Closing the Spatial Execution Gap in Digital Whiteboards via Verifiable Reinforcement Learning (2026.acl-long)
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| Challenge: | Large language models suffer from a fundamental Spatial Execution Gap, failing to translate visual semantics into precise, schema-valid coordinate operations in interactive environments. |
| Approach: | They propose a pipeline that leverages Group Relative Policy Optimization to enforce a strict Identify-Reason-Verify protocol and train on execution-verifiable rewards. |
| Outcome: | The proposed pipeline outperforms a state-of-the-art frontier model by 16.75% in operation accuracy. |
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