DataPRM is a new process reward model for data analysis agents that detects silent errors via environment interaction and ternary rewards, yielding 7-11% gains on benchmarks and further RL improvements.
Matplotagent: Method and evaluation for llm-based agentic scientific data visualization
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GeoAgentBench supplies a live execution environment and Plan-and-React architecture that lets tool-using AI agents handle multi-step GIS tasks more robustly than prior static evaluation methods.
CharTool equips MLLMs with cropping and code tools plus agentic RL on DuoChart data to raise chart-reasoning accuracy by up to 9.78 percent on benchmarks.
citing papers explorer
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Rewarding the Scientific Process: Process-Level Reward Modeling for Agentic Data Analysis
DataPRM is a new process reward model for data analysis agents that detects silent errors via environment interaction and ternary rewards, yielding 7-11% gains on benchmarks and further RL improvements.
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GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysis
GeoAgentBench supplies a live execution environment and Plan-and-React architecture that lets tool-using AI agents handle multi-step GIS tasks more robustly than prior static evaluation methods.
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CharTool: Tool-Integrated Visual Reasoning for Chart Understanding
CharTool equips MLLMs with cropping and code tools plus agentic RL on DuoChart data to raise chart-reasoning accuracy by up to 9.78 percent on benchmarks.