compression

Hard-KV introduces a Cascade Cache hierarchy and Logits Calibration mechanism to resolve the static-dynamic mismatch in head-adaptive KV cache compression, achieving up to 2x throughput improvement in long-context LLM inference.

This paper presents a quantum autoencoder for compression-driven anomaly detection in brain MRI, achieving high ROC-AUC scores and outperforming classical baselines while providing interpretable anomaly heatmaps.

Whisper large-v3-turbo has been compressed to 368 MB using Q3_K-matched quantization-aware training, with multilingual word error rate results reported.

Explains why evicting 90% of KV cache tokens fails to free GPU memory when serving reasoning models on vLLM, due to paged attention fragmentation, and introduces NVIDIA's TriAttention as a solution that achieves 2.5x speedup and 10.7x memory reduction.

OpenZL is a compression library that generates specialized compressors for specific data formats, achieving high compression ratios at high speeds suitable for datacenter workloads like AI processing.

An exploration of how the game Quake was compressed into just 13 kilobytes, showcasing extreme technical optimization.

This paper proposes InfoKV, an entropy-aware KV cache compression framework that combines token-level predictive uncertainty with attention scores to improve long-context reasoning efficiency. Experiments show it outperforms existing attention-based methods on Llama-3.1, Llama-3.2, and DeepSeek-R1.

ReFreeKV introduces a threshold-free approach to KV cache compression that adaptively allocates budgets, eliminating the need for input-specific thresholds while maintaining full-cache performance across diverse datasets and model sizes.

This paper introduces BabelTele, a compressed writing style that uses abbreviations, symbols, and mixed-language fragments to reduce text length by 72.1% while preserving 99.5% semantic fidelity for LLMs, arguing that human readability and machine recoverability are separable.

This paper presents an adaptive joint compression and synchronization mechanism for federated split learning to reduce communication overhead in IoT rainfall prediction, achieving significant traffic reduction without major loss in predictive quality.

GetCompress is a tool for lossless media compression without context switching.

This paper conducts a same-hands re-evaluation of bivariate causal direction methods on the Tübingen cause-effect pairs, introducing a parameter-free compression baseline that ties with SLOPE. It documents how published accuracy figures are inflated by protocol differences and releases all code and data.

A developer overfits a small 900KB transformer model to compress a 100MB CSV file down to 7MB, demonstrating a novel approach to data compression using overfitted neural networks.

Blog post exploring lossless recompression of GIF images using exhaustive search over LZW encoding, similar to Zopfli for PNG, to achieve smaller file sizes.

The article describes libdeflate's new level 13, a deliberately slow DEFLATE compression level that achieves marginally better compression (0.134% on Silesia) at the cost of being 56x slower than level 12, designed for scenarios where data is compressed once and decompressed many times.

A tweet criticizes token reduction fads while highlighting Headroom, an open-source tool by a Netflix engineer that compresses LLM payloads locally to reduce costs by up to 95%.

An open-source project compresses 60 million text chunks from 201 GB to 6 GB while maintaining retrieval quality, achieving 97% storage reduction and running on a regular laptop without GPU.

This paper reveals a 'benchmark illusion' where pruned LLMs perform well on multiple-choice benchmarks but fail to answer the same questions in open generation, suggesting that compressed models should be tested on generative tasks rather than just recognition tasks.

This paper shows that reducing parameter initialization scale consistently improves pretraining of large language models, with the largest gains on reasoning-demanding tasks. It uncovers a critical initialization that balances reasoning and training, and proposes a simple γ-initialization rule.

This article explores using the gzip compression algorithm as a language model, demonstrating that compression algorithms can generate text by scoring candidate continuations based on compressed length, using beam search to produce output.