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This arXiv paper is squarely about AI: it proposes JitSkill, a method that lets LLM agents retrieve skills from external libraries dynamically as a long-horizon task unfolds, rather than fixing the skill set up front. The core contribution is tracking evolving task state so retrieval decisions stay coherent across steps. Results are reported on three agent benchmarks, so readers should weigh the gains against those specific evaluations, but the framing addresses a real limitation of current agent designs.
Just-in-Time Skill Retrieval via Long-Horizon State Tracking
arXiv:2611.01701v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external skill libraries to support complex task execution. However, determining which skills are needed remains challenging in long-horizon tasks. Existing methods typically select a fixed skill set before execution, implicitly assuming that a task's procedural requirements can be determined from its initial description. This assumption is restrictive for long-horizon interaction, where new requirements emerge from intermediate observations, execution outcomes, and newly revealed constraints. This motivates just-in-time skill retrieval, which dynamically selects skills as execution unfolds. Just-in-time skill retrieval is promising but challenging: the utility of a skill depends not only on the current context, but also on the evolving task state shaped by prior steps. To this end, we propose JitSkill, a state-grounded framework that addresses this challenge through continuous task-state tracking. By connecting emerging requirements with accumulated task progress, JitSkill provides procedural support throughout execution without treating each retrieval decision independently. Experiments across three agent benchmarks demonstrate its effectiveness in improving long-horizon task completion. Our code is available at https://github.com/XMUDeepLIT/JitSkill.git/.
Abstract:Large language model (LLM) agents increasingly rely on external skill libraries to support complex task execution. However, determining which skills are needed remains challenging in long-horizon tasks. Existing methods typically select a fixed skill set before execution, implicitly assuming that a task's procedural requirements can be determined from its initial description. This assumption is restrictive for long-horizon interaction, where new requirements emerge from intermediate observations, execution outcomes, and newly revealed constraints. This motivates just-in-time skill retrieval, which dynamically selects skills as execution unfolds. Just-in-time skill retrieval is promising but challenging: the utility of a skill depends not only on the current context, but also on the evolving task state shaped by prior steps. To this end, we propose JitSkill, a state-grounded framework that addresses this challenge through continuous task-state tracking. By connecting emerging requirements with accumulated task progress, JitSkill provides procedural support throughout execution without treating each retrieval decision independently. Experiments across three agent benchmarks demonstrate its effectiveness in improving long-horizon task completion. Our code is available at this https URL.
Artificial Intelligence (cs.AI)
arXiv:2611.01701 [cs.AI]
(or arXiv:2611.01701v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.01701 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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