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Lehigh University, Salesforce AI Research ,
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Coding agents can now achieve up to 15% higher task completion rates by treating feedback as a persistent note that's followed until resolved, not just a one-off evaluation.
Wrapping standard CLI coding agents in a state-persisting orchestration layer beats unconstrained autonomous agents at research completeness without altering the underlying model.
Despite their visual reasoning prowess, today's MLLMs still struggle to understand handwritten math scratchwork, falling far short of human expert performance in diagnosing student errors.
