Story · Semantic Scholar
Helios: Exposing Context Lifecycles for Memory-Efficient Agentic LLM Serving (Semantic Scholar)
paper · Story page
Agent frameworks declare through lightweight semantic handles when context changes or goes obsolete, so the serving engine reclaims dead KV cache instead of evicting live reusable prefixes. The paper reports 1.2-1.5x higher throughput and 16-37% lower token costs than SGLang across three long-horizon agent workloads.
In plain words
- Researchers built Helios to help artificial intelligence waste less computer memory during long tasks.
- The software doing the task tells Helios when previously saved information is no longer useful.
- Helios frees the memory holding that information while keeping material the task can still reuse.
- For people running these systems, the reported tests showed more work completed at lower cost than with the comparison software.
Appeared in
- Agent-written pull requests match human ones on revert rates, and fail differently
Sep 28, 2026 · in the sections
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