compression

Huawei released a paper on KVarN, a new KV cache compression method achieving minimal loss at 2-bit quantization compared to FP16, outperforming methods like TurboQuant and KIVI with little inference slowdown.

Google engineers recount the decade-long open-source journey behind JPEG XL, highlighting key experiments like WebP Lossless, Butteraugli, and Guetzli that shaped the next-generation image standard.

This paper introduces a framework for time series forecasting that uses importance-aware news compression and process reward model-guided retrieval to incorporate long news articles within fixed context limits, improving prediction accuracy across finance, energy, traffic, and Bitcoin benchmarks.

This paper investigates whether interpretable features identified by sparse autoencoders in full-precision language models remain faithful after quantization, finding systematic degradation that behavioral metrics like perplexity can miss.

VaSE is a training-free method for KV cache eviction that protects large-magnitude value states and introduces stochasticity to improve reasoning model accuracy under compression, outperforming existing methods.

Introduces SEAOTTER, a compression framework for cloud robotics that combines learned latent representations with standard JPEG compatibility, achieving faster encoding/decoding and higher accuracy at extreme compression ratios.

A pure-Rust zstandard decoder implementation has been released, funded by Trifecta Tech, Chainguard, and the NLnet Foundation.

Trifecta Tech Foundation announces the first release of libzstd-rs-sys, a pure Rust implementation of the Zstandard compression format, offering a drop-in replacement for the C reference implementation with improved portability and memory safety at a slight performance cost.

This technical blog post explains why the ASTC texture compression format uses integer sequence encoding (ISE) instead of simpler prefix coding. It compares the efficiency of ISE to prefix codes for small integer ranges, showing that ISE's savings are modest but still worthwhile in certain contexts.

This paper introduces The Efficiency Frontier, a unified framework for cost–performance optimization in LLM context management that models context strategy selection as a deployment-aware optimization problem, achieving 25% reduction in token usage and over 50% lower token cost with amortized memory compression compared to full-context prompting.

The Alliance for Open Media has released the AV2 v1.0.0 specification, a next-generation video coding standard offering superior compression efficiency for streaming, broadcasting, and real-time video conferencing.

NestedKV is a training-free KV cache compression method that uses nested memory routing with multi-time-scale anomaly scoring to improve long-context language model efficiency, achieving significant gains on benchmarks like RULER and LongBench.

This paper identifies a bias in attention weights caused by quantizing keys in KV-cache compression for chunk-wise autoregressive video diffusion, and proposes a per-attention-score correction that removes the bias with negligible overhead, recovering near-BF16 video quality at INT2 quantization.

Shard is a drop-in HuggingFace Cache that achieves 10x KV cache compression for Llama-3.1-8B by using PCA plus int4 quantization on K and Hadamard rotation plus vector quantization on V, without accuracy loss on benchmarks.

The paper introduces Latent Cache Flow (LCF), a method for efficient model-to-model communication by exchanging compressed KV caches instead of text, reducing adapter size and enabling cross-context communication.

DeepSeek's KV cache compression innovations, including MLA and CSA/HCA, reduce KV cache size by 93%, enabling efficient long-context inference and SSD-based caching, as demonstrated by antirez's ds4.c project.

A user shares a tip to use Ollama's local llama3.1:8b model for compressing conversation context in agent workflows, reducing latency and token usage compared to sending context to providers.

Teknium shares recent performance improvements for tool calling in AI agents, including deferring imports, cutting 47% of per-conversation function calls, and deferring compression feasibility checks, with links to working code on GitHub.

This paper describes Gorilla, an in-memory time series database developed at Facebook that achieves high performance through a novel compression algorithm, enabling storage of billions of time series and fast querying for production monitoring.

Comparison of TencentDB's agent memory, which excels at compressing messy run histories for token savings, versus the Memos local plugin, which focuses on turning execution history into reusable habits and long-term learning through feedback loops.