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

TL;DR

Laser introduces a structured framework for agentic search that improves stability, interpretability, and scalability in multi-hop reasoning tasks by organizing actions and maintaining a compact context.

Contribution

The paper presents Laser, a novel framework with a symbolic action protocol and context register to enhance agentic search stability and scalability.

Findings

Outperforms existing baselines on multi-hop QA datasets

Enables interpretable and traceable reasoning trajectories

Maintains long-horizon reasoning without context overflow

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.