SING: Synthetic Intention Graph for Scalable Active Tool Discovery in LLM Agents

2026-06-15 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

SING, an intention-aware active tool discovery framework, addresses the challenges of managing expanding tool ecosystems for large language model (LLM) agents. Traditional methods like exhaustive tool schema injection are costly and limit agents to static inventories, while one-shot retrieval often fails to align tool descriptions with evolving task intentions, especially in long-horizon scenarios. SING constructs an intention-tool graph that links user intentions, tool capabilities, and collaboration patterns, enabling dynamic tool retrieval based on changing task states. Evaluated on a corpus of 7,471 tools and three real-world benchmarks, SING significantly improves Global Recall@5 by up to 59.8% and downstream success rate by up to 28.9% over baselines, while reducing full-corpus tool-schema exposure by 99.8%.

Key takeaway

For AI Engineers developing LLM agents with extensive tool access, SING offers a scalable solution to overcome the limitations of static tool inventories and one-shot retrieval. You should consider implementing intention-aware graph structures to improve tool discovery efficiency and agent performance, especially for long-horizon tasks requiring dynamic capability emergence. This approach can drastically reduce context exposure and enhance task success rates.

Key insights

Intention-aware graph structures enable accurate, context-efficient tool discovery for LLM agents in large ecosystems.

Principles

• Tool ecosystems for LLM agents are rapidly expanding.
• Exhaustive tool schema injection is costly and limiting.
• One-shot retrieval often fails for long-horizon tasks.

Method

SING builds an intention-tool graph linking user intentions, tool capabilities, and collaboration patterns to dynamically retrieve tools based on evolving task states.

In practice

• Improve LLM agent tool discovery accuracy.
• Reduce tool-schema exposure in large systems.
• Enhance success rates in complex agent tasks.

Topics

• LLM Agents
• Tool Discovery
• Intention Graphs
• Retrieval-Augmented Generation
• API Integration
• Scalable AI Systems

Best for: Research Scientist, AI Architect, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.