Unverified50% confidenceFactExact time
In LJMs, the latent state evolves smoothly between events according to closed-form deterministic dynamics.
1
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50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
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Related Claims
UnverifiedIn LJMs, at each event time driven by a Poisson process, the state undergoes a learned instantaneous jump.68% similarUnverifiedThe autoregressive generation mechanism of traditional LLMs determines that they predict token by token in a forward direction, lacking a natural ability to look back.67% similarUnverifiedThe controllability of generative systems is typically mitigated through two types of mechanisms: reinforcement learning alignment (RLHF/RLAIF) and deterministic constraint layers.66% similarUnverifiedLSTM等循环神经网络通过隐状态向量和细胞状态在时间步之间显式传递信息，天然具备序列记忆能力，但无法在序列维度上并行65% similarUnverifiedLatent Jump Models (LJMs) are proposed to address the gap in continuous-time latent state-space models, which cannot directly characterize finite, unobserved shocks.65% similar
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