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H_{2} denotes the order-2 Rényi entropy [4]. Given the high entropy of image distributions, this resampling probability is negligible (<0.01<0.01). By evaluating the remaining tokens using the stale distributions (pj,qj)(p_{j},q_{j}) via standard rejection sampling, AC effectively exploits the asymptotic behavior observed in Fig. 4 to maximize token retention, achieving a retention probability >0.7>0.7: • Acceptance: Set xjt←xjt−1x_{j}^{t}\leftarrow x_{j}^{t-1}, preserving the token. • Rejection: Sample a new token xjt∼max(pj−qj,0)x_{j}^{t}\sim\max(p_{j}-q_{j},0). This process yields a full-length sequence XtX^{t} with minimal deviation from Xt−
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Even the most advanced LLMs stumble when asked to reason over a large, heterogeneous document corpus, achieving only 34% accuracy on the new OfficeQA Pro benchmark despite direct access to the relevant documents.