Rewriting Is Forgetting: Zero Rewrite Capture and Budgeted Reading for Long Horizon Agent Memory

Memory is a core component of large language model agents, enabling them to store and retrieve information across sessions. We systematically study the common practice of rewriting conversations before storage in memory systems. We build a controlled framework with three history-length regimes (no truncation, truncation, and long horizon) and evaluate this practice under a unified protocol. We find that the value of memory is truncation-dependent. When the full dialogue history fits in the model context, memory mechanisms provide no additional gain. When history is truncated, memory systems that rewrite degrade substantially, while the no-memory baseline of full-context injection remains almost unaffected. Rewriting before storage also causes a net information loss: the median storage size of rewriting systems is less than one-tenth that of verbatim storage. We term this irreversible loss the rewriting-is-forgetting effect. We therefore propose PW-MLM: the write side stores raw messages verbatim, and the read side controls query cost through temporal resolution and fixed-budget scheduling. Across three QA benchmarks spanning different history lengths, PW-MLM uses only about one-third of the injected context under moderate truncation and matches the quality of full-context injection. In the long-horizon regime, it degrades far less than rewriting-based baselines, a result that delineates the applicability boundary of zero-rewrite, budgeted reading. Our findings offer testable principles for memory design: when the full history can be injected into the context, memory mechanisms are unnecessary; when it cannot, verbatim fidelity outperforms intelligent rewriting.