# Maximising Transparency in Context Compaction Improves Retention of Facts and Commitments for Lightweight Coding Agents

arXiv: https://arxiv.org/abs/2610.20116 · HTML: https://mlsanity.com/paper/2610.20116/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent memory systems, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Placed in

- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 9 of 9)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management](https://mlsanity.com/paper/2508.21433/) (2025)
- [How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks](https://mlsanity.com/paper/2604.22750/) (2026)
- [Toward Reliable Context Compression for Long-Horizon Agents: An Empirical Study of Execution Instability](https://mlsanity.com/paper/2608.06503/) (2026)
- [Governance Decay: How Context Compaction Silently Erases Safety Constraints in Long-Horizon LLM Agents](https://mlsanity.com/paper/2606.22528/) (2026)
- [Omission Constraints Decay While Commission Constraints Persist in Long-Context LLM Agents](https://mlsanity.com/paper/2604.20911/) (2026)
- [View-oriented Conversation Compiler for Agent Trace Analysis](https://mlsanity.com/paper/2603.29678/) (2026)

## Abstract (arXiv)

Long coding agent sessions outgrow the model's context window, so agents compact: older history is replaced by something shorter, usually a model-written summary. A summary selects and rewords, and the agent cannot see what was lost. Existing work has explored a range of compaction techniques, but comparatively little attention has been paid to their effectiveness with lightweight models, including those deployed locally or used for high-volume workloads. Here, we present transparent-compact, a simple deterministic method for the open-source Pi coding agent, designed specifically for use with such models and with the purpose of maximising the transparency of the agent's view of its own compacted context. It guarantees that user and assistant prose in its context is unchanged by a model or algorithm, and explicitly flags where compaction has stripped information. We compare it with Pi's default model-written summary compaction and with pi-vcc, a popular deterministic method with similar methods but without verbatim retention of prose. Our benchmarks run on four models of varying performance levels, with emphasis on Qwen3.8-27B: a lightweight model at the top end of what consumer hardware can serve locally. On sets probing commitments (rules the user set which exist only in the conversation) and recall of facts, transparent-compact matched or outperformed both alternatives. In particular, it showed an advantage in avoiding confident blunders, i.e. assertion of false information as opposed to recognizing that something was not recalled. Even with a frontier model, its advantage was present but smaller. We argue that transparent-compact spares lightweight models from reasoning or searching their way around what compaction lost, and results in improved recall and application of information as a result, without the need for task or context-specific structuring of the session history.