Unverified50% confidenceFactExact time
Latent Jump Models (LJMs) are proposed to address the gap in continuous-time latent state-space models, which cannot directly characterize finite, unobserved shocks.
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50%
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Long-term
Relevance
10/12/2026
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UnverifiedIn LJMs, at each event time driven by a Poisson process, the state undergoes a learned instantaneous jump.67% similarUnverifiedIn LJMs, the latent state evolves smoothly between events according to closed-form deterministic dynamics.65% similarVerifiedWhen a model's parameter count surpasses a certain critical threshold, capabilities such as reasoning and debugging that are not seen in smaller models emerge, a phenomenon known as Emergent Abilities.63% similarUnverifiedThe Linear Representation Hypothesis (LRH) assumes that models encode high-level concepts as linear directions in the activation space.63% similarUnverifiedOpen challenges faced by LLMs in embodied scenarios include accurately perceiving the current physical state, avoiding hallucinations that lead to erroneous action planning, and reasoning efficiently under real-time constraints.63% similar
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