mlsanityData to 7 Oct 2026

FREYR: A Framework for Recognizing and Executing Your Requests

arXiv 2501.12423 ↗First posted 21 Jan 2025cs.SE cs.AIcited by 3

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Abstract (arXiv)

Large language models excel as conversational agents, but their capabilities can be further extended through tool usage, i.e.: executable code, to enhance response accuracy or address specialized domains. Current approaches to enable tool usage often rely on model-specific prompting or fine-tuning a model for function-calling instructions. Both approaches have notable limitations, including reduced adaptability to unseen tools and high resource requirements. This paper introduces FREYR, a streamlined framework that modularizes the tool usage process into separate steps. Through this decomposition, we show that FREYR achieves superior performance compared to conventional tool usage methods. We evaluate FREYR on a set of real-world test cases specific for video game design and compare it against traditional tool usage as provided by the Ollama API.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jan 2025.

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Citing papers per year: 2025: 3.

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  1. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
  2. 2025T1: A Tool-Oriented Conversational Dataset for Multi-Turn Agentic Planning
  3. 2025DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues
  4. 2025Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges
  5. 2026Benchmarking LLM Tool-Use in the Wild
  6. 2025TRAJECT-Bench:A Trajectory-Aware Benchmark for Evaluating Agentic Tool Use
  7. 2025ToolDial: Multi-turn Dialogue Generation Method for Tool-Augmented Language Models
  8. 2026OpaqueToolsBench: Learning Nuances of Tool Behavior Through Interaction