compression

This article discusses and debunks a common myth about compression in Pokemon games.

DeltaX is an Apache-licensed PostgreSQL extension providing compression and columnar storage for time-series data, offering a fast alternative to TimescaleDB or ClickHouse while keeping data in PostgreSQL.

GhostSnap is a tool that lets you take multiple screenshots and automatically compresses them for AI usage.

This paper studies how post-training quantization introduces new biases in instruction-tuned LLMs, finding that 3-bit precision causes 6–21% of previously unbiased items to develop stereotypes, while standard metrics like perplexity fail to detect this degradation.

Investigates injecting Dual E8 lattice bottleneck activations into transformer residual stream, finding a sharp stability threshold at β=0.20 beyond which generation collapses into repetition loops. Generalizes across Qwen2.5 model sizes and shows compression potential.

ZIP Shrinker is a browser tool that reduces ZIP file sizes by recompressing with libdeflate, removing metadata and directory entries, and works with formats like APK and EPUB.

HodgeCover uses higher-order topological coverage to compress sparse Mixture-of-Experts layers by addressing irreducible mergeability barriers that pairwise signals miss, matching state-of-the-art baselines on expert reduction and leading on aggressive compression.

A Rust implementation of the RAR compression format, written mostly by AI language models (OpenAI Codex and Claude), which would have taken years to develop manually but was completed in weeks at low cost.

KVServe is a service-aware framework for adaptive KV cache compression in disaggregated LLM serving, achieving up to 9x job completion time speedup and 32x time-to-first-token reduction.

This paper systematically derives compression-aware neural scaling laws by training nearly 1,300 models, demonstrating that the widely used heuristic of 20 tokens per parameter is an artifact of specific tokenizers. The authors propose a tokenizer-agnostic scaling law based on bytes, offering a new framework for compute-efficient training across diverse languages and modalities.

Context Mode is a tool that solves AI agent context problems by sandboxing tool outputs and persisting sessions, achieving up to 98% compression of Playwright snapshots and using BM25 retrieval to reduce context window usage. It supports 15 platforms including Claude Code, Gemini CLI, VS Code Copilot, and is used by major tech companies.

This paper introduces a fixed-contract diagnostic tool to analyze why KV cache compression methods succeed or fail in long-context LLM inference. It identifies three failure modes—missing evidence, scoring irrelevant tokens, and breaking related evidence—and evaluates them on LongBench and NeedleBench.

This paper introduces Implicit Compression Regularization (ICR), a method to address LLM overthinking during RL post-training by guiding models toward concise yet accurate reasoning trajectories.

This paper introduces LKV, a method for end-to-end learning of head-wise budgets and token selection to optimize KV cache eviction in large language models, achieving state-of-the-art performance with high compression rates.

This paper introduces LiVeAction, a lightweight neural codec designed for real-time operation on resource-constrained devices. It utilizes an FFT-like structure and variance-based rate penalty to achieve superior rate-distortion performance while remaining practical for low-power sensors.

This paper introduces MiA-Signature, a compressed representation of global activation patterns in LLMs designed to improve long-context understanding. It proposes using submodular-based selection to approximate full activation states, yielding performance gains in RAG and agentic systems.

Community discussion on whether Google's TurboQuant compression can already be applied to KV cache in llama-server or if implementation is still pending.

A new paper proposes sequential KV cache compression using probabilistic language tries and predictive delta coding, achieving theoretical compression ratios of ~914,000× beyond TurboQuant by exploiting the sequential structure of language model tokens rather than treating vectors independently.

A reflective essay on how lossy compression creates visible artifacts that influence digital aesthetics, forensics, and art.

This paper presents a systematic benchmark of token pruning—a compression technique that removes tokens and embeddings for irrelevant languages—applied to Korean-centric LLM tasks. The study evaluates popular multilingual models (Qwen3, Gemma-3, Llama-3, Aya) across different vocabulary configurations and finds that token pruning significantly improves generation stability and reduces memory footprint for domain-specific deployments.