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No memory substrate is a one-size-fits-all solution; the right choice depends on the task, with broad retrieval boosting QA but hindering decision-making.
Self-evolving rubric rewards can dramatically enhance audio reasoning in models, outperforming traditional methods by adapting to the model's evolving capabilities.
Agentic Commerce World reveals that process-level evidence is crucial for accurately evaluating AI agents in dynamic market environments, challenging traditional reliance on final outcomes alone.
LLM agents can now autonomously generate complex skills with multi-file dependencies, rivaling human-authored skills, thanks to a co-evolutionary verification process that doesn't need ground truth labels.
