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EdgeGen is a synthetic task generation framework that extracts compliance rules from an agent's specification and uses them to generate database-grounded edge-case tasks designed to violate these rules, and when combined with existing synthetic data generation techniques, enables agent improvement through finetuning and harness optimization.
Visual attention in VLMs is not uniformly reliable; it varies across three distinct modes, revealing a surprising gap between model predictions and human intuition.
Model rankings on standard benchmarks can flip entirely when you optimize prompts for each LLM, so your "best" model might actually be the worst.
Stop evaluating agents in a vacuum: TED reveals how user expertise impacts agent performance and pinpoints actionable error remedies, boosting performance by 8-10%.
Forget OCR? Powerful MLLMs can extract information from business documents just as well from images alone, challenging the necessity of traditional OCR pipelines.
