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The year 1948 gave birth to two transformative pillars of modern computing: Claude Shannon's information theory, which gave rise to data compression, and Alan Turing's writings on intelligent machinery, which ignited the field of artificial intelligence. Today, nearly eight decades later, we are witnessing a profound h...
It is demonstrated that depth-first layouts of $k^2$-trees provide a practical and efficient alternative to traditional layouts, improving both compression and computational performance in matrix operations.
A pipeline for building an open-taxonomy semantic annotation of source code using a code-specialised Large Language Model, and a calibrated quality-assurance protocol that quantifies annotation precision by combining a small human gold set with an LLM-as-a-judge filter are presented.
A practical design that combines Ring ORAM to hide access patterns to outsourced data, with a Patricia trie for space-efficient search over variable-length strings to support search over variable-length string datasets in an ORAM scenario, while retaining efficient storage and access both on the client and the server i...
The first oblivious implementations of Rank and Select that require only a single ORAM access per operation, use O(1) client-side space, and add negligible client computation overhead are presented.
It is argued that replacing autoregressive LLMs with DLMs within the same compression framework could overcome the throughput bottleneck caused by their one-symbol-per-step limitation, and the newly proposed DLM-based framework advances the state of the art in lossless text compression.
This work introduces LLM-based compressors deploying two novel symbol-ranking variants that bound predictions to the top-$T$ ranks, and conducts the first large-scale evaluation of LLM-based source code compression across 30 LLMs, including general-domain, code-specialized, and quantized models.
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