ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use (2025 · 15 citations)
Benchmarks for LLM tool use rarely test true multi-hop behaviour. Most build datasets tool-first: they gather tools and then synthesize queries for them. Such tools often do not depend on one another, and answers cannot be checked reliably, so evaluation falls back on model-based judging, which carries bias. ToolHop…
Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments (2026 · 6 citations)
Training LLMs to call tools with reinforcement learning is difficult for two reasons. Online APIs make training environments unstable and costly to maintain, and judging trajectories usually requires a strong LLM as evaluator, which brings bias. The authors propose FTRL, which removes both problems. An automated…
Recitation over Reasoning: How Cutting-Edge Language Models Can Fail on Elementary School-Level Reasoning Problems? (2025 · 5 citations)
The paper asks whether frontier LLMs actually reason through simple problems or mostly reproduce memorized solution templates.

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Updated 30 October 2026