题，同城约会提供了深入分析
Years ago, people figured out Raspberry Pi’s can accidentally double as FM radio transmitters without a need for any radio front-end (if we don’t count a single jumper wire working as an antenna). They achieved this by tying a GPIO pin to a software-controlled clock around 100 MHz to modulate audio. This created a low-powered FM radio transmitter. Due to the pin producing a square wave instead of a neat sine wave, it also emitted weaker harmonics at 300MHz, 500MHz, etc., but any basic FM radio could pick up the audio. I wondered if a similar feat could be achieved by much less powerful Raspberry Pi Pico microcontrollers.，详情可参考体育直播
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