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Short answer

A plain text summary loses detail and can stall the agent. Two fixes: add learned memory tokens after the summary (REMORY), or compact blocks in parallel for predictable size and less wait. [S1][S2]

Reported effects

REMORY neared full-context scores using 5.2% of input positions and cut repeated tool calls. [S1] Parallel compaction reduced end-to-end wall time. [S2]

Claims

• id: C1 claim: A summary alone may not support every decision. source: S1 evidence: "a textual summary alone may not support every subsequent decision"
• id: C2 claim: REMORY uses 5.2% of input positions. source: S1 evidence: "using only 5.2% of the input positions"
• id: C3 claim: Blocking summarization stalls inference. source: S2 evidence: "the blocking call stalls agent inference for tens of seconds"
• id: C4 claim: Parallel compaction reduces wall time. source: S2 evidence: "it reduces end-to-end wall time"

Sources

• [S1] [2610.11287] REMORY: Learning Residual Memory for Context Compaction. arXiv. https://arxiv.org/abs/2610.11287 . License: CC BY 4.0. Accessed 2026-11-04.
• [S2] [2605.23296] Parallel Context Compaction for Long-Horizon LLM Agent Serving. arXiv. https://arxiv.org/abs/2605.23296 . License: CC BY 4.0. Accessed 2026-11-04.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted).

@misc{iil_note_fjcjmdng,
  author = {{iinventedlight}},
  title = {{Beyond one summary: residual memory and parallel compaction}},
  howpublished = {iinventedlight Library},
  year = {2026},
  date = {2026-11-04},
  url = {https://iinventedlight.com/library/notes/ao-compaction-memory-and-parallel/},
  note = {Permanent ID iil:note:fjcjmdng. License: CC-BY-4.0.},
}

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