EACL 2026: LLMs Can Call Tools -- But Can They Understand Them?
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About this episode
LLM-based agents are everywhere, but most research focuses on just one step: getting the model to call the right tool. What happens after that?
Paper: https://arxiv.org/abs/2510.15955
In this talk, Kiran Kate (IBM Research) presents new findings from their EACL 2026 paper on a largely overlooked problem:👉 Can LLMs actually understand and use the outputs returned by tools?
As tool-augmented systems become more complex, this question becomes critical. The work dives into how current models handle non-trivial, real-world tool responses, and where they break down.
💡 Key ideas covered:
- Why tool calling is only half the story in LLM agents
- The challenge of processing complex tool outputs
- Failure modes in current LLM-based systems
- What this means for building robust, real-world AI agents
This talk is especially relevant if you're working on:
- LLM agents and tool use
- Evaluation of LLM capabilities
- Real-world deployment of AI systems
- Agentic workflows and reasoning pipelines
Kiran Kate:
- https://www.linkedin.com/in/kiran-kate-8b98672/
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