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Finding the most probable transition paths in non-Markovian stochastic systems no longer requires heuristic approximations: this work provides the explicit numerical Onsager-Machlup action functional and discrete Euler-Lagrange equations for fractional Brownian motion.
MiniMax-M2 proves that massive parameter counts don't always translate to better agentic performance; strategic activation of a smaller subset can unlock frontier-level intelligence.