Cardinality estimation - counting the number of distinct elements in a data stream - requires a tradeoff between memory and accuracy. ExaLogLog recently established the state of the art for this tradeoff by combining wide registers with a Fisher-information-optimal maximum likelihood (ML) estimator, achieving the best known memory-variance product (MVP) among HyperLogLog variants. Here we present Arithmetic Variable LogLog (AVLL), which surpasses ExaLogLog at every memory point tested using arithmetic encoding and eliminating uncommon states to consume 64-bit words completely with 11 registers each, yielding a 5.5x register-count advantage. Its four-component blended estimator, HLDLC, exploits this density advantage to surpass ExaLogLog's ML accuracy without iterative solving. At 1 KB, AVLL achieves 1.63% width-weighted mean absolute error compared to ExaLogLog's 1.71% - a 4.7% improvement. The corresponding empirical MVP is 3.4, surpassing ExaLogLog's practical MVP of 3.78 and its theoretical optimum of 3.67. This holds at every tested size from 0.25 to 4 KB. AVLL inherits DynamicLogLog's early exit mechanism, which filters most elements before any register is touched. With thousands of simultaneous sketches per thread, AVLL is 2.7-4.5x faster than ExaLogLog due to the reduced memory bandwidth from early exits. Like DynamicLogLog, AVLL stores relative NLZ values with a shared offset, so its memory scales as O(B + log log C) rather than O(B x log log C) - decoupling maximum representable cardinality from register width. These results hold under both high-complexity (all-unique) and low-complexity (nonuniformly high duplication rate) data distributions, with zero accuracy degradation from duplication. AVLL is implemented as a single self-contained Java class with all correction formulas embedded, available in the BBTools suite at https://bbmap.org.

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