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Building one's own evaluations can turn model selection into a data-driven decision, using one's own evaluation set to run real-task performance data to determine whether to switch models.
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10/10/2026
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UnverifiedSelf-modeling involves having the model build an internal representation of its own behavioral patterns, rather than merely predicting external tasks.80% similarUnverifiedSelf-reflection is achieved by having the model evaluate the quality of its own output and iteratively improve it; the Reflexion framework demonstrates that an agent can learn from failed attempts and adjust its strategy in the next iteration.79% similarUnverifiedThere are two paths to getting the model to output an accurate solution: writing detailed requirements all at once, or using follow-up questioning to have the model think on the user's behalf and accumulate context.77% similarUnverifiedSub-agents independently select models based on their own assessment of subtask complexity rather than inheriting the model setting of the main session, which causes multiple models that the user never manually selected to appear in usage reports.76% similarUnverifiedReflexion-type agents have the model generate a self-evaluation report after each task completion and write the experience into memory for future reference.76% similar
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