compaction

The user describes struggles with context, compaction, and memory management for local AI models using pi.dev plugins and seeks suggestions for solutions that handle varying model context windows and VRAM limitations.

This article discusses a new context management strategy in GPT Astra that avoids compression and instead writes objectives, decisions, and progress to server-side notes, thereby eliminating the need to generate conversation summaries when switching contexts.

This paper argues that managing context in AI agents should be treated as a lifecycle architecture problem, proposing Agentic Context Management (ACM) with five primitives and a reference implementation that achieves high benchmark scores.

This article explains how compaction works in the Pi coding agent, summarizing old conversation history when the context window nears its limit, and details Pi's specific implementation and triggers.

The article explains 'context rot,' where AI agents degrade on long tasks as context grows, even before the window is full, and offers techniques like compaction, offloading state, and retrieval on demand to maintain reliability.

The author analyzes Codex's Remote Compaction v2 data flow, including trigger conditions, request handling, server-side ciphertext return, and the complete client-side context reconstruction process, noting that both the summarization model and encryption keys reside on the OpenAI side.

OpenAI reveals that enabling retained reasoning and context compaction tripled GPT-5.6 Sol's ARC-AGI-3 benchmark scores, highlighting how harness settings significantly impact measured model performance.

OpenAI discovered that enabling retained reasoning and compaction settings in the API harness tripled GPT-5.6 Sol's scores on the ARC-AGI-3 benchmark while cutting output tokens by 6x, revealing that benchmark performance is heavily influenced by harness design.

ARC improves retrieval efficiency and accuracy in long-context AI agents by separating append-only storage from bounded citation views, achieving near-perfect recall with lower latency and bandwidth.

This paper identifies a failure mode in agentic LLM tools like Claude Code, where session compaction summaries misinterpret partial terminal output from timed-out commands as confirmed results, propagating false positives across sessions and model versions without re-verification.

GreptimeDB's Mito2 storage engine uses an LSM-tree design with columnar Parquet SSTs, three-level scan pruning, and TWCS compaction. The blog post provides a full walkthrough of its architecture.

SelfCompact is a scaffolding approach that lets language models autonomously decide when and how to compact long agent traces, achieving better performance with reduced token costs compared to fixed-interval methods.

Snapcompact is a technique that renders text into dense pixel-font images to replace text tokens with cheaper image tokens, achieving near-verbatim recall at a fraction of the input cost.

Lantern introduces a lightweight memory layer that archives conversation turns and retrieves relevant details after compaction, recovering 78.3% of lost facts with zero LLM calls and outperforming MemGPT-based methods.

A user shares a custom Plugin SDK hook that gradually compresses older turns while keeping recent ones raw to prevent context window exhaustion in long OpenClaw sessions, reducing re-sent context by 80%.