GUI-R1 uses reinforcement fine-tuning with GRPO on a small curated dataset to create a generalist vision-language action model that outperforms prior GUI agent methods across mobile, desktop, and web benchmarks using only 0.02% of the data.
Cognitive archi- tectures for language agents.Transactions on Machine Learning Research
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UNVERDICTED 2representative citing papers
Temporal semantic caching and MCP workflow optimizations deliver 30.6x median speedup on cache hits and 1.67x overall speedup with 40% latency reduction on the AssetOpsBench industrial agent benchmark.
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GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agents
GUI-R1 uses reinforcement fine-tuning with GRPO on a small curated dataset to create a generalist vision-language action model that outperforms prior GUI agent methods across mobile, desktop, and web benchmarks using only 0.02% of the data.
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Evaluating Temporal Semantic Caching and Workflow Optimization in Agentic Plan-Execute Pipelines
Temporal semantic caching and MCP workflow optimizations deliver 30.6x median speedup on cache hits and 1.67x overall speedup with 40% latency reduction on the AssetOpsBench industrial agent benchmark.