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HyperTool boosts multi-step tool use accuracy by over 100% in LLMs, transforming how agents interact with complex tool workflows.
FORT-Searcher achieves superior performance by synthesizing training tasks that actively resist shortcut exploitation, transforming how we train deep search agents.
LLM agents struggle significantly with personalized tool use, revealing critical gaps in their capabilities that existing benchmarks overlook.
Forget resource-intensive pipelines: a purely academic team achieves SOTA search agent performance with just 10.6k SFT data points, outperforming models trained with CPT+SFT+RL.
