Parallel Context Compaction for Long-Horizon LLM Agent Serving
The paper discusses a new method called parallel context compaction for managing long-horizon LLM agents. This approach aims to improve the efficiency of conversation history management while providing operators with better control over summary volume. The authors demonstrate that parallel compaction outperforms traditional methods in terms of speed and predictability.
- ▪Long-horizon LLM agents often exceed their context window due to growing conversation histories.
- ▪The proposed parallel compaction method allows for fine-grained control over summary volume and improves throughput.
- ▪The study compares parallel compaction against a sequential baseline across various model architectures.
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Computer Science > Artificial Intelligence arXiv:2605.23296 (cs) [Submitted on 22 May 2026] Title:Parallel Context Compaction for Long-Horizon LLM Agent Serving Authors:Musa Cim, Burak Topcu, Chita Das, Mahmut Taylan Kandemir View a PDF of the paper titled Parallel Context Compaction for Long-Horizon LLM Agent Serving, by Musa Cim and 3 other authors View PDF HTML (experimental) Abstract:Long-horizon LLM agents accumulate growing conversation histories that eventually exceed the model's context window. Context compaction via LLM-based summarization keeps the conversation bounded, but summarization is inherently lossy and the blocking call stalls agent inference for tens of seconds.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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