NTILC: Neural Tool Invocation via Learned Compression
📰 ArXiv cs.AI
Learn how NTILC reduces tool invocation latency in agentic language models by compressing tool specifications, improving selection accuracy and context budget utilization
Action Steps
- Implement NTILC in your agentic language model to compress tool specifications
- Train the model using a large registry of callable APIs, functions, and local actions
- Evaluate the model's performance using metrics such as latency, selection accuracy, and context budget utilization
- Compare the results with traditional tool invocation methods to measure the improvement
- Fine-tune the NTILC model to optimize its performance for specific use cases
Who Needs to Know This
ML engineers and researchers working on agentic language models can benefit from NTILC to improve tool invocation efficiency and reduce latency, while also enhancing overall model performance
Key Insight
💡 NTILC overcomes the limitations of traditional tool invocation methods by compressing tool specifications, reducing latency and improving selection accuracy
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🚀 Reduce tool invocation latency in agentic language models with NTILC! 🤖
Full Article
Title: NTILC: Neural Tool Invocation via Learned Compression
Abstract:
arXiv:2606.06566v1 Announce Type: cross Abstract: Agentic tool-calling language models depend on large registries of callable APIs, functions, and local actions. Placing full tool specifications directly in the prompt incurs a cost that scales linearly with the size of the tool registry, rapidly consuming the context budget. As the registry grows, this leads to higher latency and degrades selection accuracy, particularly due to interference from irrelevant tools. We overcome these limitations by
Abstract:
arXiv:2606.06566v1 Announce Type: cross Abstract: Agentic tool-calling language models depend on large registries of callable APIs, functions, and local actions. Placing full tool specifications directly in the prompt incurs a cost that scales linearly with the size of the tool registry, rapidly consuming the context budget. As the registry grows, this leads to higher latency and degrades selection accuracy, particularly due to interference from irrelevant tools. We overcome these limitations by
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