but our algorithm is not for “the (1)” match, it’s designed to mark-and-sweep through all matches in the input, and looking back, the paper does not highlight the importance of this as much as it should. without pointing this out, we would have the slowest first match algorithm in the world.
Магомедова добавила, что определить, выспался ли человек, можно по нескольким простым критериям. Она заверила: если он включается в режим бодрствования через 20-30 минут после пробуждения и без проблем функционирует в течение дня, то сна было достаточно.
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她和豆包的交流也不止是这个春节。每天外婆都会和豆包闲聊几句，给豆包打语音，打视频。
We know where it broke, but we can’t see why. Was it a race condition? Did a database read return stale data that has since been overwritten? To find the cause, we have to mentally reconstruct the state of the world as it existed milliseconds before the crash. Welcome to debugging hell.
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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.