TRIAGE, a three-level routing framework that reduces token consumption by reusing historical execution trajectories, and proposes an automatic Skill extraction mechanism that distills high-frequency trajectory patterns into deterministic Skills, creating a positive feedback loop of the more you use it, the more efficient it becomes.
Abstract
Large Language Model (LLM) agents based on the ReAct paradigm have demonstrated remarkable capabilities in tool use and task execution. However, ReAct suffers from a fundamental efficiency problem: every query triggers a complete reasoning loop from scratch, and similar queries repeat identical steps without leveraging historical experience. We propose TRIAGE,a three-level routing framework that reduces token consumption by reusing historical execution trajectories. Its core innovation is TaaS (Trajectory-as-a-Skill), which abstracts historical execution trajectories into reusable skills, realizing'experience as a service'. TRIAGE classifies queries into three levels: (1) Direct Reuse-identical queries, 0 tokens; (2) Skill Substitution-similar queries, 0 tokens via deterministic parameter substitution; (3) Full ReAct-novel queries, automatically stored for future reuse. In large-scale experiments on 1,007 security monitoring queries, TRIAGE achieves 62.3% token savings, with 56.0% of queries at Level 2 and 5.5% at Level 1, both executing at zero cost. Cross-domain validation on ToolBench (15 domains, 345 queries) achieves 76.3% token reduction, confirming the generalizability of semantic routing. An online learning experiment demonstrates cold-start-to-mature evolution: the L2 hit rate rises from 0% to 57% within the first 100 queries, and the average token cost drops from 198 to 74.7. We also propose an automatic Skill extraction mechanism that distills high-frequency trajectory patterns into deterministic Skills, creating a positive feedback loop of'the more you use it, the more efficient it becomes'.
This survey presents a systematic taxonomy and technical review of dynamic orchestration strategies designed to address communication overhead, KV cache management challenges, and increased token consumption within large Language Model-based Multi-Agent Systems.
Heet Nagoriya, H. Raithatha· International Journal of Kno...· 0 citations
AgentWeave is introduced, a deterministic pre-inference routing layer that constructs a bounded model-visible action space using eligibility, requirement, capability, and routing signals and shows lower mean local-model latency.
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DualLane, a novel parallel dual-path planning architecture designed for AI agents, adaptively manages the highly skewed frequency distribution characteristic of real-world user queries and underscores the practical viability and effectiveness of adaptive dual-path planning in large-scale, interactive AIOps systems.
Haoyu Wang, Wenxuan Ma, Bing Hu et al.· Proceedings of the 32nd ACM...· 0 citations
The Adaptive Consistency Graph (ACG) is introduced, which incrementally organizes execution evidence and its provenance in a persistent graph, then constructs a temporary requirement-centered view for each decision under a bounded context budget.
Enterprise agentic systems that route every trajectory step to a frontier model waste 60-80% of their inference budget on subtasks that smaller models handle equally well. Existing routing solutions optimize single-turn query assignment but ignore a property unique to agentic workflows: subtask complexity varies widely...
Rudrendu Kumar Paul, S. Nandy· 1 citation
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