Paper

Parallel Context Compaction for Long-Horizon LLM Agent Serving

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arXiv:2605. 23296v1 Announce Type: new Abstract: Long-horizon LLM agents accumulate growing conversation histories that eventually exceed the model's context window.

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Authors: Musa Cim, Burak Topcu, Chita Das, Mahmut Taylan Kandemir
Released: 2026-05-25 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Parallel Context Compaction for Long-Horizon LLM Agent Serving overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, token, line, control, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.23296

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