Long-horizon agents compact their history to continue within a finite context window, but a textual summary alone may not support every subsequent decision.
We introduce REMORY, a neural memory network that supplements the summary with a bounded sequence of soft memory tokens.
Given the history and summary, the network learns to generate tokens that help a frozen LLM approximate the continuation it would produce with the full history.
The tokens are conditioned on the summary and appended after it, forming an analogue of a residual connection along the sequence dimension.
On SummHay, REMORY improves source attribution at nearly unchanged insight coverage and approaches the full-context joint score using only 5.2% of the input positions.
Across long-horizon agent benchmarks, Qwen3.8-27B and GLM-5.3-Flash show consistent gains with residual memory.
Both models also exhibit substantially fewer repeated tool outputs and tool errors on BrowseComp and Terminal-Bench 2.1.