Agentic services alternate between model inference on clustered GPUs and external tools on distributed CPUs. In this scenario, existing systems (1) overlook tool-return readiness in service evaluation
TAO improves scheduling for LLM agent services by considering tool readiness and using dynamic programming to place sessions, reducing latency and increasing throughput.
Agentic services alternate between model inference on clustered GPUs and external tools on distributed CPUs. In this scenario, existing systems (1) overlook tool-return readiness in service evaluation, wasting limited resources, and (2) use heuristic strategies to decide admission and placement on GPUs, resulting in suboptimal scheduling decisions. To bridge this gap, we present TAO, a tool-aware joint scheduling framework for LLM-based agentic serving. Its value interface combines near-term tool readiness, node-specific cache credit with time-aware decay, and token footprint; a dynamic program jointly selects and places waiting sessions under capacity constraints. Across the 12 reported…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
TAO: Tool-Aware Joint Scheduling for LLM-Based Agentic Serving was first seen on Hugging Face · Daily papers on Oct 29, 2026. Since then it has appeared in 15 checks on 2 different days. Its best position was #9 of 30, reached on Oct 30, 05:28 UTC. It dropped off the list after Oct 30, 15:28 UTC.
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Not at the moment. It was last on the list on Oct 30, 15:28 UTC, after 2 days of trending.
#9 on Hugging Face · Daily papers, on Oct 30, 05:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 29, 2026 and has been on the list on 2 days (15 checks) since.