# LCM: Lossless Context Management

arXiv: https://arxiv.org/abs/2605.04050 · HTML: https://mlsanity.com/paper/2605.04050/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 14 Feb 2026.

## Builds on (most cited first, 2 of 2)

- [Recursive Language Models](https://mlsanity.com/paper/2512.24601/) (2025)
- [Oolong: Evaluating Long Context Reasoning and Aggregation Capabilities](https://mlsanity.com/paper/2511.02817/) (2025)

## Built on by (most cited first, 4 of 4)

- [An Empirical Study of Harness Design for Coding Agents](https://mlsanity.com/paper/2609.20804/) (2026)
- [Context as an Environment: Programmatic Context Management for Long-Horizon Agents](https://mlsanity.com/paper/2608.21690/) (2026)
- [CADOC: Cache-Aware Dynamic Object Context for Long-Horizon Agents](https://mlsanity.com/paper/2609.37012/) (2026)
- [ReFold: Training-Free Reversible Inter-Turn Context Folding for Long-Horizon Agents](https://mlsanity.com/paper/2610.07863/) (2026)

## Abstract (arXiv)

We introduce Lossless Context Management (LCM), a deterministic architecture for LLM memory that outperforms Claude Code on long-context tasks. When benchmarked using Opus 4.6, our LCM-augmented coding agent, Volt, achieves higher scores than Claude Code on the OOLONG long-context eval, including at every context length between 32K and 1M tokens. LCM may be considered both a vindication and extension of the recursive paradigm pioneered by Recursive Language Models (RLMs). Our results demonstrate that recursive context manipulation can outperform not just conventional LLMs, but frontier coding agents with native file-system access. LCM departs from RLM by decomposing symbolic recursion into two deterministic, engine-managed mechanisms: recursive context compression, in which a hierarchical summary DAG automatically compacts older messages while retaining lossless pointers to every original; and recursive task partitioning, in which engine-managed parallel primitives like LLM-Map replace model-written loops. This trade-off, analogous to the move from GOTO to structured control flow in program-ming language design, sacrifices maximal flexibility for termination guarantees, zero-cost continuity on short tasks, and lossless retrievability of all prior state.