Laser: Governing Long-Horizon Agentic Search via Structured Protocol and Context Register

• URL: http://arxiv.org/abs/2512.20458v2
• Date: Fri, 26 Dec 2025 17:05:15 GMT
• Title: Laser: Governing Long-Horizon Agentic Search via Structured Protocol and Context Register
• Authors: Shuting Wang, Qiaolin Xia, Vich Wang, Herberttli, Bobsimons, Zhicheng Dou,
• Abstract summary: We introduce Laser, a framework for stabilizing and scaling agentic search.<n>Laser organizes agent behaviors into three spaces: planning, task-solving, and retrospection.<n>Laser consistently outperforms existing agentic search baselines under both prompting-only and fine-tuning settings.
• Score: 38.329346729947304
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in Large Language Models (LLMs) and Large Reasoning Models (LRMs) have enabled agentic search systems that interleave multi-step reasoning with external tool use. However, existing frameworks largely rely on unstructured natural-language reasoning and accumulate raw intermediate traces in the context, which often leads to unstable reasoning trajectories, context overflow, and degraded performance on complex multi-hop queries. In this study, we introduce Laser, a general framework for stabilizing and scaling agentic search. Laser defines a symbolic action protocol that organizes agent behaviors into three spaces: planning, task-solving, and retrospection. Each action is specified with explicit semantics and a deterministic execution format, enabling structured and logical reasoning processes and reliable action parsing. This design makes intermediate decisions interpretable and traceable, enhancing explicit retrospection and fine-grained control over reasoning trajectories. In coordination with parsable actions, Laser further maintains a compact context register that stores only essential states of the reasoning process, allowing the agent to reason over long horizons without uncontrolled context expansion. Experiments on Qwen2.5/3-series models across challenging multi-hop QA datasets show that Laser consistently outperforms existing agentic search baselines under both prompting-only and fine-tuning settings, demonstrating that Laser provides a principled and effective foundation for robust, scalable agentic search.

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