This paper introduces DP-DiPP, a compression pipeline for differentially private image data using stochastic codes and diffusion models, achieving significant compression rates while retaining compara…

This paper proposes a compression scheme for transmitting sparse local updates in distributed learning systems, and provides a converse based on f-divergence to characterize the communication-accuracy…

This paper presents the 2026 Algorithmic Information Theory Data Compression Challenge, evaluating lossless compressors under realistic constraints and revealing performance dependencies.

This paper systematically analyzes combining dimensionality reduction and quantization to compress text embeddings, showing that this combined approach achieves substantial compression (e.g., 0.1% siz…

This paper proposes a novel Simultaneous Data Compression and Encryption (SDCE) system that combines chaotic map-based encryption with Huffman encoding to securely and efficiently transmit large video…

This paper introduces a structural theorem for the sparsifiability of real-valued codes, which generalizes both combinatorial and continuous notions of sparsification.

The paper introduces HOPE, a mathematical framework for network compression that shifts representation deconstruction from the discrete domain to a Hilbert space, enabling unbiased architectural decis…

This paper proposes FSZ, a GPU error-bounded lossy compressor with three innovations for higher compression ratios and throughput within a single CUDA kernel.

This paper examines the use of 2D barcodes on temporary travel documents to enable machine readability and automate biometric face verification while reducing storage capacity. It compares the perform…

This paper presents a cryptanalytic attack demonstrating that a specific code-based Private Information Retrieval (PIR) scheme can be broken, allowing the server to efficiently determine the requested…

This paper derives bounds on the probability of incorrect clustering of noisy short sequences using statistically optimal rules, focusing on DNA storage decoders.

This paper investigates whether model compression techniques (like quantization and pruning) preserve a Large Language Model's ability to quantify its own uncertainty, finding that accuracy-only evalu…

This paper investigates the redundancy of the prompt KV cache during language model decoding, finding that the structure provided by chat templates is the primary source of redundancy, not the actual…

The paper proposes SubFit, a novel compression technique that achieves superior LLM compression by replacing non-contiguous, submodule-level components (Attention and FeedForward) with lightweight res…

This paper introduces approximation-preserving coresets, which provide weaker guarantees than strong coresets but stronger guarantees than weak coresets for preserving the costs of good solutions in b…

The paper analyzes the structured CVP distance on the log-unit lattice of cyclotomic fields, significantly reducing the conjectured CDPR factor for the ML-KEM cryptosystem from exponential to sub-poly…

The paper introduces Thinking as Compression (TaC), a novel paradigm showing that the inherent reasoning process of a large language model can naturally compress long context inputs, outperforming ded…

The paper proposes SpeechKV, a method to compress speech sequences inside large language models using a learned pooling, maintaining performance and delivering decoding speedup.